2018-05-30 08.04 EnPI 5.0 Demo_Webinar # 1 for Volvo Group



Colin: Requesting the webinar is Volvo Group Trucks' operations and our sites in Europe are now going to begin to do testing of the ENPI tool, as a part of an update we're making to our energy KPI to that model, the ENPI model, and this was a part of the initial training on the steps to use, version 5, and so that we can – ultimately, so that we can use this in our follow-up that we have quarterly with our management team and also for verification of projects within the WWF climate savers' program, annually.

Thank you, Sachin, for this opportunity today.

Sashin: Yes, good, absolutely. This is great, because energy performance indicator tools more and more, actually, we use it not only in the US, but other locations, also. I think we'll receive feedback from different places, and our goal is to improve ENPI functionality. Now, there are definitely two versions of the ENPI tool available on the DOE website.

And so, we'll talk about that. Again, this is Sashin _____, ______ National Lab. I'm actually technical manager for DOE's beta plants program, and I work with Total ____ manufacturing companies for this beta plants program, and Volvo Group is actually one of those companies, and Volvo Group is actually beta plant's challenge partner in DOE's beta plants program.

So, what we'll do is we'll provide you overview of DOE's ENPI tools. We'll actually demo how to use the ENPI tool, and also, we'll discuss whatever questions you may have, we will actually focus on your questions, also. ______ early start in 15 minutes.

And so, rough agenda for today's discussion on DOE's ENPI tools, is actually I'm showing it on my screen, so we'll – I have a few slides in the beginning, so to provide you overview on DOE's ENPI tools, and then actual demonstration, and as I'm demoing the ENPI tool, Excel version of the tool, I will try to answer some of these questions.

So, whether you want to benchmark, what data do you need to start benchmarking energy performance, how you basically process your data, formatting of your data, and then how you use ENPI tool different possible way. Actually, you can use ENPI tools to do your traditional energy intensity based analysis, as well as you can use ENPI tools to do regression-based analysis.

So, we will actually show you how to do that. And then, of course, _______ interface, entering data, critical minimum of data points needed to create record. What metric does the tool calculate? How long does it take to enter data? It is ____ reporting outputs available, whatever results we get.

How to basically use results to figure out what's going on at your facility. And then, of course, other considerations, do you need the internet to use the tool? Is there a particular size or type of facility for which the tool is best suited? What are the advantages and unique features of the tool, and then what additional technical assistance available for this tool, so we'll try to cover plus additional questions from your end. We'll also discuss those.

So, I think in regards to downloading this tool, it's very straightforward. If you actually go to Google, and if you search ENPI DOE, it will take you to this website, DOE website energy performance indicator tool, Department of Energy, and from here, you can actually download latest version of ENPI tools, version 5.0.

And it also provides you instructions and then we just uploaded ENPI tool, user menu, also online, so it tells you how to use this tool, step by step. So, let's say you have downloaded this tool, so what – once you install this tool, what you will see is in your Excel, whatever version you have, Excel 2016, 2017, whatever version you have, you see this add-in, ENPI add in on right next to help, you will see ENPI add-in.

And so, once you have that, that means you are good to go. There are a few issues people experience, the ENPI after installing ENPI tool, you didn't see ENPI tabs, so in that case, just send us an e-mail, and we can actually try to help you, or maybe even talk with your IT person, they may be able to help you with those issues, too.

So, let's go back to my slides, the few things I wanted to discuss before we actually go for ENPI demo. So, I think as you're using ENPI tool, there's one specific document that is available on DOE website. It is called Beta Plants Energy Intensity Baselining and Tracking Guidance Document.

That document is very helpful, very useful, because it explains how to conduct energy performance analysis on hardware label, facility label, and integration-based approach, using regression based approach.

Beta Plants program in the US, we allow manufacturers, use one of these approaches, and ENPI tool, Excel-based ENPI tool, allows you to do facility labels, traditional energy intensity based analysis, as well as regression based facility level analysis. So, these two approaches are actually built in this ENPI tool.

Corporate label is very crude approach for doing energy performance analysis. There are a few companies, they use corporate label approach, where they just use total energy consumption, annual total energy consumption, they would divide it by production, and then they conduct analysis that way. They calculate energy intensity on corporate label, and then –

We generally do not recommend companies use this corporate label approach. Facility label, it is traditional energy intensity based approach, and then regression based approach. Now, ENPI tool, generally, our beta plants program, we basically work with manufacturing companies, and we do analysis on facility level.

Let's say the facility one, plant two, plant three, the three different plants, what you do is you use ENPI tools on individual plant levels, and then you roll up everything on corporate level. That is actually typical use of ENPI tools. But what we realize, there are a few plants, there are a few manufacturing companies, what we saw, we can actually even use ENPI tools on individual levels, or individual process levels, individual equipment levels.

So, business level, unit level, process level, as long as you have your data, whatever energy going in, and then whatever product coming out, you can actually use this ENPI tool on individual process level, too. For example, paint shop, if you know, total natural gas, you are using, and ____ paint shop, as electricity consumption for specific paint shop.

And then, product going in, product coming out, and then whatever weather condition, ambient temperature, and as the moisture level. So, if you have that kind of data, you can actually do ENPI analysis, Energy Performance Analysis on individual process level, too.

And so, for example, iron and steel industry, we divided the large plants in separate subdivisions, and we conducted ENPI analysis on separate building level, or division level, and then we roll up everything on facility level. And then, once we have it on facility level, then multiple facilities, we roll up everything on corporate levels.

So, you can actually do that, using ENPI tools. That's what I'm showing in this graphic, too. Individual facilities, or subdivisions, and then you roll up everything on corporate level, and then gendered report on corporate level. Benefits of regression analysis, I think you provide aware to basically determine the true energy saving. We need to normalize electricity natural gas consumption or different variables, whatever variables driving natural gas electricity consumption in your facility, we should normalize energy numbers for those variables.

So, because what happens is production goes up, energy intensity actually goes down. If production goes down, energy intensity goes up, and then whether, also same thing, hitting the _____, putting ____, harsh winter, harsh summer, it impacts energy intensity numbers. Now, if you are implementing energy efficiency projects, and you want to calculate true energy performance, true energy saving, it is important for us to normalize, take care of weather fluctuations, take care of production fluctuation, and then use a basic integration based approach, and calculate true energy savings.

And this tool, ENPI tool, allows you to do that. And in terms of effort, actually, there's no significant difference between regression based approach, and facility level approach, because whatever data needed for conducting regression based approach is more ______ as facility level approach.

And then number of states is the same, so for example, on the left hand side, I'm showing regression based approach, whatever steps you need to take, and then facility level, energy intensity based approach. If you look at state one, two, three, four, almost same, and are define the boundary, choose the baseline year, determine _____ variables affecting energy consumption at each facility, gather data on energy consumption and delivering variables for each facility.

Now, once you have all these first steps completed, step five is different. Step five is you do regression analysis, you basically normalize, elect _____ that you guess _____ for different variables in step five, in regression based approach, and step five for energy intensity based approach is you just divide energy consumption by total production and then you calculate energy intensity.

And then, step six, seven, eight, again, same. Again, for more information, download – there's actually energy, if you go to that website, I'm going to share these slides with you, so this particular, on DOE website, this energy intensity baselining and tracking guidance document explains in detail each step, how it is done, and then it also explains facility level approach, corporate level approach, and then regression based approach in detail.

I do not want to spend a lot of time there. Now, let's actually go to ENPI version 5.2. So, what you do is you have installed, so let's say you have installed ENPI 2, Excel-based ENPI 2, and you are able to see ENPI tab that add in in your Excel, so step number one after this, after installing the ENPI tool, is actually gather your energy consumption data and your variables, whatever production hitting the ____, pulling the ____, whatever data you have, so this is the format we need.

It is very important to basically put together this data in this particular format, what I am showing on my screen. So, first column, always first column needs to be your date column, so ______ date, and then monthly, if you are using monthly data, so, whatever data you have.

In this case, I have data from January 2011, until December 2016. To run ENPI tool, we need minimum 24 data points, so 12 months and 12 months. So, 12 months for baseline year, and 12 months for reporting year. So, minimum two years work of data is needed to run ENPI tool.

If you want to actually use ENPI tool on weekly data, so you can actually use that, ENPI tool, you can actually use weekly data, and you can process weekly data to quantify your annual performance. If you have weekly data, that's fine. So, what happened is column date, so that is first column. You have month, different month on left hand side.

Then, first two columns in this case, and natural gas consumption. In this case, I have a natural gas consumption in MMB2, and electricity consumption in megawatt hours. So, whatever energy sources you have, you basically start with column B, C, and D, whatever.

Let's say you have – you're also consuming oil, number 4, number 6 oil, or coal. If you have multiple energy sources, you just keep adding different columns, and then enter monthly data. So, that is energy data, and then in this case, I also have water data, ____ cube, total water, monthly water consumption.

And because we consider water as a utility, so if you want to quantify water performance, actually you can use – although this tool is not designed for doing water analysis, water performance analysis, you can actually use this tool to do water performance analysis.

So, now, on right hand side, I have three columns, so in this case, I have three variables, driving, natural gas consumption, and electricity used in this particular facility. But if you have multiple variables, so let's say you have three different products, and then, hitting the ___, _______ data, and then you want to add moisture, rain, whatever different variables you think driving electricity and natural gas consumption in your facility, you just add different columns, and you need to make sure then you have monthly data for those different variables.

So, once you have data in this particular format, you are all set to use ENPI tool. If you are using ENPI tool first time, I would recommend, use ENPI wizard approach. There is actually a step by step approach. We call it ENPI step by step wizard approach. So, you go through all steps, and then you conduct analysis, you complete your analysis.

That is approach one. Approach two is once you are familiar with the ENPI tool, you can just jump around different steps, and you can conduct analysis. So, if you look at ENPI step by step wizard, if you click on that first step on left hand side, first step, on right hand side, you will see – let me actually put it down here, this go to meeting thing.

On right hand side, you will see ENPI step by step wizard menu or window on right hand side. If you want to actually increase size of this window, what you can do is you can drag this window to left hand side, and then now you are able to read everything. So, this is actually step one. ENPI step by step wizard.

So, it actually gives you some help message on right hand side here, and so you're all set to click next. So, you click next, and now, step one, enter your energy data and independent variables driving energy. And so, there are two options, my data is in the shape right here, or I need to enter my energy and variable dta.

I generally, what I do is I generally put together data in this particular format, and I always try to use this first box, my data is in the shape, because the second option, it's additional steps you need to take, so why – if you know this is a format we need, then why not do that? So, this is the format we need, so what you do is you click on my data is in the shape, and then once you do that, it takes you to step 1.1.

Format data as an Excel table. So, in this case, I already have Excel table. You can see these arrows, what that means is I already have all my data is in Excel table format. So, in this case, I don't need to click on format data as an Excel table. So, what it's saying is once all your data is entered in the shape, it must be formatted as an Excel table.

If your data is not already formatted in an Excel table, first select a cell in the middle of the table, and then select – you can read these instructions, and then proceed that way. In this case, my data is already in Excel table, so I don't need to worry about this step. So, I'm going to click Next.

Now, step two is Assign labels to your reporting tiers. So, reporting tiers, basically I have monthly data, and then my monthly data is from January to December, so I'm using calendar year as my reporting years. So, January to December. So, what you want to do is add one more column on right hand side here, and we are going to label, we are going to assign labels to your reporting periods.

Reporting periods is going to be 2011, 2012, 2013, 2014, 2015, 2016, like that. If you are using fiscal year as your reporting periods, so your data is going to start as July, for example, July to June, so what you do is then your reporting periods are going to be from July to June, and whatever label column will have reporting period column, we are going to call a fiscal year, then fiscal year one, fiscal year two, fiscal year three, like that.

And so, in this case, we are leading with calendar year, so calendar year, what we do is yes, I would like to add labels to my reporting periods, so I click on this button, Label Reporting Periods. Now, it will give you dropdown menu, so select the first data of the baseline year, and in my case, January 2011 is my baseline year.

I'm going to use January 2011 as my baseline year, so click First Month, January '11, and then my data is actually monthly data, it's not weekly, not daily data. I'm actually processing monthly data. I select monthly. Then, I, in this case, label calendar year. I'm not using fiscal year, I'm using calendar year. So, I select calendar year, and then click Label reporting period.

So, what it will do is after clicking Label Reporting Period, you will see now we added one more column on right hand side. It is called ____ Year. It is very important to call this column ____ Year, so column H, column B – date column and period column are very important. If you call this something else, ENPI tool will give you an error.

If you call first column, let's say, Month, it will give you issues. So, call first column date, call last column period, and you're all set. One more thing, actually, in whatever titles you are using, whatever column titles you are using, do not use special characters. If you look at your keyboard right now in front of you, there is computer keyboard, and numbers one, two, three, four, five, six.

So, there are special characters on top of those numbers, pound sign, dollar sign. So, those special characters, do not use those in this column title. For example, sometimes, you like to use, "I'm going to use pound." Do not use those special characters. If you use those, you will get an error message, so avoid using special characters.

Next, step three is energy data conversion. What we want to do is now from beta plants program in the US, all analysis is done in MMB2 source, or MMB2 primary, primary energy use. So, in this case, our natural gas numbers are _____ MMB2. And then, electricity numbers are in MWH.

So, and then, electricity numbers are actually site, so on-site electricity numbers. So, these are on-site electricity consumption numbers. So, what I need to do is I need to convert MWHI numbers into MMB2 source, or MMB2 primary. And so, to do that, I use convert units button, and so, I don't need to worry about natural gas. I just need to select electricity, and then it is purchased electricity.

So, for purchased electricity, site to source conversion factor is three, because when you consume one KWH electricity on site, on national, if we use national site to source conversion factor, at the remote power plant, you are consuming 3KWH of electricity, because ________, transmission, distribution losses. So, at the power plant, you are consuming almost 3KWH, on an average.

If you have region specific – you are regional site to source contributing factor, you can actually use that, too. Now, I'm going to select current energy unit, is MWH, so you purchased electricity, current energy unit, MWH, so convert to MMB2. I want to convert everything to MMB2, and then your conversion factor is right there.

Now, site to source is three. You can actually change this number. If you don't care about site to source, you can just put one, and then you should be all right. If you care about site to source conversion factor, you put three.

One more thing is, if your electricity is not purchased electricity, and if you are using on-site solar energy or green energy, real electricity, a new _____ electricity, for beta plants program, we have asked partners to use site to source conversion factor 1 rather than 3, because we give credit to beta plants partners if they're using renewal electricity.

And so, once you do this, click Convert, and then you see now one more column on right to electricity column. This is basically electricity, now I'm going to call it source. And then, I don't need to worry about MWH, because now, everything is converted to MMB2.

I have now natural gases in MMB2, electricity source is in MMB2. I have production and heating degrees, cooling degrees as independent parameters. So, that's it. I click Next. Now, step four, there are two options. One is your traditional energy intensity based approach, and then second one is regression based approach.

So, first, let's actually use a traditional energy intensity based approach. Use actual data. I'm going to click that, and step number five, select data for calculations. And so, energy sources, let's say if your facility is commercial building, not manufacturing site, in that case, you can actually use building ____ from this particular box.

But in this case, my facility is actually production facility, so I have production data. I'm going to ignore that second box, so first box, energy sources, I'm going to select natural gas, I'm going to select electricity source in MMB2. That's it, those two energy sources.

If you have multiple, you select all, whatever number oil, number four oil, number six oil, so whatever energy sources you have in MMB2, you select all those. And then, I'm going to select production. I'm going to ignore that second box, because this is production facility.

And then, third box, production, because as I'm using actual data, like energy intensity based analysis, I don't need to worry about hitting the _________ ________. Let's say you have multiple products, so product one, product two, product three, or you have monthly revenue, dollar valueship.

Whatever you need, you want to use in the denominator when you're calculating energy intensity number, you select that. In this case, total energy consumption divided by production, so I'm selecting production, and then baseline year is 2011, and click, Next.

And then, version 5.0, ENPI version 5.0, if you have energy cost data, along with natural gas column in MMB2, if you have natural gas dollar numbers, also, select the city dollar numbers also, monthly elected city bill, natural gas bill, you can actually do cost analysis, too.

In this case, we are going to ignore state 6, because we don't have dollar values, or monthly natural gas and electricity bills, so I'm going to just ignore. I'm going to click, Next. And then, this one is step seven, CO2 avoided emission calculations. Because if you are, let's say, because you implemented multiple energy efficiency projects, you are expecting energy savings, and you also want to quantify CO2 awarded emissions, because of your energy efficiency activities, you can actually use step seven to do that.

Step seven, so in this case, what I'm going to do is natural gas. I'm going to select natural gas energy source as natural gas right here, and CO2 emission factor for natural gas is this right here. We're using environmental production agency, DOE, basically US EPA data for a CO2 emission factors, for natural gas in the US is 53.06.

Kilogram of CO2 _______ for MMB2. Second one is electricity, so select the city, I'm going to select electricity, and then fuel type origin, this is use average, but if you know specific region in the US, and you want to use CO2 emission factors for that specific electricity in specific region, you can select that. But I'm going to use deport US average.

And CO2 emission factor for US average is 150.25 kilograms of CO___ _________ MMB2, so I click my calculator. Once I do that, I click calculate. And that's it. Now, the ENPI2 will run in the background. It will do energy intensity based analysis, actual data, and it will quantify your energy performance on facility level, and it will also calculate our CO2 emissions data.

So, this is energy intensity, and if you look at results, these are annual natural gas consumption numbers, electricity consumption numbers, this is totally MMB2, so total, primary energy consumption for this specific facility, and then you have total production numbers.

What you are doing, traditional approach, you just divide total energy consumption by production, and then you get energy intensity numbers in role 11, you have production energy intensity numbers, and then you quantify total implement in energy intensity, and then annual implement energy intensity.

Now, this specific facility, total implement in energy intensity -248.9, 49 percent. What that means is negative means it's bad, degradation of energy performance. And quality numbers means improvement in energy intensity numbers. Now, what I would like to do is I would like to go back and then click ENPI2 again, and then, I just use this actual data.

I use energy intensity based approach, and now I want to use a regression based approach. What I do is I go back to ENPI tool, and then click on use regression. Once I click on use regression based approach, you are back to that step by step result. And here, again, it's asking us to select energy sources, so other than natural gas, electricity numbers are selected.

Then, now second box, now it is saying variable. What variables you would like to consider, what variables driving electricity natural gas in your facility? And if you don't know, you just select all. In this case, I'm just going to select all, total production, hitting the due dates, put in the due dates.

Next box, so energy sources, variables, you think driving electricity, natural gas consumption, and then third box is baseline year. Again, I want to select, same, 2011 as baseline year. Now, one more box, you will see right up to baseline year is model year. So, if you select – what is model year? Model year is actually year you use to develop regressions for electricity and natural gas.

And there are three different options for you in terms of model year. You can actually just select your baseline year as model year. If you do that, we call it full cost meter, full cost regression meter. If you use last year, whatever last year reporting year as model year, then it becomes back cost meter.

And then if you use anything between baseline year and reporting year, any year between all these years, then it becomes chaining regression method. Generally, first time, when I do analysis, regression based analysis first time on any specific facility, I start with full cost.

And so, I see what kind of electricity natural gas equations I get. If I get good equations, electricity natural gas equations in terms of our square value, then B values, whatever statistical stunts we use, for selecting good equations, best equation. If I'm getting good R square value, like is R square better than .5.

And then individual parameter, individual variable B values, less than .1, and then more of the ____ B value is less than .2. If I'm actually satisfying, so _____ energy performance measurement and verification protocol, then I don't need to worry about chaining or back costs.

But generally, what happens is particularly if you are doing analysis long term, like 2011, 2017, so many years there. What happens is your baseline conditions are no longer valued for reporting year. So, this ENPI tool actually gives you statistical error, or indicate that no good equations are available, electricity and natural gas.

So, what you do is you try other methods, like back cost and chaining, and then see what method gives you best equation. And so, try full cost first, that means use baseline year as model year, and then see what kind of equations you get. If you're not getting good equations, and tool will show you that you are not getting good equations.

And if you are not getting good equations, then try back cost, and if back cost also is not giving you good equations, then try chaining. In all cases, if all different options, if you are not getting good equations, then in that case, try to basically look at your data, basically calendarization. We need to make sure energy bills match with production data, same days, basically production data and energy data is for same dates.

So, then it's trying to refine your data, make sure your data is in good format, formatting is done correctly. And if you are still struggling with equations, then what you do is then go for energy intensity based approach. Your approach one, rather than regression based approach.

If you are not getting good equations, you should not use regression based approach. So, let's actually use full cost for this one, and once you select model year as 2011, now reporting year, let's select 2016, that's my last reporting year, 2016, so I'm going to click Next.

I'm going to ignore energy cost data analysis, and then, again, CO2, our _____ emissions, let's select electricity, so this is natural gas, so I'm going to select natural gas, and then here, I'm going to select electricity, and then calculate.

Now, what tool is doing is tool is using production, hitting the ____, putting in _____'s data to normalize electricity, natural gas equations, basically electricity natural gas data. It's actually developing equations for electricity and natural gas, all possible equations for electricity and natural gas.

And then, also, tool is selecting best possible equation for electricity and natural gas. Generally, if you have three different independent parameters, in this case we have three, you get two _____ two, three minus one equations, two _____ two, and minus one equation, and subset regression analysis gives you two _____ two and minus one equation.

So, two ______ two minus one, you are going to get seven equations. Seven equations for natural gas, seven equations for electricity. If you have two variables, then two _______ two, minus one, so you are going to get three equations for electricity, three equations for natural gas.

Generally, tool complex analysis in fraction in a few seconds. Now, in this case, actually, what's happening is a lot of things are running on my computer in the background, so that's why things are a little bit slow. But just wait to complete the analysis. So, this is done. Now, if you look at right up to ENPI actual results, your first step is natural gas, second tab is electricity. So, all possible equations for natural gas, all possible equations for electricity. Then there is model data tab, this is where it's calculating model, calculates electricity numbers, calculates the natural gas.

And then, validation checks. As I mentioned, this tool actually checks validity of your baseline year, reporting year, 2011, 2017, is your baseline year data still valued, 2011 data still valued for 2016, for 2016 analysis. So, that validation check is done here. And you can see in model data tab a little bit more details on that.

And then, you have ENPI results, for beta plants program, we use ENPI results. And then, last one is for superior energy performance facilities. So, at CP, they use different ENPI for beta plants, we use different ENPI. But results are same, in terms of total implement energy intensity, CP or beta plants programs, same outcome.

Again, if you look at now using regression based analysis, it's showing total implement in energy intensity is -28.83. So, compared to traditional approach, in this case, it's -49 percent, and using regression based analysis, -8.8 percent. So, right now, in this case, it's better, but not necessarily every time you will get better numbers.

If you have good equations for natural gas electricity, you may actually end up getting more negative numbers or positive numbers, but whatever number is there, it's most likely through energy performance, because we think regression based approach gives you true performance.

Now, if you look at natural gas, it's showing equation for natural – if it is highlighted in green color, if any equation highlighted in green color, what that means is our square is above .5, and then, individual variable B values, these B values, are less than .1, and then model B value equation B value is less than .2.

In this case, there are total three equations that are true. Those are valued equations for natural gas. You can select any of these equations. If you take production and hitting the _______, that means winter space heating or process heating is driving natural gas consumption in your facility, you select equation one.

If you think no natural gas is only used for space heating, then in that case, you select equation two. So, a little bit, your plant experience, your intuition is also important. And so, one more thing I want to mention in this equation, for example, what you're saying is this _______ times production, plus another coefficient times heating _________.

And here, we have -2, roughly -8,000 MMB2s per month. So, if you have -2 coefficient, Y intercept, that means whatever last – not constant value. If it is negative, it is okay, because very rarely, you are going to run your plant, zero production, and then almost no hitting the due date.

And so, you are not going to use this equation at most zero production level or hitting the due date level. So, it is okay sometimes till you have -2 down here. Ideally, positive coefficient is good, because that's more realistic. But as you are using linear regression equations, it is possible sometimes, you may see negative Y intercepts.

But if you have negative production coefficients, so other than 6.7, imagine if it is -6.7, avoid those kind of equations, because it doesn't make sense. If you are using natural gas in your production, maybe pen shop or something. And if you have negative coefficient, what that means is production going up, natural gas going down. Doesn't make sense.

Same thing with heating degree days, now cooling degree days, if you have cooling degree days in your equation, for example, here, this is actually two models. It is actually value model, but you can see cooling degree days is negative. What that means is as cooling degree days go up, your natural gas consumption goes down, which makes sense, because more and more space cooling days are there, the less space heating days, more summer days rather than winter days, so that makes sense.

So, you need to actually think about all these equations, coefficient, negative terms when you are selecting equations, so that's it. This is a regression based analysis, so one more thing I want to show before we start taking questions. I'm going to save this file, for example. And I also want to show you corporate level analysis, and to do that, I need to quickly run one more analysis on plan 2.

So, if you have multiple plants, or you divide your plant in multiple divisions, or you basically, if you divide your plant in different production lines, that means if you have separate energy consumption, production data, for separate processes, different production lines, you can actually use this tool to do analysis on integer process level, integer facility level, and then you can dole up everything on either facility level or corporate level.

Now, in this case I'm just going to do – I'm not going to use step by step wizard approach, because I would have electricity, natural gas, ____ MMB2, and now, in this case, you can see I have heating degree days, cooling degree days, and then I have different products.

So, almost five different products, and so, I can just add multiple columns for different products. Now, what I'm going to do is use regression analysis, and then I'm going to select electricity and natural gas, and then variables are heating degree days, cooling degree days, and then I'm going to select – just to save time, I'm just going to select these two – actually, let's select all, just to show you an example.

Baseline year, I'm going to use FY 1, and then I basically am going to do forecasting, and then reporting year, FY 4, and then next. And then, electricity, natural gas. As this tool is running in the background, I also want to show you one more thing, and that is ENPI light. So, we were just discussing ENPI Excel version, but at the same time, DOE, we are developing this ENPI light online version of ENPI tool.

And online version of ENPI tool, it is actually, we are trying to make it simple for beginners or people, not familiar with regression based analysis, we are just trying to make things easier for them. Although, ENPI light is ready for facility level analysis, still, it is still not similar to ENPI Excel, because in ENPI Light, you only have forecast and back cast meter, there is no chaining meter right now.

But we are planning to add chaining meter, and then we are also planning to add corporate role of functionality. Right now, corporate role of functionality is not dead. There are a few things still not there, but right now, if you are interested in forecast and back cast method for doing facility level analysis, you can use this tool.

I'm just going to lower random data to basically do this analysis – let's not do that. Let's actually use load data cases. I'm just going to use case 2. It's actually same type of analysis, only this is online, and in most __________, so what I'm doing in this case is 20__ is baseline year, and 2012 is my reporting year for this specific facility.

If I do that, and I'm using forecast model, 12.6 percent performance, using primary data, and using side data is 13.9 percent. And it's also showing basically actual energy use, and then forecasted, calculated energy use. I can actually easily change baseline year. I can easily change reporting year.

Let's say I want to use 2011 as reporting year. I just click on that, and then now, performance in 2011, with respect to 2010, is 13.3 percent. This is online version of ENPI tool. To basically lower data, to basically bring data to ENPI light, you first need to use ENPI – sorry – you need to use DOE energy ______ tool. And it's not going to show, because it's actually running ENPI tool right now for this plant 2.

So, maybe, let me take a few questions as we are running this tool in the background. So, so far, any questions?

Male: ________ speaking, I heard about the variable, the input we are using should be independent variables, and cooling degree days and heating degree days are connected, they are not independent, or – ?

Sachin: Let me go back here and show sample data. For example, what your question is heating degree days and cooling degree days, they're basically, they are connected, they are correlated. Is that your question?

Male: Yeah, is it okay?

Sachin: Yes, but what's happening is when you actually calculating heating degree days and cooling degree days, what you are doing is you are selecting your balance point temperature. So, let's see 65 degrees Fahrenheit is my balance point temperature. And so, if temperature is above 65, the difference between those two temperatures, whatever, and _____ temperature minus balance point temperature, that difference actually has me in calculating cooling degree days.

If temperature is below 65, below balance point temperature, then that difference helps me in calculating heating degree days. So, if temperature is below balance point, then it is actually, it goes under heating degree days. If temperature is above 65, balance point temperature, then it goes under cooling degree days.

So, somehow, we are separating heating degree days, and cooling degree days, in specific year. So, for example, heating degree days, for example, in January to April, you are seeing some numbers here. But then cooling degree days, those are going to be almost zero, during those four months.

But then, shorter months, you are absolutely right. There are going to be a couple of months where you see both heating degree days and cooling degree days. But generally, we do not get into that column, that correlation is not strong correlation. But you are absolutely right, sometimes, we select variables where you see strong correlation.

In that case, you should avoid one specific variable, and just go with one, rather than both.

Male: Thank you.

Sachin: Now, yes, for second plant, also, now I have performance, and then now what I do is if I want to use corporate tool of function, what I do is create – I open another Excel file, separate Excel file, and then basically I save it, first I actually save it somewhere, and I'm just going to save it in my same folder, ENPI _____ folder, and then call it – what?

I just want to save it. Maybe I'm just going to save a text doc right now. Once you have a new Excel file, and you save it in the sample, I did the same folder. What you do is you click on corporate rollup right here, and then you click on Input Data from Other Files.

Now, I'm just going to select two plant files, click Open. And if you look at here, I have ENPI actual results, ENPI, so this, too, I'm going to select these two specific results for two different plants, and then I'm going to click Import Data. Now, I have two plants, one is Arlington Plant, second one is Baltimore Plant, and I click, Create Report.

Generally, within a couple of seconds, it should give you results. Somehow, my computer is slow. So, that's it. So, what tool is doing is it's taking plant level data, Arlington or Baltimore Plant, these two plants, then it's bringing their annual energy performance for different years, 2010 to 2017, and then it's rolling up everything on corporate level.

And when we are rolling up everything, it's actually ______ rated average, so this total implement, -210.7 percent, that total implement is rated average implement on corporate level. Now, the reason this is – if you look at this plant, for Baltimore Plant, it's 58 percent total implement. For Arlington Plant, it's -13.6 percent. And corporate level is -10.7 percent.

If we just take simple average of these two numbers, you will see different numbers. But as it's doing integrated average, because Baltimore Plant is really small plant, compared to Arlington Plant, and because of that, we are more negative rather than positive. So, that is it.

You use corporate rollup to roll up everything on corporate level. You can actually, if you have divided your plant in subdivisions or processes, production lines, you can do rollup on facility level, too, by rolling up everything, by taking multiple production lines or divisions, and the you can roll up everything on facility level.

So, that's it. I just wanted to cover all these topics. We have one more webinar on Friday. I'm sorry, it's 8:59, 9:00 here in the US, Eastern Time. If you have more questions, one, you can send me an e-mail, second, I will send you these slides, we have recorded today's webinar, and then we have one more webinar on Friday.

So, if you have questions, you can join that webinar, too. Colin?

Colin: Yes, great. Thank you very much, Sachin, for this, and then, yeah, if we have questions, you could also e-mail those questions to me, and I can sort of work them through so that Sachin doesn't get a lot of different – and that way, we can all see the answers, because that way, it's more grouped together. That might be a little better.

But yes, thank you Sachin, so much. I know you gotta run, 'cause you've got something right now at this time.

Sachin: Yes. Sorry about that, yes.

Colin: It's okay. Thank you.

Sachin: Thank you so much, Colin. Have a great day, bye.

Colin: Bye now.

[End of Audio]

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