THE EFFECT OF APPLIANCE ENERGY EFFICIENCY …



The effect of appliance ENERGY efficiency improvements on domestic electric loads in European householdsS P Borg, N J KellyEnergy Systems Research Unit, Mechanical Engineering, University of Strathclyde, Glasgow, UKABSTRACT Across Europe domestic electricity consumption is on the rise. In an attempt to counter this increase, various initiatives have been introduced to promote the replacement of less energy-efficient appliances with more efficient ones. Whilst the likely aggregate effect of such measures over long time periods has been modelled extensively, little is known about the affect that a change to higher efficiency appliances will have on the electrical demand profile of individual households at higher temporal resolutions. To address this issue a means by which established approaches to detailed electrical demand modelling can be adapted to simulate the improvements in the efficiency of appliances is elaborated in this paper. A process is developed by which low-resolution empirical appliance demand data can be transformed to produce high-resolution electrical demand data for different periods in the year, factoring in improvements in appliance performance. The process is applied to simulate the effects a changeover to more energy-efficient appliances would have on the minute resolution demand profiles of a group of households. Results indicate that improving the energy-efficiency of appliances in households leads to a significant reduction in electrical energy requirements but does not appear to have a significant affect on the peak electrical demand. Keywords: energy-efficiency; domestic appliances; electrical energy consumption; demand profile, daily peak demand; minute resolution 1.INTRODUCTIONElectricity consumption across Europe has increased significantly over the past years such that it now accounts for around 21% of final energy demand ADDIN EN.CITE <EndNote><Cite><Author>DG_for_Energy_and_Transport</Author><Year>2009</Year><RecNum>202</RecNum><record><rec-number>202</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">202</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DG_for_Energy_and_Transport</author></authors><tertiary-authors><author>European Commission</author></tertiary-authors></contributors><titles><title>EU energy and transport in figures</title></titles><dates><year>2009</year></dates><pub-location>Brussels</pub-location><urls><related-urls><url>;[1]. The domestic sector alone accounts for almost 25% of final energy consumption and almost 30% of electrical energy demand ADDIN EN.CITE <EndNote><Cite><Author>DG_for_Energy_and_Transport</Author><Year>2009</Year><RecNum>202</RecNum><record><rec-number>202</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">202</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DG_for_Energy_and_Transport</author></authors><tertiary-authors><author>European Commission</author></tertiary-authors></contributors><titles><title>EU energy and transport in figures</title></titles><dates><year>2009</year></dates><pub-location>Brussels</pub-location><urls><related-urls><url>;[1]. The key drivers for increasing electrical demand in housing have been a rapid increase in the number of electrical appliances ADDIN EN.CITE <EndNote><Cite><Author>Almeida</Author><Year>2008</Year><RecNum>80</RecNum><record><rec-number>80</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">80</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Aníbal de Almeida</author><author>Fonseca, Paula </author><author>Bandeirinha, Rui </author><author>Fernandes, Tiago </author><author>Araújo, Rui </author><author>Nunes, Urbano </author></authors></contributors><auth-address>ISR-University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</auth-address><titles><title>Final Report REMODECE - Residential monitoring to Decrease Energy use and Carbon Emissions in Europe (IEEA Program Funded Project)</title><secondary-title>REMODECE - Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe (IEEA Program Funded Project)</secondary-title><short-title>REMODECE</short-title></titles><keywords><keyword>Residential energy</keyword><keyword>residential electricity</keyword><keyword>energy monitoring</keyword><keyword>standby consumption</keyword><keyword>home appliances</keyword><keyword>residential lighting</keyword><keyword>electronic loads</keyword><keyword>air conditioning</keyword><keyword>savings potential</keyword><keyword>energy efficiency policies</keyword><keyword>market transformation</keyword><keyword>climate change</keyword></keywords><dates><year>2008</year></dates><pub-location>Coimbra</pub-location><publisher>University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</publisher><work-type>IEEA Project Funded Program</work-type><urls><related-urls><url>;[2]; a significant increase in the use of certain appliances such as PCs and televisions ADDIN EN.CITE <EndNote><Cite><Author>Bertoldi</Author><Year>2007</Year><RecNum>131</RecNum><record><rec-number>131</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">131</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Paolo Bertoldi</author><author>Atanasiu, Bogdan </author></authors><tertiary-authors><author>JRC European Commission</author></tertiary-authors></contributors><titles><title>Electricity Consumption and Efficiency Trends in the Enlarged European Union - Status Report 2006</title></titles><pages>1-53</pages><number>EUR 22753EN</number><dates><year>2007</year></dates><publisher>Institute for Environment and Sustainability</publisher><urls></urls><language>English</language></record></Cite><Cite><Author>Atanasiu</Author><Year>2008</Year><RecNum>214</RecNum><record><rec-number>214</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">214</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Atanasiu, Bogdan</author><author>Bertoldi, Paolo</author></authors></contributors><titles><title>Residential electricity consumption in New Member States and Candidate Countries</title><secondary-title>Energy and Buildings</secondary-title></titles><periodical><full-title>Energy and Buildings</full-title></periodical><pages>112-125</pages><volume>40</volume><number>2</number><keywords><keyword>Residential</keyword><keyword>Electricity</keyword><keyword>Lighting</keyword><keyword>Appliances</keyword><keyword>Electronics</keyword><keyword>European Union</keyword><keyword>New Member States</keyword></keywords><dates><year>2008</year></dates><isbn>0378-7788</isbn><urls><related-urls><url>;[3, 4]; an increase in the installation and use of domestic air conditioning ADDIN EN.CITE <EndNote><Cite><Author>Almeida</Author><Year>2008</Year><RecNum>80</RecNum><record><rec-number>80</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">80</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Aníbal de Almeida</author><author>Fonseca, Paula </author><author>Bandeirinha, Rui </author><author>Fernandes, Tiago </author><author>Araújo, Rui </author><author>Nunes, Urbano </author></authors></contributors><auth-address>ISR-University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</auth-address><titles><title>Final Report REMODECE - Residential monitoring to Decrease Energy use and Carbon Emissions in Europe (IEEA Program Funded Project)</title><secondary-title>REMODECE - Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe (IEEA Program Funded Project)</secondary-title><short-title>REMODECE</short-title></titles><keywords><keyword>Residential energy</keyword><keyword>residential electricity</keyword><keyword>energy monitoring</keyword><keyword>standby consumption</keyword><keyword>home appliances</keyword><keyword>residential lighting</keyword><keyword>electronic loads</keyword><keyword>air conditioning</keyword><keyword>savings potential</keyword><keyword>energy efficiency policies</keyword><keyword>market transformation</keyword><keyword>climate change</keyword></keywords><dates><year>2008</year></dates><pub-location>Coimbra</pub-location><publisher>University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</publisher><work-type>IEEA Project Funded Program</work-type><urls><related-urls><url>;[2]; and social/demographic changes that mean that there are a larger number of smaller households ADDIN EN.CITE <EndNote><Cite><Author>Bertoldi</Author><Year>2007</Year><RecNum>131</RecNum><record><rec-number>131</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">131</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Paolo Bertoldi</author><author>Atanasiu, Bogdan </author></authors><tertiary-authors><author>JRC European Commission</author></tertiary-authors></contributors><titles><title>Electricity Consumption and Efficiency Trends in the Enlarged European Union - Status Report 2006</title></titles><pages>1-53</pages><number>EUR 22753EN</number><dates><year>2007</year></dates><publisher>Institute for Environment and Sustainability</publisher><urls></urls><language>English</language></record></Cite></EndNote>[3]. This has been particularly evident in southern European households. Over the period 2002 to 2008, electricity consumption in EU countries bordering the Mediterranean has increased by an average 3.7% per year ADDIN EN.CITE <EndNote><Cite><Author>Eurostat</Author><RecNum>189</RecNum><record><rec-number>189</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">189</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>Eurostat</author></authors></contributors><auth-address>Joseph Bech building, 5 Rue Alphonse Weicker, L-2721 Luxembourg</auth-address><titles><title>Eurostat Web portal</title><secondary-title>Main Database - Environment and Energy</secondary-title></titles><number>31st August 2010</number><dates></dates><pub-location>Luxembourg</pub-location><publisher>Eurostat</publisher><urls><related-urls><url> Database</remote-database-provider><language>English</language></record></Cite></EndNote>[5]; compared to a 2% increase registered by the remaining EU Countries ADDIN EN.CITE <EndNote><Cite><Author>Eurostat</Author><RecNum>189</RecNum><record><rec-number>189</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">189</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>Eurostat</author></authors></contributors><auth-address>Joseph Bech building, 5 Rue Alphonse Weicker, L-2721 Luxembourg</auth-address><titles><title>Eurostat Web portal</title><secondary-title>Main Database - Environment and Energy</secondary-title></titles><number>31st August 2010</number><dates></dates><pub-location>Luxembourg</pub-location><publisher>Eurostat</publisher><urls><related-urls><url> Database</remote-database-provider><language>English</language></record></Cite></EndNote>[5].Increasing electrical energy demand is of concern to governments across the EU in that it undermines their efforts to reduce greenhouse gas emissions as set out under the terms of the Kyoto protocol ADDIN EN.CITE <EndNote><Cite><Author>The_World_Bank</Author><Year>2010</Year><RecNum>200</RecNum><record><rec-number>200</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">200</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>The_World_Bank</author></authors><tertiary-authors><author>The International Bank for Reconstruction and Development / The World Bank</author></tertiary-authors></contributors><titles><title>World Development Report 2010</title><secondary-title>Development and Climatte Change</secondary-title></titles><dates><year>2010</year></dates><pub-location>Washington</pub-location><urls><related-urls><url>;[6]. Numerous EU-wide initiatives have been introduced in an effort to curb and eventually reverse growing electrical demand; these include directives on: energy labelling of major power-consuming appliances ADDIN EN.CITE <EndNote><Cite><Year>1992</Year><RecNum>195</RecNum><record><rec-number>195</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">195</key></foreign-keys><ref-type name="Bill">4</ref-type><contributors></contributors><titles><title>Directive 92/75/EEC of 22 September 1992 on the indication by labelling and standard product information of the consumption of energy and other resources by household appliances.</title><secondary-title>Official Journal of the European Union</secondary-title><tertiary-title>European Parliament and European Council</tertiary-title></titles><dates><year>1992</year><pub-dates><date>22 September 1992</date></pub-dates></dates><label>Official Journal of the European Union</label><urls></urls></record></Cite></EndNote>[7]; enhancing energy efficiency in buildings (EPBD, and proposed EPBD2) ADDIN EN.CITE <EndNote><Cite><Year>2002</Year><RecNum>94</RecNum><record><rec-number>94</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">94</key></foreign-keys><ref-type name="Bill">4</ref-type><contributors></contributors><titles><title>Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on &quot;the energy performance of buildings&quot;</title><secondary-title>Official Journal of the European Union</secondary-title><tertiary-title>European Parliament and European Council</tertiary-title></titles><dates><year>2002</year><pub-dates><date>16 December 2002</date></pub-dates></dates><label>Official Journal of the European Union</label><urls></urls></record></Cite></EndNote>[8]; and increasing energy end-use efficiency ADDIN EN.CITE <EndNote><Cite><Year>2006</Year><RecNum>196</RecNum><record><rec-number>196</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">196</key></foreign-keys><ref-type name="Bill">4</ref-type><contributors></contributors><titles><title>Directive 2006/32/EC of the European Parliament and of the Council of 5 April 2006 on energy end-use efficiency and energy services.</title><secondary-title>Official Journal of the European Union</secondary-title><tertiary-title>European Parliament and European Council</tertiary-title></titles><dates><year>2006</year><pub-dates><date>5 April 2006</date></pub-dates></dates><label>Official Journal of the European Union</label><urls></urls></record></Cite></EndNote>[9]. Additionally, changes in taxation have been proposed to provide financial incentives to increase the uptake of energy efficient services and appliances ADDIN EN.CITE <EndNote><Cite><Author>BIO_Intelligent_Service</Author><Year>2008</Year><RecNum>201</RecNum><record><rec-number>201</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">201</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>BIO_Intelligent_Service</author></authors></contributors><titles><title><style face="normal" font="Arial" size="10">A study on the costs and benefits associated with the use of tax incentives to promote the manufacturing of more and better energy-efficient appliances and equipment and the consumer purchasing of these products.</style></title></titles><dates><year>2008</year></dates><publisher><style face="normal" font="Arial" size="10">European Commission (DG TAXUD) </style></publisher><urls><related-urls><url>;[10].Electrical demand trends published by the European Network of Transmission System Operators for Electricity ADDIN EN.CITE <EndNote><Cite><Author>ENTSOE</Author><Year>2008</Year><RecNum>250</RecNum><record><rec-number>250</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">250</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>ENTSOE</author></authors><secondary-authors><author>Secretariat of ENTSO-E</author></secondary-authors></contributors><titles><title>Statistical Yearbook</title></titles><dates><year>2008</year></dates><pub-location>Brussels (Belgium)</pub-location><publisher>Secretariat European Network of Transmission System Operators for Electricity</publisher><urls></urls></record></Cite></EndNote>[11] show that over the four year period between 2005 and 2008, the European electricity network has witnessed an average increase in peak demands of 1%. On the generation side, increased peak demands incur added costs to provide for more peak generation capacity. From the perspective of the distribution system, high peak demands may create localised problems such as voltage dips and current draws exceeding cable capacities, requiring strengthening of the LV network. 2.PREDICTING THE EFFICACY OF DEMAND MEASURES IN BUILDINGSAn appropriate means to assess the potential effectiveness of different initiatives to reduce electrical energy consumption in dwellings is through demand modelling. Various approaches are evident in the literature, which can be broadly categorised as ‘top-down’ and ‘bottom-up’. With the top-down approach, dwellings in country grouped together as one component in a large econometric-type model, which may also include other sectors of the economy. Examples of this type of model include the UK-MARKAL ADDIN EN.CITE <EndNote><Cite><Author>KCL</Author><Year>2008</Year><RecNum>199</RecNum><record><rec-number>199</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">199</key></foreign-keys><ref-type name="Computer Program">9</ref-type><contributors><authors><author>KCL </author></authors><secondary-authors><author>UK Energy Research Centre</author></secondary-authors></contributors><titles><title>UK MARKAL</title></titles><pages>Bottom-up, dynamic, linear programming (LP) optimisation model </pages><dates><year>2008</year></dates><publisher>UK Energy Research Centre</publisher><urls></urls></record></Cite></EndNote>[12]; which uses readily available bulk economic and social data (e.g. housing stock surveys, appliance ownership, number of households, demolition rates, etc.) to provide an estimate of future energy consumption characteristics of the total housing stock (example) against different economic scenarios. ‘Top-down’ models are calibrated with, and reliant on historical data and according to Ugursal ADDIN EN.CITE <EndNote><Cite><Author>Swan</Author><Year>2009</Year><RecNum>97</RecNum><record><rec-number>97</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">97</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Swan, Lukas G.</author><author>Ugursal, V. Ismet</author></authors></contributors><auth-address>Department of Mechanical Engineering, 1360 Barrington St., Dalhousie University, Halifax, N.S., B3J 1Z1, Canada</auth-address><titles><title>Modeling of end-use energy consumption in the residential sector: A review of modeling techniques</title><secondary-title>Renewable and Sustainable Energy Reviews</secondary-title></titles><periodical><full-title>Renewable and Sustainable Energy Reviews</full-title></periodical><pages>1819-1835</pages><volume>13</volume><number>8</number><keywords><keyword>Residential energy model</keyword><keyword>Residential energy consumption</keyword><keyword>Housing energy model</keyword><keyword>Energy model</keyword></keywords><dates><year>2009</year></dates><isbn>1364-0321</isbn><urls><related-urls><url>;[13] are incapable of accounting for discontinuous changes in technology or individual events that would impact upon energy consumption. Bottom-up or so-called ‘stock’ models are typically housing sector-specific and typically do not account for interactions between sectors. The total building stock is represented as a population of characteristic building types, with the numbers of specific dwellings underpinning each model ranging from under 10 ADDIN EN.CITE <EndNote><Cite><Author>MacGregor</Author><Year>1993</Year><RecNum>216</RecNum><record><rec-number>216</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">216</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>WA MacGregor</author><author>Hamdullahpur F., </author><author>Ugursal VI.,</author></authors></contributors><titles><title>Space heating using small-scale fluidized beds: a technoeconomic evaluation</title><secondary-title>International Journal of Energy Research</secondary-title></titles><periodical><full-title>International Journal of Energy Research</full-title></periodical><pages><style face="normal" font="default" size="100%">445</style><style face="normal" font="default" charset="129" size="100%">–</style><style face="normal" font="default" size="100%">66</style></pages><volume>17</volume><number>6</number><dates><year>1993</year></dates><urls></urls></record></Cite></EndNote>[14] to models consisting of 10,000 dwellings ADDIN EN.CITE <EndNote><Cite><Author>Nishio K</Author><Year>2006</Year><RecNum>217</RecNum><record><rec-number>217</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">217</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>Nishio K, </author><author>Asano H.</author></authors></contributors><titles><title>A residential end-use demand model for analyzing the energy conservation potential of new energy efficient technologies</title><secondary-title>Proceedings of energy efficiency in domestic appliances and lighting</secondary-title></titles><dates><year>2006</year></dates><urls></urls></record></Cite></EndNote>[15]. In these models specific instances of building performance are calculated (often using a simplified energy model) and then scaled up to give an aggregate picture of the performance of the entire stock again according to different scenarios. The advantage of these model types is that they provide greater resolution on the likely energy demands in buildings, providing disaggregated information on domestic energy consumption ADDIN EN.CITE <EndNote><Cite><Author>Swan</Author><Year>2009</Year><RecNum>97</RecNum><record><rec-number>97</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">97</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Swan, Lukas G.</author><author>Ugursal, V. Ismet</author></authors></contributors><auth-address>Department of Mechanical Engineering, 1360 Barrington St., Dalhousie University, Halifax, N.S., B3J 1Z1, Canada</auth-address><titles><title>Modeling of end-use energy consumption in the residential sector: A review of modeling techniques</title><secondary-title>Renewable and Sustainable Energy Reviews</secondary-title></titles><periodical><full-title>Renewable and Sustainable Energy Reviews</full-title></periodical><pages>1819-1835</pages><volume>13</volume><number>8</number><keywords><keyword>Residential energy model</keyword><keyword>Residential energy consumption</keyword><keyword>Housing energy model</keyword><keyword>Energy model</keyword></keywords><dates><year>2009</year></dates><isbn>1364-0321</isbn><urls><related-urls><url>;[13]; and as bottom-up models typically have some form of energy model underpinning them, they can be used to gauge the impact of new technology deployments (e.g. the impact of the widespread uptake of LED lighting). The clear disadvantage of stock models is that unlike econometric models they consider the built environment in isolation from other sectors of the economy. Whilst top-down and bottom-up models can provide policy makers and planners with useful data on the future energy performance of the domestic sector, the typical data output from both of these models is lacking when it comes to looking at the detailed implications of electrical energy efficiency measures on an individual dwelling’s electrical energy demand. Neither model type will provide high-resolution details on the characteristics of the electrical demand such as temporal variation, load duration and peak demands. With the prospect of increasingly distributed energy supplies to the domestic sector featuring increased quantities of micro co-generation, micro-renewables, local energy storage and active load management, such detailed load data for individual dwellings will be required when it comes to (for example): modelling the feasibility and performance of local electrical micro-grids PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TLiBGaXJ0aDwvQXV0aG9yPjxZZWFyPjIwMDg8L1llYXI+

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ADDIN EN.CITE.DATA [16, 17]. Micro-grids, which are low voltage networks to which local demand and supply are connected, can work both in interconnection mode with the main grid and isolated in islanding mode ADDIN EN.CITE <EndNote><Cite><Author>Kamel</Author><Year>2010</Year><RecNum>251</RecNum><record><rec-number>251</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">251</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Rashad M. Kamel</author><author>Chaouachi, Aymen </author><author>Nagasaka, Ken </author></authors></contributors><auth-address>Tokyo University of Agriculture and Technology, Tokyo, Japan</auth-address><titles><title>Wind power smoothing using fuzzy logic pitch controller and energy capacitor system for improvement Micro-Grid performance in islanding mode</title><secondary-title>Energy</secondary-title></titles><periodical><full-title>Energy</full-title></periodical><pages>2119-2129</pages><volume>35</volume><keywords><keyword>Micro-Grid</keyword><keyword>Islanding</keyword><keyword>Fuzzy pitch controller</keyword><keyword>Wind power smoothing</keyword><keyword>Ultra capacitor</keyword></keywords><dates><year>2010</year></dates><urls></urls></record></Cite></EndNote>[18]. In the former case, detailed knowledge of load data for individual dwellings is essential to accurately assess the technical and economic potential assessment of its performance through a more precise estimation of electrical export and import ADDIN EN.CITE <EndNote><Cite><Author>Hawkes</Author><Year>2005</Year><RecNum>114</RecNum><record><rec-number>114</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">114</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Adam Hawkes</author><author>Leach, Matthew</author></authors></contributors><auth-address>ICEPT, Department of Environmental Science and Technology, Imperial College London, South Kensington, London SW7 2AZ, UK</auth-address><titles><title>Impacts of temporal precision in optimising modelling of micro-Combined Heat and Power </title><secondary-title>Energy</secondary-title></titles><periodical><full-title>Energy</full-title></periodical><pages>1759-1779</pages><volume>30</volume><keywords><keyword>Micro-CHP</keyword><keyword>Energy demand volatility</keyword><keyword>Precision</keyword><keyword>Optimisation</keyword></keywords><dates><year>2005</year></dates><urls></urls></record></Cite></EndNote>[19]. In the latter scenario, such knowledge is required to provide sufficient micro-grid generation and hence ensure a supply and demand match. The modelling of domestic electrical demands described in this paper provides this degree of detail in that it can generate high-resolution data relating to the temporal variation in demand of individual appliances using a combination of appliance data, end-use energy surveys and a customised stochastic model. In the paper, the basis of a high-resolution electrical demand model is described; the model is calibrated using southern European data; and the model is then used to predict the potential characteristics of appliance demand for electricity based on projections of technology efficiency developments to 2020.3.ELECTRICAL DEMAND DATA AT HIGH LEVELS OF TEMPORAL RESOLUTIONMost of the work carried out on detailed electricity domestic profiles revolves around the processing and manipulation of energy end-use monitoring campaigns datasets. The campaigns typically involve the direct measurement of electrical energy consumption from selected household appliances, operating under real life conditions. Measurements are either taken using power meters connected individually to separate appliances, from which a total for the whole household is then calculated by aggregating all values for the separate appliances or else, by measurement across the main switchboard: the European projects EURECO ADDIN EN.CITE <EndNote><Cite><Author>EURECO_Partners</Author><Year>2002</Year><RecNum>110</RecNum><record><rec-number>110</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">110</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>EURECO_Partners</author></authors></contributors><auth-address>ENERTECH - 26160 Félines/Rimandoule - France</auth-address><titles><title>EURECO Database - Demand Side Management - End-use metering campaign in 400 households of the European Community (SAVE Program Funded Project)</title><short-title>EURECO</short-title></titles><volume>2009</volume><number>1st December 2009</number><dates><year>2002</year></dates><publisher>ENERTECH - 26160 Félines/Rimandoule - France</publisher><work-type>Database</work-type><urls><related-urls><url> Database</remote-database-name><remote-database-provider>University of Coimbra, Portugal</remote-database-provider></record></Cite></EndNote>[20] and the REMODECE ADDIN EN.CITE <EndNote><Cite><Author>REMODECE_Partners</Author><Year>2008</Year><RecNum>78</RecNum><record><rec-number>78</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">78</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>REMODECE_Partners</author></authors></contributors><auth-address>ISR-University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</auth-address><titles><title>REMODECE Database - Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe (IEEA Program Funded Project)</title><short-title>REMODECE</short-title></titles><volume>2009</volume><number>1st December 2009</number><keywords><keyword>Residential energy</keyword><keyword>residential electricity</keyword><keyword>energy monitoring</keyword><keyword>standby consumption</keyword><keyword>home appliances</keyword><keyword>residential lighting</keyword><keyword>electronic loads</keyword><keyword>air conditioning</keyword><keyword>savings potential</keyword><keyword>energy efficiency policies</keyword><keyword>market transformation</keyword><keyword>climate change</keyword></keywords><dates><year>2008</year></dates><publisher>ISR-University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</publisher><work-type>Database</work-type><urls><related-urls><url> Database</remote-database-name><remote-database-provider>University of Coimbra, Portugal</remote-database-provider></record></Cite></EndNote>[21] are examples of this. 3.1Generating synthetic high-resolution electrical demand profilesA complementary approach to detailed field measurements is to develop synthetic high-resolution data using statistical methods in combination with lower resolution demand data. Stokes ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22], Richardson et al ADDIN EN.CITE <EndNote><Cite><Author>Richardson</Author><Year>2009</Year><RecNum>171</RecNum><record><rec-number>171</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">171</key></foreign-keys><ref-type name="Conference Paper">47</ref-type><contributors><authors><author>Ian Richardson</author><author>Hodgson, Graeme </author><author>Thomson, Murray </author><author>Infield, David </author><author>Delahunty, Alice </author></authors></contributors><titles><title>Simulation of high-resolution domestic electricity demand based on a building occupancy model and its applicability to the study of demand side management</title><secondary-title>5th EEDAL - International Conference on Energy Efficiency in Domestic Appliances and Lighting</secondary-title></titles><dates><year>2009</year><pub-dates><date>16-18 June 2009</date></pub-dates></dates><pub-location>Berlin</pub-location><urls></urls></record></Cite></EndNote>[23], Widen and W?ckelg?rd PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XaWTDqW48L0F1dGhvcj48WWVhcj4yMDA5PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA [24, 25] have all employed this approach. For example, Richardson et al ADDIN EN.CITE <EndNote><Cite><Author>Richardson</Author><Year>2008</Year><RecNum>96</RecNum><record><rec-number>96</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">96</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Richardson, Ian</author><author>Thomson, Murray</author><author>Infield, David</author></authors></contributors><auth-address>CREST (Centre for Renewable Energy Systems Technology), Department of Electronic and Electrical Engineering, Loughborough University, Leicestershire LE11 3TU, UK</auth-address><titles><title>A high-resolution domestic building occupancy model for energy demand simulations</title><secondary-title>Energy and Buildings</secondary-title></titles><periodical><full-title>Energy and Buildings</full-title></periodical><pages>1560-1566</pages><volume>40</volume><number>8</number><keywords><keyword>Domestic occupancy</keyword><keyword>Energy modelling</keyword></keywords><dates><year>2008</year></dates><isbn>0378-7788</isbn><urls><related-urls><url>;[26] utilise demand data generated using UK ‘time-use’ survey data ADDIN EN.CITE <EndNote><Cite><Year>2003</Year><RecNum>215</RecNum><record><rec-number>215</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">215</key></foreign-keys><ref-type name="Aggregated Database">55</ref-type><contributors></contributors><titles><title>United Kingdom Time Use Survey, 2000 (Computer File)</title></titles><volume>third ed.</volume><dates><year>2003</year></dates><pub-location>Colchester, Essex, UK</pub-location><publisher>Ipsos-RSL and Office for National Statistics, UK Data Archive (distributor)</publisher><isbn>SN: 4504</isbn><urls></urls></record></Cite></EndNote>[27]; ‘time-use surveys’ are detailed diaries kept by individuals recording their activities on a daily basis at 10 minute intervals. This data is then manipulated to produce minute resolution electrical demand profiles ADDIN EN.CITE <EndNote><Cite><Author>Richardson</Author><Year>2008</Year><RecNum>96</RecNum><record><rec-number>96</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">96</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Richardson, Ian</author><author>Thomson, Murray</author><author>Infield, David</author></authors></contributors><auth-address>CREST (Centre for Renewable Energy Systems Technology), Department of Electronic and Electrical Engineering, Loughborough University, Leicestershire LE11 3TU, UK</auth-address><titles><title>A high-resolution domestic building occupancy model for energy demand simulations</title><secondary-title>Energy and Buildings</secondary-title></titles><periodical><full-title>Energy and Buildings</full-title></periodical><pages>1560-1566</pages><volume>40</volume><number>8</number><keywords><keyword>Domestic occupancy</keyword><keyword>Energy modelling</keyword></keywords><dates><year>2008</year></dates><isbn>0378-7788</isbn><urls><related-urls><url>;[26].4.METHOD OF PROFILE GENERATION The approach adopted for the generation of electrical demand profiles in this paper is similar, but uses one-hour resolution electrical demand data derived from field monitoring as the starting point. Additionally, the calculation of the detailed demand profile is augmented with a means to adapt the high-resolution profile based on future estimations of improvements in appliance energy efficiency; this allows detailed profiles to be generated for future scenarios. The generation of these future demand profiles is done on an appliance-by-appliance basis; a profile for each appliance is generated and the population of appliance profiles can then be aggregated to give a high-resolution electrical demand profile for the dwelling. The initial base data for the profile generation are hourly datasets of the individual appliances’ energy consumption. In this case, these datasets were obtained from the REMODECE energy end-use measuring campaign ADDIN EN.CITE <EndNote><Cite><Author>REMODECE_Partners</Author><Year>2008</Year><RecNum>78</RecNum><record><rec-number>78</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">78</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>REMODECE_Partners</author></authors></contributors><auth-address>ISR-University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</auth-address><titles><title>REMODECE Database - Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe (IEEA Program Funded Project)</title><short-title>REMODECE</short-title></titles><volume>2009</volume><number>1st December 2009</number><keywords><keyword>Residential energy</keyword><keyword>residential electricity</keyword><keyword>energy monitoring</keyword><keyword>standby consumption</keyword><keyword>home appliances</keyword><keyword>residential lighting</keyword><keyword>electronic loads</keyword><keyword>air conditioning</keyword><keyword>savings potential</keyword><keyword>energy efficiency policies</keyword><keyword>market transformation</keyword><keyword>climate change</keyword></keywords><dates><year>2008</year></dates><publisher>ISR-University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</publisher><work-type>Database</work-type><urls><related-urls><url> Database</remote-database-name><remote-database-provider>University of Coimbra, Portugal</remote-database-provider></record></Cite></EndNote>[21], which was an EU funded project conducted in a number of European countries between 2006 and 2008. The data consisted of both real field measurements and questionnaires returns. The work in this paper specifically used the REMODECE Italian dataset, which consisted of measurements done in 60 households. Data was collected on the most energy intensive appliances and the 10 most used lamps ADDIN EN.CITE <EndNote><Cite><Author>Larsonneur</Author><Year>2006</Year><RecNum>79</RecNum><record><rec-number>79</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">79</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Pascal Larsonneur</author></authors></contributors><auth-address>French Environment and Energy Management Agency</auth-address><titles><title>Establishment of the samples for the monitoring and survey campaigns </title><secondary-title>REMODECE - Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe (IEEA Program Funded Project)</secondary-title><short-title>REMODECE</short-title></titles><volume>Work Package 3 - Sub task 3.2 </volume><dates><year>2006</year></dates><pub-location>Paris</pub-location><publisher>ADEME</publisher><work-type>IEEA Project Funded Program</work-type><urls><related-urls><url>;[28]. Additional measurements were also taken across the main switchboard to measure the overall household electrical energy consumption. The energy consumption for each appliance and each hour was then averaged over the measuring period (2 weeks) to create one representative daily profile of energy demand at an hourly time resolution representative of the whole month for each appliance monitored; these are the base profiles to which the transformation process described below is applied.4.1Transforming the base datasets - OverviewThe methodology used to obtain the final high-resolution profiles representative of the changes brought about by the change-over to more energy-efficient appliances, relies on a three stage transformation approach: Stage 1 of the transformation extrapolates a set of 12 hourly day-long datasets, representative of each month from the original base profile by applying a scalar modifier to the original data; this modifier is partly a time-dependent sinusoidal function and partly a random number. This stage of the transformation therefore ensures that for each appliance 12 complete hourly daily sets, one for each month, are available. Stage 2 transforms the resulting hourly datasets for each month into minute resolution datasets, effectively creating a finer profile resolution. Stage 3 applies the effects due to appliance energy-efficient improvements. Stage 3 can be applied either to Stage 1 or Stage 2 results as discussed later in section 4.1.3. The overall result of the three transformation process is therefore to obtain high-resolution minute long profiles representative of the changes brought about by the change-over to more energy-efficient appliances from coarse resolution appliance hourly datasets. An in-depth analysis of the individual stages follows in sections 4.1.1 to 4.1.3.4.1.1 Stage 1 – Introducing monthly variationSimilar to other end-use measurement campaigns, one limitation of the REMODECE datasets was that only two weeks’ worth of demand data was collected for each household ADDIN EN.CITE <EndNote><Cite><Author>Almeida</Author><Year>2008</Year><RecNum>80</RecNum><record><rec-number>80</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">80</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Aníbal de Almeida</author><author>Fonseca, Paula </author><author>Bandeirinha, Rui </author><author>Fernandes, Tiago </author><author>Araújo, Rui </author><author>Nunes, Urbano </author></authors></contributors><auth-address>ISR-University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</auth-address><titles><title>Final Report REMODECE - Residential monitoring to Decrease Energy use and Carbon Emissions in Europe (IEEA Program Funded Project)</title><secondary-title>REMODECE - Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe (IEEA Program Funded Project)</secondary-title><short-title>REMODECE</short-title></titles><keywords><keyword>Residential energy</keyword><keyword>residential electricity</keyword><keyword>energy monitoring</keyword><keyword>standby consumption</keyword><keyword>home appliances</keyword><keyword>residential lighting</keyword><keyword>electronic loads</keyword><keyword>air conditioning</keyword><keyword>savings potential</keyword><keyword>energy efficiency policies</keyword><keyword>market transformation</keyword><keyword>climate change</keyword></keywords><dates><year>2008</year></dates><pub-location>Coimbra</pub-location><publisher>University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal</publisher><work-type>IEEA Project Funded Program</work-type><urls><related-urls><url>;[2] and in most cases only data for one specific month was available per household. In order to obtain the monthly variation in electrical energy consumption for each individual appliance in the analysed households, a procedure similar to that adopted by Stokes in ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22] was used. The procedure relies on scaling a single appliance’s representative monthly profile using a seasonally-dependent modifier; enabling the generation of a whole-year dataset that incorporates seasonal variations in electrical energy consumption. The coefficients of the modifier which, as described by Stokes, follows a sinusoidal trend coupled with some random ‘noise’ were obtained using the following procedure.The original REMODECE Italian datasets for each appliance were first grouped by appliance type and then sorted on a per month basis as represented by the left hand side matrix shown in Figure 1. Given the different appliances’ ownership rates, frequency of use and operating behaviour (repetitive/cyclic, such as in the case of refrigerators or single-off events, such as washing machines), multiple sets of hourly data were available for each month for the most commonly owned, frequently used or repetitive/cyclic behaviour appliances such as refrigerators (32 datasets), electronic equipment (27 datasets), televisions (32 datasets), lighting (600 datasets) and water heaters (40 datasets). Conversely, for the other less commonly owned, less used single-off event appliances such as dish washers (12 datasets), microwave ovens (12 datasets), electric ovens (12 datasets) and washing machines (34 datasets) the data available was mostly enough to produce just one single data entry for each individual month. This effectively resulted in a situation whereby, as discussed in the verification process, the results derived from the methodology described in Stage 1 were most accurate for the former type of appliances. Fig. 1 – Grouping and normalisation of the original datasetsFor each grouped appliance dataset, each column in the left hand side matrix was then divided by the highest occurring row value, effectively normalising the hourly electrical energy consumption values by the maximum consumption value recorded during that hour (contained in the original dataset). This gives a flexible, dimensionless hourly value that can be scaled to represent variations in demand over the course of a year. The end result of this normalisation process, are the columns present in the right hand side matrix in Figure 1, which represent the normalised hourly consumption values. Once calculated, each column in the right hand side matrix was then used to find the coefficients of the general trend described in equation (1) for each individual Hour i. QUOTE - (1)Equation (1) describes the normalised electrical energy consumption for a specific domestic Appliance k during Hour i of Month j, EAppk,Monthj,Houri. As discussed by Stokes ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22] , this is made up of three parts. A constant θi; a sinusoidally varying component with amplitude Ai and phase angle φi and a third part made up of a random ‘noise’ value. The constant θi, amplitude Ai and phase angle φi for each individual Hour i were obtained by applying a simplified curve fitting algorithm to the values contained in each individual column present in the right hand matrix of Figure 1 (normalised values of each hour set Houri,Month1, Houri,Month2,…., Houri,Month12 etc.) ADDIN EN.CITE <EndNote><Cite><Author>John</Author><Year>1998</Year><RecNum>249</RecNum><record><rec-number>249</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">249</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>E. G. John</author></authors></contributors><auth-address><style face="normal" font="default" size="100%">Systems Division, School of Engineering, Cardiff University, Cardiff CF2 3TE, Wales UK</style><style face="normal" font="default" size="10">.</style></auth-address><titles><title>Simplified Curve Fitting using Spreadsheet Add-ins</title><secondary-title>Int. J. Eng Ed.</secondary-title></titles><periodical><full-title>Int. J. Eng Ed.</full-title></periodical><pages>375 - 380</pages><volume>14</volume><number>5</number><dates><year>1998</year></dates><urls></urls></record></Cite></EndNote>[29], effectively, calibrating equation (1) for each individual Hour i. The random value added in the third part of the equation was obtained by using a uniformly distributed selection to select a value from a range comprising σSTDEVi and -σSTDEVi, the standard deviation value calculated for the values contained in each column present in the right hand side matrix of Figure 1 (normalised values of each hour set Houri,Month1, Houri,Month2,…., Houri,Month12 etc.).Equation (1) differs from that proposed by Stokes in ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22] in two ways. Firstly, given that the monitored data was only available as a profile representing one day of each month, the variations in appliance hourly electrical energy consumption were calculated on a monthly basis (Stokes applies the calculation on a daily basis). However, this can be changed if more than one daily profile is available for each appliance for each month. Secondly, given that the standard deviation was being calculated from a relatively small dataset, choosing a random value using a normal distribution as suggested by Stokes from such a small sample might not have given representative results. For this reason in this particular case the random ‘noise’ was selected using a uniform distribution. Nonetheless, the results obtained and the subsequent verification of the approach discussed later on in the paper show that such minor differences do not invalidate the method or the results obtained. For each appliance, the end result was an hourly list of coefficients which when applied in equation (1) give the trend in demand followed by that particular appliance for each of the 24 hours at any time of the year. Table 1, shows some of the parameters calculated for a refrigerator. Table 1 – Parameters for seasonal variation equation for a refrigeratorHour iConstant(θi)Amplitude(Ai)Sine Phase(φi) Standard Deviation(σSTDEVi)[0,1]0.264-0.0720.8180.079[1,2]0.288-0.0710.5400.069:::::[22,23]0.230-0.0640.8160.079[23,24]0.218-0.0681.0200.086Once the coefficients were obtained for each individual appliance, the original datasets could be scaled to include for seasonal variation. The original dataset containing the hourly electrical energy consumption of a particular appliance was first divided by the corresponding normalised (maximum hourly) electrical energy consumption EAppk,Monthj,Houri, calculated for that month using equation (1). The resulting value was then multiplied by EAppk,Monthj,Houri (again calculated using equation (1)) for the desired month in order to obtain the electrical energy consumption for that specific appliance at a particular hour in that month. Using this approach two sets of appliance data are available for the whole year: the original averaged measured dataset based on all the appliances’ data collected in the REMODECE database and a modelled dataset based on the calculated sinusoidal trend which can be used to seasonally scale individual appliances. This same procedure was used for all appliances for which datasets were available including televisions, PCs, electric water heaters, lighting, microwaves ovens and washing machines.The aggregation of the appliances’ energy demand available from the original dataset does not, however, add up to the total electrical energy demand of a household. As discussed by Widen in ADDIN EN.CITE <EndNote><Cite><Author>Widén</Author><RecNum>118</RecNum><record><rec-number>118</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">118</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Widén, Joakim</author><author>W?ckelg?rd, Ewa</author></authors></contributors><titles><title>A high-resolution stochastic model of domestic activity patterns and electricity demand</title><secondary-title>Applied Energy</secondary-title></titles><periodical><full-title>Applied Energy</full-title></periodical><pages>1880-1892 </pages><volume>87</volume><number>6</number><keywords><keyword>Domestic electricity demand</keyword><keyword>Stochastic</keyword><keyword>Markov chain</keyword><keyword>Bottom-up</keyword><keyword>Load model</keyword></keywords><dates><year>2010</year></dates><isbn>0306-2619</isbn><urls><related-urls><url>;[25], end-measurement campaigns are usually unsuccessful in measuring and recording all of a household’s electrical energy consumption by monitoring individual appliances, and some of the household’s consumption remains unspecified. This unspecified demand may be broadly divided into two categories - a continuous base load demand as suggested by Widen ADDIN EN.CITE <EndNote><Cite><Author>Widén</Author><RecNum>118</RecNum><record><rec-number>118</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">118</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Widén, Joakim</author><author>W?ckelg?rd, Ewa</author></authors></contributors><titles><title>A high-resolution stochastic model of domestic activity patterns and electricity demand</title><secondary-title>Applied Energy</secondary-title></titles><periodical><full-title>Applied Energy</full-title></periodical><pages>1880-1892 </pages><volume>87</volume><number>6</number><keywords><keyword>Domestic electricity demand</keyword><keyword>Stochastic</keyword><keyword>Markov chain</keyword><keyword>Bottom-up</keyword><keyword>Load model</keyword></keywords><dates><year>2010</year></dates><isbn>0306-2619</isbn><urls><related-urls><url>;[25] and a number of random miscellaneous loads as suggested by Stokes ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22]. In order to calculate the amount of unspecified demand attributable to each category, a similar procedure to that adopted to find the monthly variation of individual appliances was applied to the general electrical energy consumption values, which were also measured in the REMODECE campaign for each individual household. Based on the values calculated for the electrical energy consumption for each household deduced using this method, the minimum value obtained, that is the minimum hourly electrical energy consumption, was aggregated to the entire profile as a continuous daily base load. Any difference between the aggregated appliance load (now including the base load) and the general electric energy consumption calculated on an hourly basis was awarded to a miscellaneous load occurring during that hour.The Stage 1 methodology can be adapted to discriminate between weekdays and weekends in that two individual sets of trends can be calculated separately for both weekdays and weekends; one reflecting the monthly hourly variation on weekdays and the other the monthly hourly variation on weekends. However this is only possible if suitable data is available. A limitation of this study is that the original data used in this research does not distinguish between weekdays and weekends, hence differences between weekdays and weekends are not taken into account.4.1.2 Stage 2 – Converting to one-minute time resolutionStage 2 takes on from the work done in Stage 1 to produce 1-minute time resolution load profiles for each hour of each individual appliance. The presumptions behind this process were that: most appliances (including television sets, entertainment appliances, water heaters, fridges, cooking and lighting) can be assumed to follow a simple square ON/OFF pulse pattern varying between zero power during its OFF state and the steady-state operating power during its ON state ADDIN EN.CITE <EndNote><Cite><Author>Widén</Author><RecNum>118</RecNum><record><rec-number>118</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">118</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Widén, Joakim</author><author>W?ckelg?rd, Ewa</author></authors></contributors><titles><title>A high-resolution stochastic model of domestic activity patterns and electricity demand</title><secondary-title>Applied Energy</secondary-title></titles><periodical><full-title>Applied Energy</full-title></periodical><pages>1880-1892 </pages><volume>87</volume><number>6</number><keywords><keyword>Domestic electricity demand</keyword><keyword>Stochastic</keyword><keyword>Markov chain</keyword><keyword>Bottom-up</keyword><keyword>Load model</keyword></keywords><dates><year>2010</year></dates><isbn>0306-2619</isbn><urls><related-urls><url>;[25]; andcertain appliances such as washing machines, a finite amount of energy consumed by that particular appliance over an hour can be re-modelled using a known energy utilisation pattern.In both cases switching-on transients are ignored as these are for the most part only seconds long ADDIN EN.CITE <EndNote><Cite><Author>Zmeureanu</Author><Year>1999</Year><RecNum>150</RecNum><record><rec-number>150</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">150</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Radu Zmeureanu</author><author>Farinaccio, Linda </author></authors></contributors><auth-address>Department of Building, Ci?il and En?ironmental Engineering, Centre for Building Studies, Concordia Uni?ersity, Montreal, Quebec, Canada H3G 1M8</auth-address><titles><title>Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses</title><secondary-title>Energy and Buildings </secondary-title></titles><periodical><full-title>Energy and Buildings</full-title></periodical><pages>245-259</pages><volume>30</volume><dates><year>1999</year></dates><urls></urls></record></Cite></EndNote>[30]. 4.1.2.1Appliances with On/Off patternFor an appliance k having a steady-state operating power POn-Appk in Watts (W), whose electrical energy consumption during Hour i was EAppk,Houri in Watt-Hours (Wh), the active time in minutes, minOn(Hour i), for that appliance can be calculated using: QUOTE - (2)If the appliance has a stand-by power, PStand-By, in Watts, then equation (2) becomes: QUOTE - (3)In the generation of the high-resolution profile, the start of the appliance’s power use during the hour is assigned on a random basis using a uniform distribution. In selecting the appropriate operating steady-state power (POn-Appk) of an appliance a review of typical steady-state power ratings was undertaken. This is shown in Table 2. Figure 2 shows the resulting 1-minute profile when applying the Stage 2 transformation method to the hourly consumption data of an 85 Watts CRT TV. Fig. 2 – Minute resolution for the power demand of an 85 Watts CRT TV created using the transformation method Table 2 - Range of steady-state power ratings for selected appliances ApplianceRange of steady-state operatingpower ratings (POn-Appk) (Watts) CommentRefrigerator, Fridge Freezers and Freezers ADDIN EN.CITE <EndNote><Cite><Author>EURECO_Partners</Author><Year>2002</Year><RecNum>149</RecNum><record><rec-number>149</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">149</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>EURECO_Partners</author></authors></contributors><titles><title>Final Report</title></titles><volume>Final Report</volume><number>EURECO Project - End-use metering campaign in 400 households of the European Community, SAVE Programme Contract N° 4.1031/Z/98-267</number><dates><year>2002</year></dates><publisher>EURECO Project - End-use metering campaign in 400 households of the European Community</publisher><urls></urls></record></Cite></EndNote>[31]80 - 250Television (CRT) ADDIN EN.CITE <EndNote><Cite><Author>Almeida</Author><Year>2006</Year><RecNum>151</RecNum><record><rec-number>151</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">151</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>A. Almeida </author><author>Fonseca, P. </author><author>Schlomann, B. </author><author>Feilberg, N. </author><author>Ferreira, C. </author></authors></contributors><titles><title>Residential monitoring to Decrease Energy use and Carbon Emissions in Europe</title><secondary-title>Energy Efficiency in Domestic Appliances and Lighting (EEDAL) Conference 2006</secondary-title></titles><dates><year>2006</year></dates><pub-location>London</pub-location><urls></urls></record></Cite></EndNote>[32]32 - 185Active stand-by may account to a maximum of 12 Watts.Television (LCD) ADDIN EN.CITE <EndNote><Cite><Author>Almeida</Author><Year>2006</Year><RecNum>151</RecNum><record><rec-number>151</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">151</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>A. Almeida </author><author>Fonseca, P. </author><author>Schlomann, B. </author><author>Feilberg, N. </author><author>Ferreira, C. </author></authors></contributors><titles><title>Residential monitoring to Decrease Energy use and Carbon Emissions in Europe</title><secondary-title>Energy Efficiency in Domestic Appliances and Lighting (EEDAL) Conference 2006</secondary-title></titles><dates><year>2006</year></dates><pub-location>London</pub-location><urls></urls></record></Cite></EndNote>[32]31 - 421Active stand-by may account to a maximum of 18 Watts.Microwave (Defrost) ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22]200 - 300Microwave (Cooking) ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22]700 - 1,300Typical peak power is of 900 Watts. Variations including other accessories account for the variable power range.Electric Oven ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite><Cite><Author>Widén</Author><RecNum>118</RecNum><record><rec-number>118</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">118</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Widén, Joakim</author><author>W?ckelg?rd, Ewa</author></authors></contributors><titles><title>A high-resolution stochastic model of domestic activity patterns and electricity demand</title><secondary-title>Applied Energy</secondary-title></titles><periodical><full-title>Applied Energy</full-title></periodical><pages>1880-1892 </pages><volume>87</volume><number>6</number><keywords><keyword>Domestic electricity demand</keyword><keyword>Stochastic</keyword><keyword>Markov chain</keyword><keyword>Bottom-up</keyword><keyword>Load model</keyword></keywords><dates><year>2010</year></dates><isbn>0306-2619</isbn><urls><related-urls><url>;[22, 25]1,000 - 3,000Electric Water Heater ADDIN EN.CITE <EndNote><Cite><Author>Widén</Author><Year>2009</Year><RecNum>129</RecNum><record><rec-number>129</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">129</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Widén, Joakim</author><author>Lundh, Magdalena</author><author>Vassileva, Iana</author><author>Dahlquist, Erik</author><author>Elleg?rd, Kajsa</author><author>W?ckelg?rd, Ewa</author></authors></contributors><titles><title>Constructing load profiles for household electricity and hot water from time-use data-Modelling approach and validation</title><secondary-title>Energy and Buildings</secondary-title></titles><periodical><full-title>Energy and Buildings</full-title></periodical><pages>753-768</pages><volume>41</volume><number>7</number><keywords><keyword>Load modelling</keyword><keyword>Time-use data</keyword><keyword>Household electricity</keyword><keyword>Hot water</keyword><keyword>Load profiles</keyword></keywords><dates><year>2009</year></dates><isbn>0378-7788</isbn><urls><related-urls><url>;[24]1,000 - 3,000.Hi-Fi ADDIN EN.CITE <EndNote><Cite><Author>Almeida</Author><Year>2006</Year><RecNum>151</RecNum><record><rec-number>151</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">151</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>A. Almeida </author><author>Fonseca, P. </author><author>Schlomann, B. </author><author>Feilberg, N. </author><author>Ferreira, C. </author></authors></contributors><titles><title>Residential monitoring to Decrease Energy use and Carbon Emissions in Europe</title><secondary-title>Energy Efficiency in Domestic Appliances and Lighting (EEDAL) Conference 2006</secondary-title></titles><dates><year>2006</year></dates><pub-location>London</pub-location><urls></urls></record></Cite></EndNote>[32]4 - 40Laptop ADDIN EN.CITE <EndNote><Cite><Author>Almeida</Author><Year>2006</Year><RecNum>151</RecNum><record><rec-number>151</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">151</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>A. Almeida </author><author>Fonseca, P. </author><author>Schlomann, B. </author><author>Feilberg, N. </author><author>Ferreira, C. </author></authors></contributors><titles><title>Residential monitoring to Decrease Energy use and Carbon Emissions in Europe</title><secondary-title>Energy Efficiency in Domestic Appliances and Lighting (EEDAL) Conference 2006</secondary-title></titles><dates><year>2006</year></dates><pub-location>London</pub-location><urls></urls></record></Cite></EndNote>[32]40 – 100Different active stand-by and hibernation modes make it difficult to establish a single stand-by power demand. Indicative figures for the two modes of stand-by mentioned are 20 - 40 Watts for laptops and 20 – 70 Watts for desktops.Desktop ADDIN EN.CITE <EndNote><Cite><Author>Almeida</Author><Year>2006</Year><RecNum>151</RecNum><record><rec-number>151</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">151</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>A. Almeida </author><author>Fonseca, P. </author><author>Schlomann, B. </author><author>Feilberg, N. </author><author>Ferreira, C. </author></authors></contributors><titles><title>Residential monitoring to Decrease Energy use and Carbon Emissions in Europe</title><secondary-title>Energy Efficiency in Domestic Appliances and Lighting (EEDAL) Conference 2006</secondary-title></titles><dates><year>2006</year></dates><pub-location>London</pub-location><urls></urls></record></Cite></EndNote>[32]50 – 175Set-Top Boxes ADDIN EN.CITE <EndNote><Cite><Author>Almeida</Author><Year>2006</Year><RecNum>151</RecNum><record><rec-number>151</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">151</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>A. Almeida </author><author>Fonseca, P. </author><author>Schlomann, B. </author><author>Feilberg, N. </author><author>Ferreira, C. </author></authors></contributors><titles><title>Residential monitoring to Decrease Energy use and Carbon Emissions in Europe</title><secondary-title>Energy Efficiency in Domestic Appliances and Lighting (EEDAL) Conference 2006</secondary-title></titles><dates><year>2006</year></dates><pub-location>London</pub-location><urls></urls></record></Cite></EndNote>[32]20During stand-by periods, which can account to 80% of the active time a continuous demand of 9 Watts is drawn.4.1.2.2Complex demand patternsFor those appliances with complex demand patterns, such as dishwashers and washing machines, appliance cycle patterns presented by Stokes in ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22] were employed. The data presented by Stokes provides the minutely power demand over the duration of a whole cycle for different appliances and for different cycles. In order to decide which particular cycle was to be selected, the electrical energy consumption derived from Stage 1 was matched to the total electrical energy consumption per cycle calculated using Stoke’s pattern with the closest match yielding the selected pattern. Again the start of the cycle is awarded on a random basis following a uniform distribution. This time however since the cycle can be longer than one hour, a single cycle may continue over to the following hour. 4.1.2.3Pattern for the miscellaneous demandThe demand which in Stage 1 was awarded to an unspecified miscellaneous electrical demand is assumed to follow an On/Off pattern with POn-Appk chosen at random from a range of 50 Watts and 2,000 Watts which roughly covers the steady-state power demand of most domestic appliances ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22]. Again the start of the cycle is awarded on a random basis following a uniform distribution.4.1.3Stage 3 – Accounting for improvements in energy-efficiencyIn order to obtain the yearly scaling factors by which the individual appliance profiles could be transformed to reflect changes due to efficiency improvements, data from the UK’s Department for Environment, Food and Rural Affairs (DEFRA) Market Transformation Program (MTP) ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><RecNum>147</RecNum><record><rec-number>147</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">147</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>DEFRA</author></authors></contributors><titles><title>Market Transformation Programme</title></titles><number>8th April 2010</number><keywords><keyword>DEFRA, MTP</keyword></keywords><dates></dates><pub-location>UK</pub-location><urls><related-urls><url>;[33] was used. Although the data is based on a UK program and the profiles under investigation are those of Italian households, the fact that the assumptions and policy instruments used in the MTP are mostly based on the EU wide energy-efficiency labelling program helps to ensure the intra-country validity. The MTP sets out the envisaged improvements due to energy-efficiency in specific appliances up to 2020. In practice, these improvements mimic what would happen to appliance energy consumption, should households change their existing appliances to more energy efficient ones. The data used, effectively comes from a database used within the MTP program, namely, the “What If?Tool” ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2008</Year><RecNum>218</RecNum><record><rec-number>218</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">218</key></foreign-keys><ref-type name="Online Database">45</ref-type><contributors><authors><author>DEFRA</author></authors></contributors><titles><title>What if? Tool </title></titles><dates><year>2008</year><pub-dates><date>1st March 2010</date></pub-dates></dates><publisher>Defra</publisher><urls><related-urls><url>;[34]. The database presents future energy consumption for a number of commercial and residential appliances under different policy scenarios. One of these scenarios named the Earliest Best Practice Scenario specifies what would happen to various appliances’ electrical energy consumption should the market adopt the best available technologies ignoring financial or other market barriers. Based on the “What If?Tool” Earliest Best Practice Scenario’s data, the scaling factors for each appliance were obtained by dividing the electrical energy consumption used in 2008, which is the reference year for this work, by the expected consumption in 2020. In most cases the scaling factor is simply a result of the technology’s development and so it applies to the device average power draw. For example, for appliances such as lighting and television, the scaling factor reflects the fact that the time period during which such appliances will be active will remain unchanged, and the change will only be due to changes in the electrical power demand of that appliance. However, in some cases the scaling factor is the result of changes in human requirements and behaviour, such as the expected shift from cooking using traditional ovens in favour of microwave ready-cooked meals ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2008</Year><RecNum>153</RecNum><record><rec-number>153</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">153</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Assumptions underlying the energy projections of cooking appliances</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2008</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[35]. The scaling factor in this case will have an effect on the time the appliance is active. The scaling factors are therefore applied to either the average steady-state power demand of the device keeping the active time calculated in Stage 2 constant, or to the time of use of the device keeping the steady-state power demand assumed in Stage 2. Table 3 gives the calculated scaling factors used in this study for the most common household appliances and whether these should be applied to the steady-state power demand or the time-of-use. Table 3 – 2020 Scaling factors for selected appliances under the future Earliest Best Practice ScenarioAppliance2020 Scaling factor compared to 2008 present scenarioApplication of scaling factorRationale behind assumed changeRefrigerator0.467reduced power demandShift towards A++ technology ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2008</Year><RecNum>152</RecNum><record><rec-number>152</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">152</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Assumptions underlying the energy projections for domestic cold appliances</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2008</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[36].Fridge Freezer0.650reduced power demandElectric Oven0.690reduced time-in-useReduction in cooking time, driven by technology improvements and aptitude towards more ready-made meals ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2008</Year><RecNum>153</RecNum><record><rec-number>153</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">153</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Assumptions underlying the energy projections of cooking appliances</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2008</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[35].Microwave Oven1.016increased time-in-useMore frequent use ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2008</Year><RecNum>153</RecNum><record><rec-number>153</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">153</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Assumptions underlying the energy projections of cooking appliances</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2008</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[35].Electric Water Heater0.943reduced time-in-useSlight improvement brought about by better insulation ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2008</Year><RecNum>155</RecNum><record><rec-number>155</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">155</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Water heaters: proposed energy labelling directives</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2008</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite><Cite><Author>DEFRA</Author><Year>2008</Year><RecNum>154</RecNum><record><rec-number>154</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">154</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Policy Brief: Improving the energy performance of domestic heating and hot water systems</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2008</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[37, 38].Television0.782reduced power demand and standbyTechnology evolution leading to OLED. Envisaged that new technologies will be mature and efficient by 2020 with additional energy-efficiency features such as automatic switching off during prolonged stand-by periods or motion sensors ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2009</Year><RecNum>161</RecNum><record><rec-number>161</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">161</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Televisions (TVs) Government Standards Evidence Base 2009: Best Available Technology Scenario</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2009</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[39].Domestic Lighting0.502reduced power demandIncreased use of Compact Fluorescent Lamps (CFLs) and LEDs, with direct replacement of tungsten filament lamps ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2010</Year><RecNum>156</RecNum><record><rec-number>156</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">156</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Domestic Lighting Government Standards Evidence Base 2009: Best Available Technology Scenario</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2010</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[40]. Given that a range of power ratings is equivalent to an incandescent bulb the scaling factor represents a conservative average of the future puter0.364reduced power demandGeneral improvement in energy-efficiency ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2008</Year><RecNum>157</RecNum><record><rec-number>157</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">157</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>ENERGY STAR? specification for computers</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2008</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[41].Set-Top Boxes1.050increased power demandAlthough more elaborate and powerful set-top boxes will be available, efficiency is expected to improve slightly ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2010</Year><RecNum>160</RecNum><record><rec-number>160</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">160</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Set Top Boxes Government Standards Evidence Base 2009: Best Available Technology Scenario</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2010</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[42].Dishwasher (65 Deg?C cycle) (55 Deg?C cycle)0.8450.902reduced power demandImproved technology and better detergents ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2010</Year><RecNum>158</RecNum><record><rec-number>158</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">158</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Dishwashers Government Standards Evidence Base 2009: Best Available Technology Scenario</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2010</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[43].Washing Machine (90 Deg?C cycle) (60 Deg?C cycle) (40 Deg?C cycle)0.9580.8950.902reduced power demandImproved technology with better laundry load management ADDIN EN.CITE <EndNote><Cite><Author>DEFRA</Author><Year>2010</Year><RecNum>159</RecNum><record><rec-number>159</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">159</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>DEFRA</author></authors><tertiary-authors><author>UK, DEFRA</author></tertiary-authors></contributors><titles><title>Combined Laundry: Government Standards Evidence Base 2009: Best Available Technology Scenario</title><secondary-title><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></secondary-title></titles><dates><year>2010</year></dates><publisher><style face="normal" font="default" size="100%">Volume in Series </style><style face="italic" font="default" size="100%">&quot;Market Transformation Programme&quot;</style></publisher><urls></urls></record></Cite></EndNote>[44].Given that the end result of using the scaling factor in Stage 3 is a change in the electrical energy consumption, the scaling factor can also be applied directly to Stage 1 in order to project the effect of energy-efficiency onto the future electrical energy demand.4.2.1 Application: Generating full-year high-resolution 2020 REMODECE datasets The three-stage transformation process described was used to produce predicted high-efficiency appliance energy consumption profiles for 8 Italian households monitored in the REMODECE end-use measurement campaign. As shown in Table 4 the households taken into consideration have different occupancy levels and different levels of appliance ownership. All dwellings are apartments.Table 4 – Characteristics and appliance ownership of the 8 modelled householdsHousehold codeNumber of occupantsAppliances monitored in the REMODECE campaignLights monitored in the REMODECE campaign (values are in Watts)1A1Hi-Fi, Fridge, Laptop, Microwave Oven, Washing Machine2x60, 2x75, 1x15, 1x401B1Fridge, Water Heater, PC, Washing Machine,2x17, 1X41, 1x26, 1X372A2TV, Fridge, Laptop, Electric Oven, Washing Machine2x7, 2X140, 1x60, 1X402B2Fridge, Laptop, Electric Oven, Water Heater, Microwave Oven, Dish Washer, Washing Machine2x75, 2x25, 1x60, 1x30, 1x11, 1x33, 1x80, 1x100, 1x703A3Hi-Fi, Fridge, Laptop, Water Heater, Electric Oven, Microwave, Dish Washer Washing Machine2x11, 2x25, 2x75, 1x70, 1x80, 1x60, 1x30, 1x333B3TV, Hi-Fi, Fridge, Set-Top Box, Electric Oven, Coffee Machine, Microwave Oven, Washing Machine2x25, 1x20, 1x7, 1x120, 1x1004A42xTV, Hi-Fi, Fridge, Microwave Oven, Washing Machine4x25, 2x40, 1x120, 1x35, 1x804B42xTV, Fridge, Microwave Oven, Dish Washer, Washing Machine1x225, 1x40, 1x50, 1x36, 1x8, 1x15, 1x22, 1x16For each of the selected households 12 characteristic days (one for each month) were generated, for the present and future scenarios. The present scenario represents the typical electrical energy consumption in 2008 (the year in which the REMODECE data was recorded) with Stage 1 and Stage 2 being applied to the original dataset to first find the monthly hourly variation and then the minute resolution respectively. The future scenario is the resulting data obtained after Stage 3 has been applied to the present scenario and represents the energy-efficient 2020 scenario. In the analysis carried out the two scenarios were compared on two different levels, that is, at appliance and household level.4.2.2 Selecting the number of representative characteristic days Given the limitations in the original dataset described previously, only a single representative day in each month can be generated using the approach presented previously for each scenario, giving a possible 12 characteristic days covering the year for each scenario. Three of these days are presented in detail as representative of a shoulder (transition) [May], summer [August] and winter [February] days. However, changes in demand characteristics observed throughout the year between each scenario (as discussed in Section 5.1.2 and 5.2.2 respectively) are calculated using all 12 representative days.5.0 RESULTSIn this section the results from application of the transformation process to the appliances and households described in Table 4 are presented. 5.1Appliance Level 5.1.1Appliance Level – Instantaneous power demandChanges at this particular level are strongly dependent on the individual characteristics of the particular appliance. Figure 3, for example illustrates the impacts due to energy-efficiency improvements assumed by the Earliest Best Practice Scenario on the daily instantaneous electric power demand for household 4B due to lighting for a characteristic day in February. It is clear that the changeover to LED technology and Compact Fluorescent Lamps (CFLs) from incandescent bulbs will lead not only to a lower electrical energy consumption (the daily consumption in the example shown in Figure 3 is reduced from 0.537kWh for the present scenario to 0.292kWh for the future scenario) but also a lower instantaneous electric demand. A similar analysis was performed for all the other appliances. Comparable results were obtained for most appliances. Fig. 3 - Electric load profile due only to lighting for household 4B in FebruaryAn aspect which is worth further discussion is the fact that stand-by consumption is becoming an increasing problem with most manufactures opting to equip appliances with stand-by power functions ADDIN EN.CITE <EndNote><Cite><Author>Tanides</Author><Year>2008</Year><RecNum>219</RecNum><record><rec-number>219</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">219</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tanides, C. G.</author></authors></contributors><titles><title>Estimation of stand-by energy consumption and energy saving potential in Argentine households</title><secondary-title>Energy for Sustainable Development</secondary-title></titles><periodical><full-title>Energy for Sustainable Development</full-title></periodical><pages>76-80</pages><volume>12</volume><number>4</number><keywords><keyword>energy efficiency</keyword><keyword>energy savings</keyword><keyword>residential sector</keyword><keyword>stand-by</keyword><keyword>electronic equipment</keyword></keywords><dates><year>2008</year></dates><isbn>0973-0826</isbn><urls><related-urls><url>;[45]. In this context should energy reduction devices such as motion sensors become more widely utilised, certain appliances might even experience a higher reduction in their electrical energy consumption than that predicted in this study.5.1.2Appliance Level – Yearly aggregation Figure 4 shows the aggregated annual electrical energy consumption for all appliances of all 8 households on a by-appliance basis for the two reference years, 2008 (present scenario) and 2020 (future scenario). The annual electric energy consumption in this paper is considered to be the product of the daily electrical energy consumption, calculated for each characteristic day of each month, for the level being considered, in this case each individual appliance, and the number of days present in each month. The annual consumption is therefore the total addition.Fig. 4 - Annual electrical energy consumption aggregated for all households on a by-appliance basisAs might be expected, electric water heating is one of the highest energy demands given its high operating power rating and relative frequent use. Unfortunately, little improvement is expected in terms of energy-efficiency of this particular appliance and the percentage change shows only a minor reduction of about 7% in electrical energy consumption. In this case a change in technology used or fuel may be more useful. On the other hand, improvements in energy-efficiency will result in a huge reduction in energy consumption of refrigerating appliances, which is currently another significant energy consumer. Electronics equipment and lighting, with energy consumption reductions of 30% and 50% respectively, are two other sectors in which improvements due to energy-efficiency are expected to be considerable.5.2Household Level5.2.1 Household Level – Daily profiles for the present and future scenariosOnce the profiles for the individual appliances were created for both scenarios these were aggregated on a per household basis. Taking the case of household 4B for which the lighting electric load was analysed in section 5.1.1, Figure 5 shows the total daily power demand profile for the future scenario superimposed on that of the present scenario for a characteristic day in February. Figure 6 is a similar chart for the same household but for a characteristic day in August.Fig. 5 - Household 4B daily power demand profile for a characteristic day in February for the present and future scenario Figure 5 shows how the changeover to energy-efficient appliances in the future scenario will have a positive outcome in terms of energy reduction on the demand profile. In fact, whereas the total daily electrical energy demand and the average daily demand will both experience a decrease of around 26%, the daily peak demand occurring during the day will decrease by 22%, down from 2,597W to 2,129W. Similarly for a characteristic day in August as shown in Figure 6, the total daily electrical energy demand and the average daily demand will both experience a decrease of around 26% whilst the daily peak demand occurring during the day will decrease by 12%, down from 3,042W to 2,712W. Fig. 6 - Household 4B daily power demand profile for a characteristic day in August for the present and future scenarioA similar analysis was carried out for all the other households. Table 5 summarises the results for the three characteristic days chosen as representative of their respective season.Table 5 – Summarised results for all householdsMonthValueScenarioHousehold1A1B2A2B3A3B4A4 BFebruaryAverage dailydemand(W)Present180251147431525289387411Future132207108335413218289303% Difference-36-21-37-29-27-33-34-36Dailypeak demand (W)Present2,1511,9882,1343,3203,4662,2192,6212,597Future2,0032,4412,0053,6223,0571,9722,6172,129% Difference-719-68-13-130-22Daily electrical energy consumption (kWh)Present4.336.083.5310.3012.666.929.289.84Future3.184.962.598.049.945.226.927.27% Difference-36-23-36-28-27-33-34-35MayAverage dailydemand(W)Present167252135405489259361366Future123197107314394196257274% Difference-36-28-26-29-24-32-41-34Dailypeak demand (W)20082,0612,0292,0483,2344,0652,2762,7842,59020202,0002,8272,0553,3443,3702,6553,7632,240% Difference-32803-211426-16Daily electrical energy consumption (kWh)Present4.056.033.289.7511.846.198.608.78Future2.974.702.587.559.454.706.176.62% Difference-36-28-27-29-25-32-39-33AugustAverage daily demand(W)Present12321495304299186250289Future8716270225229140179212% Difference-42-33-36-35-31-33-40-36Dailypeak demand (W)Present1,6612,0392,0213,2023,4382,1412,5553,042Future2,0181,9832,0042,0842,1632,0092,2702,712% Difference18-3-1-54-59-7-13-12Daily electrical energy consumption (kWh)Present2.965.122.297.287.184.456.006.95Future2.083.861.685.415.503.364.305.07% Difference-42-32-36-35-31-33-40-37Although, only three specific days are being presented in this research it is clear from Table 5 that with more energy-efficient appliances being deployed in the future scenario there will a significant reduction in both the daily electrical energy consumption and the average daily demand. In fact, the average reduction for these two parameters for all households investigated is between 20% and 40% depending on the type of appliances ownership. However, it is important to point out that the predicted daily peak demands in the future scenario are not always lower than those in the present scenario. Such a result suggests that a changeover to more energy-efficient appliances may not always be successful in reducing daily peak electrical demands on a household level. This in part shows that daily peak demands are more susceptible to aggregation of loads rather than to specific individual high-loads. In practice this means that, whereas the changeover to more energy-efficient appliances achieves reductions in electrical energy consumption, successful reduction or shifting of daily peak demands may have to be sought through different means. In this regard, measures such as demand side management or time-of-use tariffs may be more successful alternatives.Another important factor which is worth discussing is the impact the changeover to more efficient appliances will have on load duration profile characteristics. Figure 7 compares the load duration curves for the daily profiles of a characteristic day in May for three different households, between the present and future scenarios. The shallower duration curves of the future scenario indicate that there is an overall reduction in power demand and that for the same time interval the households’ instantaneous power demands tend to be lower in the future scenario compared to the present scenario.Fig. 7 - Duration curves for 3 different households for the present and future scenarios 5.2.2Household level – Yearly aggregationTable 6 summarises the annual electrical energy consumption calculated under the two different scenarios for all households investigated. The method used for calculating the annual energy consumption is similar to the one used in 5.1.2.Table 6 – Yearly total electrical energy consumption under the present and future scenariosHousehold codePresent scenario (2008) consumption (kWh)Future scenario (2020) consumption (kWh)Percentage reduction in 2020 over the 2008 valuePer capita (kWh/person) Present/Future1A1,37099527.41,370 / 9951B2,0791,64720.82,079 / 1,6472A1,14983327.5575 / 417 2B3,3592,60122.61,680 / 1,3013A4,0753,40916.31,358 / 1,1363B2,2191,83517.3740 / 6524A2,8272,10225.6707 / 5264B3,1752,40124.4794 / 600Considering the annual electrical energy consumption of all households due to the technology improvements envisaged by the future scenario it is clear how on annual basis the improvement in energy-efficiency is considerable with a total average reduction in consumption for all households of around 22%. In terms of the difference between appliance ownership it is also clear how the consumption of the households owning an electric water heater, households 1B, 2B and 3A, is considerably higher than that of the other households. In fact the energy consumption due to electric water heating accounts to 41%, 34% and 36% of the annual consumption of the three households, respectively. 6.VERIFICATION OF THE TRANSFORMATION APPROACHThe use of a sinusoidal function coupled with random ‘noise’ to describe the seasonal variation of an appliance’s hourly energy demand, used in the Stage 1 transformation has already been validated by Stokes ADDIN EN.CITE <EndNote><Cite><Author>Stokes</Author><Year>2004</Year><RecNum>111</RecNum><record><rec-number>111</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">111</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Melody Stokes</author></authors></contributors><titles><title>Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis</title><secondary-title>Institute of Energy and Sustainable Development</secondary-title></titles><volume>Doctor of Philosphy</volume><dates><year>2004</year><pub-dates><date>2004</date></pub-dates></dates><pub-location>Leicester, UK</pub-location><publisher>De Montfort University</publisher><work-type>Doctoral Thesis</work-type><urls></urls></record></Cite></EndNote>[22]. The verification of the approach techniques used in this particular research therefore revolves around ensuring that the original profiles created for the present scenario and the methodology used in creating them are comparable to the results conducted by other research studies. 6.1Comparison with other end-measurement campaigns and researchesThe annual electrical energy consumption, calculated using the output data modelled for the present scenario (shown in Table 6) yields results similar to those reported by another electrical consumption research conducted in Italy, the MICENE report ADDIN EN.CITE <EndNote><Cite><Author>eERG</Author><Year>2006</Year><RecNum>98</RecNum><record><rec-number>98</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">98</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>eERG</author></authors></contributors><titles><title>MICENE - MIsure Dei Consumi di ENergia Elettrica nel settore domestico</title><short-title>Micene</short-title></titles><dates><year>2006</year></dates><pub-location>Milano</pub-location><publisher>eERG - Politecnico Di Milano</publisher><work-type>Database Files &amp; Reports</work-type><urls><related-urls><url>;[46]. In the MICENE report, 80% of the measured households have an electrical energy consumption of between 2,000 and 5,000 kWh per annum. In this research analysis, 75% of the modelled annual electrical energy consumption values are within this range. 6.2Normalised Verification FactorOne method to compare modelled to measured data is the Normalised Verification Factor, NVF, suggested by both Capasso ADDIN EN.CITE <EndNote><Cite><Author>Capasso</Author><Year>1994</Year><RecNum>116</RecNum><record><rec-number>116</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">116</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>A. Capasso</author><author>Grattieri, W.</author><author>Lamedica, R.</author><author>Prudenzi, R.</author></authors></contributors><auth-address>Electrical Engineering Dept., University of Rome &quot;La Sapienza&quot; Via Eudossiana 18,00184 Rome, Italy</auth-address><titles><title>A Bottom-Up approach to residential load modelling</title><secondary-title>IEEE Transactions on Power Systems</secondary-title></titles><periodical><full-title>IEEE Transactions on Power Systems</full-title></periodical><pages>957-964</pages><volume>9</volume><number>2</number><dates><year>1994</year></dates><urls></urls></record></Cite></EndNote>[47] and Widen ADDIN EN.CITE <EndNote><Cite><Author>Widén</Author><Year>2009</Year><RecNum>129</RecNum><record><rec-number>129</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">129</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Widén, Joakim</author><author>Lundh, Magdalena</author><author>Vassileva, Iana</author><author>Dahlquist, Erik</author><author>Elleg?rd, Kajsa</author><author>W?ckelg?rd, Ewa</author></authors></contributors><titles><title>Constructing load profiles for household electricity and hot water from time-use data-Modelling approach and validation</title><secondary-title>Energy and Buildings</secondary-title></titles><periodical><full-title>Energy and Buildings</full-title></periodical><pages>753-768</pages><volume>41</volume><number>7</number><keywords><keyword>Load modelling</keyword><keyword>Time-use data</keyword><keyword>Household electricity</keyword><keyword>Hot water</keyword><keyword>Load profiles</keyword></keywords><dates><year>2009</year></dates><isbn>0378-7788</isbn><urls><related-urls><url>;[24]. The NVF is the squared sum of the error between the modelled (Emodelled) and the measured (Emeasured) data, normalised by the mean squared average of every time step, n. The smaller the resulting NVF value the more accurate is the predicted modelled data. The NVF can be calculated using the following equation (4): QUOTE - (4)Emeasured is the normalised original data used to calculate the parameters in equation (1), that is, the actual data derived from the REMODECE database for all appliances, whilst Emodelled is the output data after Stage 1 has been applied to Emeasured, that is, the data derived from the assumed sinusoidal trend equation used in Stage 1. Although the NVF should be used to compare two independent datasets, in this case the NVF is being used as a measure to quantify how closely the modelled data follows the measured data. Using equation (4) the hourly NVF, that is, the measure of how each single modelled hour value compares to the measure data on a 12 month period, was deduced. Table 7 shows the hourly NVF averaged over all 24 hours for all appliances, the maximum hourly NVF value for each appliance under investigation and the hour during which this maximum error value occurs.Table 7 – Hourly NVF for all appliances ApplianceHourly NVF averaged over all hoursMaximum Hourly NVF; (Hour occurring)Refrigerator0.1110.247; (23-24)Television1.6489.736; (4-5)Water Heater0.6772.548; (6-7)Lighting0.5071.066; (5-6)Electric Oven3.58018.917; (1-2)Microwave Oven3.13510.869; (0-1)Washing Machine10.77387.683; (5-6)Dish Washer2.75712.406; (5-6)Total General Consumption0.2820.648; (7-8)The low NVF values for refrigerators, lighting and electronic appliances and the high NVF values for washing machines, electric ovens and dish washers suggests that the methodology is most accurate for cyclic or relatively repetitive appliances rather than single-off event appliances, mainly due to the availability of data. Moreover, the results also indicate that the maximum error is during the night period, when hourly variations between the modelled and measured data are highest. This is comparable with results from Widen ADDIN EN.CITE <EndNote><Cite><Author>Widén</Author><Year>2009</Year><RecNum>129</RecNum><record><rec-number>129</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">129</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Widén, Joakim</author><author>Lundh, Magdalena</author><author>Vassileva, Iana</author><author>Dahlquist, Erik</author><author>Elleg?rd, Kajsa</author><author>W?ckelg?rd, Ewa</author></authors></contributors><titles><title>Constructing load profiles for household electricity and hot water from time-use data-Modelling approach and validation</title><secondary-title>Energy and Buildings</secondary-title></titles><periodical><full-title>Energy and Buildings</full-title></periodical><pages>753-768</pages><volume>41</volume><number>7</number><keywords><keyword>Load modelling</keyword><keyword>Time-use data</keyword><keyword>Household electricity</keyword><keyword>Hot water</keyword><keyword>Load profiles</keyword></keywords><dates><year>2009</year></dates><isbn>0378-7788</isbn><urls><related-urls><url>;[24].6.3Aggregated loads frequency distributionOne further method used to verify the profiles created was to compare the statistical frequency distribution of the aggregated load of all 8 households. Individual household domestic load profiles do not follow any specific frequency distribution ADDIN EN.CITE <EndNote><Cite><Author>Wright</Author><Year>2007</Year><RecNum>112</RecNum><record><rec-number>112</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">112</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Andrew Wright</author><author>Steven Firth </author></authors></contributors><auth-address>Institute of Energy and Sustainable Development, De Montfort University, Queens Building, The Gateway, Leicester LE1 9BH, UK</auth-address><titles><title>The nature of domestic electricity-loads and effects of time averaging on statistics and on-site generation calculations</title><secondary-title>Applied Energy</secondary-title></titles><periodical><full-title>Applied Energy</full-title></periodical><pages>389-403</pages><volume>84</volume><number>2007</number><keywords><keyword>Domestic, Enery, Electrical, Load, Generation</keyword></keywords><dates><year>2007</year></dates><urls></urls></record></Cite></EndNote>[17], however low voltage aggregated load profiles tend to follow skewed statistical distributions such as Weibull or Beta distributions ADDIN EN.CITE <EndNote><Cite><Author>Herman</Author><Year>1999</Year><RecNum>142</RecNum><record><rec-number>142</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">142</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ron Herman </author><author>Kritzinger, J. J. </author></authors></contributors><auth-address>Department of Electrical and Electronic Engineering, University of Stellenbosch, Banhoek Road, Stellenbosch 7600 (South Africa)</auth-address><titles><title>The statistical description of grouped domestic electrical load currents</title><secondary-title>Electric Power Systems Research </secondary-title></titles><periodical><full-title>Electric Power Systems Research</full-title></periodical><pages>43-48</pages><volume>27</volume><keywords><keyword>electrical load analysis, statistical modelling, distribution systems</keyword></keywords><dates><year>1999</year></dates><urls></urls></record></Cite><Cite><Author>Herman</Author><Year>2008</Year><RecNum>143</RecNum><record><rec-number>143</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">143</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="11">Ron Herman</style></author><author><style face="normal" font="default" size="11">Trevor Gaunt, C. </style></author></authors></contributors><auth-address>University of Cape Town, Rondebosch, Western Cape 7701, South Africa</auth-address><titles><title>A Practical Probabilistic Design Procedure for LV Residential Distribution Systems</title><secondary-title>IEEE Transactions on Power Delivery</secondary-title></titles><periodical><full-title>IEEE Transactions on Power Delivery</full-title></periodical><volume>23</volume><number>4</number><keywords><keyword>Load modeling, low-voltage (LV) distribution, probabilistic methods</keyword></keywords><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>[48, 49]. Figure 8 shows the fit obtained when the 3 Probability Density Functions (PDFs), namely Beta, Weibull and Gamma, suggested in literature ADDIN EN.CITE <EndNote><Cite><Author>Herman</Author><Year>1999</Year><RecNum>142</RecNum><record><rec-number>142</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">142</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ron Herman </author><author>Kritzinger, J. J. </author></authors></contributors><auth-address>Department of Electrical and Electronic Engineering, University of Stellenbosch, Banhoek Road, Stellenbosch 7600 (South Africa)</auth-address><titles><title>The statistical description of grouped domestic electrical load currents</title><secondary-title>Electric Power Systems Research </secondary-title></titles><periodical><full-title>Electric Power Systems Research</full-title></periodical><pages>43-48</pages><volume>27</volume><keywords><keyword>electrical load analysis, statistical modelling, distribution systems</keyword></keywords><dates><year>1999</year></dates><urls></urls></record></Cite><Cite><Author>Herman</Author><Year>2008</Year><RecNum>143</RecNum><record><rec-number>143</rec-number><foreign-keys><key app="EN" db-id="dw02w0wxraa0xtedvd3x90s6zzzw552zpt2r">143</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="11">Ron Herman</style></author><author><style face="normal" font="default" size="11">Trevor Gaunt, C. </style></author></authors></contributors><auth-address>University of Cape Town, Rondebosch, Western Cape 7701, South Africa</auth-address><titles><title>A Practical Probabilistic Design Procedure for LV Residential Distribution Systems</title><secondary-title>IEEE Transactions on Power Delivery</secondary-title></titles><periodical><full-title>IEEE Transactions on Power Delivery</full-title></periodical><volume>23</volume><number>4</number><keywords><keyword>Load modeling, low-voltage (LV) distribution, probabilistic methods</keyword></keywords><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>[48, 49] are superimposed on the demand frequency histogram created using the aggregated 1-minute resolution electrical demand of all eight households calculated for a characteristic day in February in the present scenario. The level of significance calculated using the Kolmogorof-Smirnov test exceeds the minimum threshold of 0.05. Values in this case are normalised using the highest daily occurring load. Fig. 8 - Demand frequency histogram with the probability density functions superimposed7.CONCLUSIONIn this paper a method has been presented whereby low-resolution electrical demand datasets can be used to create high-resolution demand data reflecting the effects of appliance energy-efficiency improvements in future years. The method makes use of a three stage transformation technique which first creates seasonal variations of individual monthly data, then converts the low-resolution hourly data into high-resolution minute data and finally projects the data into a future scenario reflecting improved appliance energy-efficiency. Results obtained using the transformation show that whereas a changeover to more energy-efficient appliances will have a beneficial effect on the electrical energy consumption of domestic households, with annual average reductions in electrical consumption of 23% for the households under investigation, such a measure may not necessarily be effective in reducing daily peak power demands of the individual households. In fact results suggest that such a measure is practically ineffective in shifting and levelling out daily peak demands given that such instantaneous demands are more susceptible to the sum of the different appliances which are active during any specific time rather than to single loads. Notwithstanding this, the results predict that although the maximum daily peak demand may be in fact higher with the proposed energy-efficient future scenario, in general the overall demand of individual households tends to be lower over the entire duration of a single day. ACKNOWLEDGMENTSThe research work disclosed in this publication is partially funded by the Strategic Educational Pathways Scholarship (Malta). The scholarship is part-financed by the European Union – European Social Fund (ESF) under Operational Programme II – Cohesion Policy 2007-2013, “Empowering People for More Jobs and a Better Quality of Life”.REFERENCES ADDIN EN.REFLIST [1]DG for Energy and Transport (2009). "EU energy and transport in figures" - European Commission. Brussels. Available from: [2]Almeida, A.d., et al. (2008). "Final Report REMODECE - Residential monitoring to Decrease Energy use and Carbon Emissions in Europe (IEEA Program Funded Project)" - University of Coimbra, Dep. of Electrical Engineering, Pólo II, 3030 290 Coimbra, Portugal Available from: [3]Bertoldi, P. and B. Atanasiu (2007). "Electricity Consumption and Efficiency Trends in the Enlarged European Union - Status Report 2006" - EUR 22753EN. Institute for Environment and Sustainability. 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