Monica: Hi, everyone, and good afternoon. We're going to get started here in a few minutes, just to give folks a few extra minutes to join. Thank you.
Ron Townley: Good day, everyone. This is Ron Townley with the Upper Coastal Plain Council of Governments. We'll go ahead and get started. I'd like to start by having USDOE open the show with a couple of comments. Ookie, are you out there?
Ookie Ma: Yeah, but I think Monica's going to start with some housekeeping first.
Ron Townley: Okay. Sure. Thanks, Monica. You can go ahead.
Monica: All right, everybody. Your lines are muted. This webinar will be recorded and available later, but if you do have a question throughout the webinar, please enter it into the question panel, and they will be addressed at the end of the webinar. That's all. Thank you.
Ron Townley: Thank you, Monica.
Ookie Ma: Thanks, Monica. So hello, everyone, and welcome to the Cities Leading Through Energy Analysis and Planning, or Cities-LEAP webinar, from the Upper Coastal Plain Council of Governments in Northeastern North Carolina, on that Powering Energy Efficiency & Impacts Framework Project. I'm Ookie Ma, with the US Department of Energy, Office of Energy Efficiency and Renewable Energy, and I'm the Cities-LEAP project manager. Next slide, please.
So just to provide some context, the Cities-LEAP project partners with local governments across the US to improve their ability to leverage energy data and analysis for decision making and advancing their clean energy goals. The Upper Coastal Plain Council of Governments team is piloting one of three Cities-LEAP demonstration projects. The other two include the Bellevue, Washington team, comprised of five additional local government participants, and the Portland, Oregon team.
We also have provided technical assistance to 12 other local governments through our City Energy Data to Decision series. So please visit our home page or check our City Energy profiles.
So with that brief overview, I'd like to introduce Ron Townley, who is from the Upper Coastal Plains Council of Governments. He's the project lead, and will be the moderator for today's session.
Ron Townley: Thank you, Ookie, and good day, everyone. I'm glad folks have tuned in from across the nation. We have some great participants, looking at the registration list.
As mentioned, the presentation will be provided, a link at the end, and sent out in the email after the show to all registrants, so that you can log in through a link to see both presentation folders and notes and this actual presentation today.
Presenters today will include Daniel Pate. Daniel is an energy program specialist supporting custom energy solution programs that enable stakeholders to access the benefits of clean energy. He's with the North Carolina Sustainable Energy Association. We also have with us Al Ripley, consumer and housing project director with the North Carolina Justice Center. He's worked there since 2003, and his work includes policy development and advocacy at the North Carolina General Assembly, where he's worked on efforts to protect consumers and homeowners in a variety of ways. He's been a very, very busy man here in North Carolina the last couple of weeks.
We also have with us today Mark James. He is an assistant professor at the Vermont Law School. He is a senior research fellow at the Institute for Energy and Environment, and he focuses on data privacy, data access, and grid security.
We will also have Dr. Charlynne Smith, director of recreation resource services currently, at the North Carolina State University recreation, and has over 20 years of geospatial research and teaching experience. She's recently moved over to the recreation and resource services department, but she's been with us on this program for a year and a half, and so she'll be the lead presenter today, with assistance from Mr. Josh Randall, a PhD student in the Department of Parks and Rec and Tourism Management at the university, who focuses on social and environmental justice related to energy through GIS, as well as Mr. Bill Slocum, a research associate in the Center for Geospatial Analytics at the university, and he's been involved with geospatial research and teaching since 1993.
We'll also have a short slide from Mr. Daniel Kauffman. He's president and principal at ResiSpeak, a company that performs energy data analytics and visualization.
I'd also like to take this opportunity to just thank and recognize some key partners for us. The Town of Enfield, the City of Wilson, and the Roanoke Electric Membership Cooperative, for their partnership as data providers and instrumental to helping make this pilot project. Other data providers we'd like to thank and recognize include the North Carolina of Department of Environmental Quality, the Department of Health and Human Services, and I'm sure there are probably others that we've forgotten, but they are all important. So as you can see, we've had a large and fantastic team of people.
We've gone ahead and introduced our speakers, but throughout the rest of the presentation, we'll be introducing the region to you, we'll be introducing the tools and a number of things that we've moved forward on, so we'll continue with those introduction themes. We'll be talking and using – about how we're using the tool and building relations to address low income and energy burdens as a special North Carolina project, and then towards the end, Charlynne and her team will be going over the actual project tool, this geospatial analytical online tool that folks can have public access to through a simple registration process.
At the end of today's presentation, in about an hour, we will take about 20 to 30 minutes of questions and answers. So please, as we go along, do prepare your questions and answers. Feel free to type them in the box so that they're queued up for us to review.
I'd like to take just a couple of minutes to introduce the Upper Coastal Plain Council of Governments. We're one of 16 regional councils in the State of North Carolina. Most of the nation has regional councils of governments or lead regional organizations that represent multi-county areas. They were designed in the late sixties and seventies through – often through special state legislation.
We are also at Upper Coastal Plain Council of Governments designated an economic development district by the US Economic Development Administration. This allows us to create a comprehensive economic development strategy that includes planning and community development and energy efficiency, clean energy, sustainability, resilience, climate resilience, and other things that are part of those comprehensive economic development strategies.
Our region consists of 5 county governments and 41 municipal governments, and this project fits with those regional priorities.
Before you now is a map that shows our location in North Carolina. As you can see, the Upper Coastal Plain Council of Governments is located strategically in the northeastern part of the state. We are largely rural and agricultural. We have excellent farmlands. If you've eaten sweet potatoes, you've probably eaten our crops. But soy, cotton, peanuts, and many others things are grown in quantity here.
We have I95 and CSX rail lines running right through the heart of our region, from the Virginia border, about halfway through the state, and we are close to the Outer Banks and the Sounds, and we're about halfway between the Northeast and Miami, so we are a logistics hub. We are in an economic free trade zone internationally, and we have a number of large and affordable industrial sites within the region.
Here's a slide on just some of the background of the five counties. As you can see, we're a rural population. We have a fairly low per capita income, below the state's national average, but we have some aging housing stock, but we also have a number of housing units available throughout our rural region.
At this point, I'm going to hand it off to Daniel Pate, who'll be speaking a little bit more about why this project.
Daniel Pate: Thank you, Ron. Hi, everyone. My name is Daniel Pate. I'm from the North Carolina Sustainable Energy Association, and I'm going to talk a little bit about why we decided to pursue this project and choose the area that we did.
So this project addresses the need that there's a lot of data from these disparate agencies and entities, and that our idea was that these agencies could work more efficiently if they had a higher visibility of not only their own data, but of other agency's data as well, and I'll note that a lot of this data serves the same residents.
So by putting all this data in one place, in this tool that's intuitive and it's visual, it'll allow all these different agencies to basically have more impact on the services that they provide. Specifically, we felt that these agencies could pursue best practices by identifying which homes were in the highest need, and then the agencies would also be able to see the history of what's been done to the homes.
Having all this information in one place also saves from the agencies having to collect multiple types of information, such as utility data. To give you an example, one of the connections we focused on in this project was between the utility and the state weatherization program. So the state weatherization program would need to view the utility bills of potential applicants, and so this tool would prevent the weatherization program from having to go to the utility and collect this data. They could, instead of taking on that burden, just use the tool to view that data. And this also helps promote the accuracy of the available information by preventing all this distribution of information among the agencies.
Also, with this tool being geospatial, the data is presented in map form, where it's easier to analyze. And then the agencies would also be able to toggle on and off the different data layers, based on their preference, and this will be demonstrated later in the presentation.
So why this region? Well, since this was a proof of concept, one thing we wanted to do was to get something running quickly, so we decided to start with the relationships that we already had. And also, we wanted to focus on electric cooperatives as opposed to investor owned utility area, because we knew there would be fewer barriers to getting the data. We also wanted an area that had a presence of quality energy efficiency programs. One of the utilities in this territory actually had a tariff on bill financing energy efficiency programs. And so we were able to kind of use this program as a prototype for opportunities to increase program efficacy through the database tool that we'll show you later.
There were also several partners involved with this project who had a good relationship with the Upper Coastal Plain Council of Governments. Also, we can speak – from NCSEA, we knew that the Upper Coastal Plain Council of Governments and Ron had really solid relationships with local governments in that territory, so that was a sign right there that we knew that this would be a really strong collaboration.
Also, like Ron touched on earlier, these rural areas are needy areas, and also, rural areas tend to struggle more compared to urban areas with identifying technological solutions to program challenges.
This slide right here gives you an idea of the different data types that we worked with, and the database, and then you'll see on this slide, this is more of a geospatial breakdown of the different data layers used in this tool. And now to give you – and also I'll note that the NC State team will be demonstrating the use of this tool in a little bit.
But now to give you more information on our user groups and our beneficiaries, I'll have Al Ripley from the North Carolina Justice Center speak next.
Al Ripley: Thank you, Daniel, and good afternoon, everyone. I want to take just a minute to talk about the practical impacts of having the ability to look at this data and use it and correlate it in different ways. And one way to put this in context is to talk about some of the local communities in this five county area.
We've had meetings with local Department of Social Services, for example, and talked to them about the programs that they administer, crisis intervention programs or CIP, LIEAP programs, low income energy assistance programs, and the acronym there is LIEAP, as you can see. And they administer these program, but very often, they concede to us that they don't have a lot of data on how these programs are performing and how they interrelate with each other.
For example, part of what we've been working on is taking census track data that shows levels of poverty in the region, and then comparing that to those that have received CIP and LIEAP assistance, and looking at penetration rates to determine where these programs might be improved, where greater marketing efforts could be deployed to help with penetration rates of these programs, and to help these entities better administer the programs that they have.
Another aspect of this as well is interagency program delivery, and how they can interact with each other. So we hope that eventually, once we have all the data that we need, and that it's working appropriately, that we would be able, for example, to do analysis to show connections between WAP or weatherization assistance program data and LIEAP and CIP data.
For example, we hope to be able to see correlations between those individuals that receive weatherization program assistance for their homes and their reliance on CIP or LIEAP assistance as well, and be able to establish those. This is not just a individual department program, if you will, but it's inter-department, inter-program analysis that can help deploy these efforts in better ways.
In talking with these local communities, we also realized some very interesting dynamics, that there's differences between recipients of assistance that own their homes and live in their homes, and those, for example, that are renters, and might move more often. And so we've learned how we need not only to track the properties that are actually getting structural improvements through WAP, for example, but also the individuals who are receiving that assistance, because they might live in different structures over the period of time that's being examined, and it's important to take that into account.
So those are just some very brief examples, and of course, I'd be happy to give more information about that, if there are any questions.
Another area to look at on this next slide is the fact that there's also the need to ensure that the state agencies that administer these programs are also talking to each other and sharing data. In North Carolina, the Department of Environmental Quality, or DEQ, administers the WAP and HARRP programs, but it's the North Carolina Department of Health and Human Services, DHHS, that administers the LIEAP and CIP programs. So it's very important that those entities share data and talk to each other about how their programs are being run and what that data can mean to them. We're in the process of securing agreements between those agencies so that they can freely share that data and have those conversations.
And then, of course, another layer that is very critical in all of this is the utility data, and being able to look at utility performance data from individual homes and individuals to see how these programs are working or not working or could be improved. So taken together, there's a lot of value in being able to have this platform, to be able to share data in this way, that impacts not only local communities and how they can utilize that information, but also state agencies, and how we can try to make these programs function better. Thank you.
Mark James: Thank you, Al.
Ron Townley: There you are, Mark. _____.
[Crosstalk]
Mark James: Good afternoon, everyone. I'm Mark James. I'm an assistant professor at Vermont Law School. And I just want to thank Ron, Daniel, and Al, have done a great job setting up sort of the concept and the proof of concept and the potential range of beneficiaries that would be able to use the PEEIF tool, either in assessing and evaluating their own information, or improving collaboration within their agencies or between their agencies.
So my – the section of the presentation I'm going to talk about is the processes that we went through in order to secure data and begin to populate the PEEIF tool with the information that would then allow it to deliver all the range of benefits that we've been talking about in the last 15 minutes. And as with any project where you move from a concept into reality, you always begin with a set of assumptions and beliefs that will either speed up your process or slow down your process, and we were no different than any other project, and we had come in thinking that the utilities would be reluctant to share their data, and we learned that by leaning on the relationships that Daniel and Al had talked about, that we were able to secure information from a number of the co-ops and municipal utilities in the area.
Another major assumption, we assumed that everyone had collected their data in the same manner, and therefore, it would be clean and easy to use, and we quickly learned that pulling together data from different data sets was an issue, because there was not always over-matching information, especially when transferring between utility consumption data and program data collected from a state agency.
We had a third assumption, with sort of thinking that the utilities would use the data to inform their programs, and that's been an area that we had to continue to work on and think about how do we provide benefits to all the potential users of the tool, and this is an area that we're continuing to work on and think about how utilities could use this information and use the tool to their benefit.
And the final one, and probably the most important one, which I'm going to spend the rest of my time talking about, is the legal aspects, that once the utilities and agencies share their data and the data was uploaded into the PEEIF database, that there wouldn't be any legal restrictions. And we've continued to deal with limitations and conditions placed upon data use that have been coming from different pieces of legislation and regulation. So going forward, there is a need for constant evaluation of how data is being protected and managed so that it can be – continue to be in compliance with the directives handed down by the various data-sharing parties. Next slide, please.
And so I'm going to focus on just some of the legal assumptions. That the data would be easy to secure, and that we could skip over our business arguments and just avoid having to do any – making our strong policy arguments. These two are going to – these two combined. And that in going forward we had an understanding or a knowledge that there was data being collected. We could clearly see that within the North Carolina general statutes or the North Carolina administrative code, that there could be a process for getting access to that data.
We had begun to formulate the legal arguments, and then needed to sort of reset and make a strong policy argument, some of the points that Al had picked up on, and present it, that there needed to be a demonstration of benefits before data – taking on a new project and a new concept, that you need to demonstrate how participation will assist that agency or that entity. And once those arguments were formed and made, we could then begin the process of getting into the legal arguments and the structures that we needed to create to make the database function.
And the last point here, just the standardized approach for securing data access and facilitating data sharing, thinking that just operating within a single state, within a five county region of a single state, that there would be uniformity in legislation and regulation, and that was – that is not the case. There are – when funding is coming down from the federal government, that there are regulations that need to be complied with by the state agency that's receiving it, and they're going to develop their own set of regulations. The regulations are often coming down, developed from legislation that was passed in the 1970s. It's been amended, it's been _____ put in place that – often, trying to access data from an agency, you run into regulations that were developed for a different purpose. For example, Human and Health Services has invested considerable resources into developing data sharing protection, data sharing regulation and protections around health information. So what you run into is a complex matrix of interacting regulations and legislation. We'll go to the next slide.
After we were able to demonstrate the business value, we moved on to dealing with the different legal departments, either parties who are the utilities, often outside general counsel, or a general counsel in the state agencies, and that began – that was where we began the process of fulfilling all of the legal requirements to gain access to the data, and ensuring that the PEEIF data collection system met the standards that were set out by the different state agencies.
And as I mentioned here before, this is just an example of how restrictions on data sharing can affect information that is shared. It can also limit what information is available for both the Department of Environmental Quality and Department of Human Health Services, that their programs are collecting confidential information. For clients who are seeking services, they're often required to provide different types of financial information. There are limitations placed upon that. So it affects how data can be shared and how data can be used.
We also ran into a situation with the North Carolina Housing Finance Agency that had anticipated that they would be able to participate in the program, because they do have a number of programs serving low income individuals and focused around energy efficiency, but federal banking laws prevented them from participating, as they serve to provide mortgages, which then run to – adds in additional data sharing protection and additional consent requirements, which in the end prevented them from providing information to the database. Next slide, please.
And this is just the last sort of summary, some of the legal issues that had been identified earlier on, understanding how data moves, and that's the first point here, is the nature of the relationship between state agencies and their program implementers. Once we were able to map out how data flows from point of contact with a client, either a client who is applying or a client who has received services, and how that data moves up from that point of contact through a local program or a local county agency or community action agency up to the state agency that's providing the funding, in mapping that out, we were able to then design a data protection system that reflected the true conditions of how data was moving.
And importantly, here, to note that we've been – the participating utilities that we work with are not governed by the North Carolina Utilities Commission, which is tasked with overseeing investor-owned utilities. So there was a different set of rules that we needed to consider. Often, for electric co-ops who were incorporated in the late 1930s and still maintain the same articles of incorporation, or municipal-owned utilities who are creatures of state statute that we needed to go back even farther into the legislative record to determine how that utility could comply with its obligation to serve its customers, and at the same point in time, gaining access to a new tool that might allow them to tap into additional benefits.
And lastly, of course, once you go through all that process of working on those individual issues, you think about how it fits in with a larger statewide mandate, like the North Carolina Statewide Information Security Manual, which sets out rules, which really help shape how we set up the security protocols for the database, and that's something that Charlotte and her team will talk about in their presentation.
And then my final point here is after identifying all of the assumptions and the issues, that we moved to solutions, so how do we get data into the database? The first one obviously is creating a single data collection point, having a portal, restricting access, and that immediately begins to narrow the potential range of risk. We created non-disclosure agreements for the data sharing parties and anyone accessing the data. We worked to identify what was confidential information, and then developed data aggregation and data blurring techniques that would turn that into non-confidential information. And you'll see the difference between that in the upcoming presentation.
Then we had flexibility in how we developed those nondisclosure agreements to reflect some of the individual characteristics that I talked about on the previous slides.
And the last two parts here, the short term and long term thinking about immediately getting data into the databases, _____ trying to continue to reduce barriers and develop systems that would expand our data collection. And we've been working with the Department of Environmental Quality and the Department of Human Health Services in the state to use existing regulations that would allow them to – allow the Department of Environmental Quality to see clients who are receiving services, human health services.
So those regulations were put into place. They haven't really been used. We're now assisting in that process, as we access into existing regulations. And long term, working consent and getting the customer to sign off on it.
So that is my part, and I will now turn it over to Al – sorry, to Ron, and then to Charlynne. Thank you.
Ron Townley: Okay. Thank you very much, Mark. And as you can see, folks, we spent a few minutes on the legal side of this, because in our initial program of work, we thought that the list would probably take, three four months, and we were still working heavily on some of these data sharing agreements a year into this project. And so for those of you out there across the nation looking at how to share data when you're doing an overlaying public data, private data, confidential data, utility data, different things like that, it will get into the weeds on you. And so do make sure that you have resources available, like Mark, and other finances, and the time to try and attack it, because it will take a little longer than you might expect.
I see we've transitioned our slides over to the online ESRI product now that NC State University will be talking about. But as we get started, I also wanted – and looking at the logos up there, give another shout out, special shout out, to the North Carolina Clean Energy Technology Center. Anne Tazewell has been instrumental in moving this project forward, visionary from the very beginning, as well as Laura Langham with the North Carolina Sustainable Energy Association, who's been – many years of experience in research and policy and financing, and just all kinds of areas in the clean energy realm. And the Sustainable Energy Association of course is a great asset to North Carolina and even the nation, why we are leaders in this country in clean energy work.
With that, go ahead, Charlynne, and knock our socks off.
Charlynne Smith: Okay. All right.
Ron Townley: _____.
Charlynne Smith: Oh, okay. Yeah. So this is just a – a slide – this is one of our first versions of the tool that's available, that we had set up in a story map format, just so we can tell you a little bit. This is a way we could tell folks about the program, about this project, and all the partners involved. So say when Ron goes out to visit local communities, speak to local governments, or Al is meeting with folks, this is one way that we can do it.
So I'll talk about some of the different aspects of this, and as our team, as Ron said earlier, Josh Randall is a doctoral student working on this project, Bill Slocum, research associate with our Center for Geospatial Analytics, and so there may be times that we just chip in and do this together, so we may answer questions, because we've certainly done this as a team.
So on the storyboard – and we'll see that there's sometimes – one thing that we found with this, so the image in the center, some people, it might be cut off, so one of the web aspects of it is that you have to change your zoom level, depending on your browser settings. So that's not something we can set and it push out, but certainly let people know that as we go through this.
One of the things we talked about was wanting to know who accesses the tool. Currently, this isn't totally open to the public. You have to contact us, and we set up a user account for you, so you can sign in and get access, and that's really as we're working through this data blurring, data aggregation that Mark talked about, so we can make sure that you only have access to data that you're allowed or permitted to see. We can certainly – with this tool, we can certainly embed a sign-in sheet that we collect data on the back end, get your contact information, organization, and what your interest is in using the tool.
So to talk about the project overview, you've heard much of it already. Again, these are our collaborative efforts in data sharing, so these are a lot of the partners that we worked with. We've talked about some of the data providers, but we're also looking to data – publicly available data that's out there. The US Census Bureau is certainly – NC1 map is the geospatial portal for North Carolina, and we'll talk about some of the data we were able to get from there.
The real push, again, just kind of reviewing, is the current energy efficiency services situation looks at data from – you can see the resident in the center, so the DEQ, weatherization assistance, or other assistance for energy efficiency, from DHHS, goes directly to the resident, and then the resident also deals directly with the utility to pay bills and potentially access other energy efficiency programs.
The ideal situation that we're looking for is how can we improve sharing of information to make everything more efficient and more effective, and directly impact the residents? Those are the goals. So adding the geospatial component also allows us to elevate that ability to analyze and understand the data and how residents are being impacted by these.
As we did this, data security framework was certainly part from the very beginning of how we set this up and how we considered it. So this is a diagram that we pulled together I think for our application to the Department of Energy, just to give a understanding. So anywhere you see these red lines, these are firewalls. So those are areas that are protected that allow for secure transfer of data. In this case, on the left, you have the utility can use a secure file transfer protocol, FTP service, to send us the data, so only a select few people have access to the data. Those were all set up. That's part of that legal framework that Mark was alluding to. So the State of North Carolina – so the Department of Health and Human Services, before they would allow us – or agree to share data with us, we had to demonstrate that our servers and FTP systems at the university met their minimum standards for data security. That is something we established in the web portal.
Basically, from that we pulled the data. The data team pulled the data together to create an enterprise database using tabular data, with the goal to make it spatial, which we'll talk about in a moment. From that, we can use desktop GIS to do modeling and analysis, and then create web services. So those are things, layer _____ that we can push out to the – through the internet through a web mapping applications.
The users in this final section here will have access to the web map portal, be able to see their own data or data that they have permission to view confidential information in more detail, but there's these blended maps here represented in this bottom part of the image of data that we can see, that we're able to share without providing personally identifiable information.
The other thing that we kind of put together from the start of this, or – what are the current challenges and what are the benefits? So the entire team here worked on this, worked with our data partners, and just looked at what our goals were. And then our goal on the data team here was how to map that, because as we said many times, we'll map just about anything. Most data has a geographic component, and sure, we can put it on the map, but we want to make sure that the information is useful, the attributes, the data that's provided and information that we push out and represent on those maps is useful for decision making.
So part of the process in making it spatial, if you look at the kind of bottom of this diagram here, how do we locate it? So you can geo-code an address, like you do when you use your phone, pull up an address or a location, and it puts it on a street. But we needed to be able to pull data, so we did a lot of data exploration.
So tax parcel information became our base layer for locating. This is where we're going to match everything to the tax parcel. One reason for that is first, North Carolina has a statewide parcel system, so we have 100 counties in the state, and we can get data for every county. The ability to scale this out beyond the region, we thought that was certainly a benefit for this.
From tax parcels, the reason we use that is you can typically get square footage of a home, the year built, and the structure type, and the structure type would be single family residential, mobile home – now that's not – we don't necessarily have that for every county. Some counties have a lot more data in their tax parcels than other counties. This was something – at least a starting point.
The goal was to then get the energy meter data attached to the parcel location and program participants, so everything was tied to the parcel. Does the parcel have meter data? Does it have program participation? And then we could start to compare this and put in socio-demographic data.
So here's an example of a web application that's loading on the screen here, and this is one that we have embedded in the story map, but this could be totally separate. You could go directly to this web mapping application. In the long term, each portal information will just – might not have the story map, but would have this embedded web map application that you see here.
In this case, this is a way to look back over a lot of the information that Ron already shared about the Upper Coastal Plain Region. In this case, we're using census data. You can see the concentration of total number of households, where they're located in the region, around Rocky Mount, Wilson. But we can also sort of color code it in different ways. Say we want to see where are the areas that have higher number of population over 65, if that was our concern or our need.
These are displayed by census block group. So you have census tracts is the largest kind of geographic area, census block groups, and then census blocks. Block group is the level of most detail of socio-demographic information that you can get. Census block – individual block, at that point, you're able to individual homes. So this is the area of geographic interest that we're looking at.
You can see with this we have information pulled from the census data, so you can get actual – the total population in this block group, median age, poverty status, and median household income. And we'll talk about that a little more in a moment.
Again, we can put information on the Upper Coastal Plain Region. Again, estimated population of just over 300,000 residents, with a per capita income of $35,000.00, and 23 percent of residents earning an income below the federal poverty level. Another key component that Ron had mentioned is 65 percent of the total housing stock was built before 1990. That's other data we can start to look at.
This just shows a little more of the data that we use from the American Community Survey. These are the 2016 estimates. Again, you can pull up information on each individual county, and that's some of the data we looked at earlier.
If you look at this map to the left over here, you have the State of North Carolina. You can see the region in red. This county just to the west here is this – where our state capital Raleigh is. So Nash County in this example is probably the closest to the state capital, so a little difference as you go through the region on the characteristics of each county.
Working with the team, income and poverty status, and certainly talking with the Department of Health and Human Services and DEQ and the utility providers, this is information that they wanted to know. In this case, we're able to do a web mapping application that shows four characteristics at one time. So we have median income – again, as you zoom in, you can see, you can click on a census block and pull up detailed information about that block group, and then compare it to total households in poverty, poverty status at or below 150 percent, and poverty status at or below 200 percent. Did you want to – Al or Josh, on either of those, or –
Josh Randall: Yeah, I think that – so one of the things as we were going through this project and talking with different partners is trying to figure out what information would be best to present in a way that's useful, even this publicly available information. And so a lot of these programs are – have these cutoffs for poverty status in order to qualify for different programs.
So 150 percent and 200 percent are just levels above what the basic income level would be for poverty. So essentially, 200 percent would be twice as much as the poverty level, which still isn't that – necessarily that high, but this is how you qualify for certain things, like having – being able to weatherize a house for WAP information, or being able to qualify for income assistance.
Laura Langham: And I just want to add one thing. This is Laura Langham from NCSEA. One of the really rich experiences with building this was we had this idea, we got together, and we laid out what we thought the data was that needed to be developed, and having these sharing mechanisms. And then we went out and started meeting with partners. And it was such an interesting experience, because we were trying to teach them about the tool, and at the same time, we needed to learn from them what they needed.
So we really got to dive in deep with them in looking at the processes for how they were collecting data, what data they needed, how they needed to be able to access it, to view it, to use it, what data they got from residents versus what data they would get, for example, from a utility or from another service provider.
So it was a really deep learning experience for us, and we're still learning, and I think it'll never stop. But that has been one of the things that was a big aha for me. I thought that we were 80 percent there before we went out, and that we were going to fill in the last 20 percent. And I should have known. It was the reverse. We had maybe 20 percent figured out of what they needed us to be able to do with this tool, and so it was us getting them up to speed enough that they could then, yeah, teach us what we needed to know.
Charlynne Smith: Yeah, thanks, Laura. I mean, it's true. It's been a back and forth on this, which has really made a better tool in the end.
So the next thing we have here is rental households, and this is just another way to display it. And one of the other things we tested is methods to display information, and getting feedback on that.
So in this case, this is the – looking at the percentage of rental households, because that makes a difference on qualifying for energy efficiency. Do we promote these programs to landlords? How do we work with this?
On this one, we can look at the Wilson area here. The way this is displayed, this is, again, census block groups. Each one has a circle. The circle represents the total number of households, so the larger the circle, the greater number of households in that particular census block group. And then the colors are the percentage of rental households, so it goes from white color to blue. So if it's bluer, it is a higher – so this is a higher number of total households and a higher percentage of households. And we can click on that to pull up the data with that. So total households, 2,353, 1,200, or 54 percent, are rental households in that census block group.
When we look at the energy assistance programs, again, we have the riders from Department of Environmental Quality, Health and Human services, and when we also had some data from the Roanoke Electric Cooperative Upgrade to Save Program.
So with environmental quality, our goal was to look at the census block group area. However, there weren't enough homes in the individual census block groups for us to be able to display it at that level. For example, there may be only two homes in a census block group, so then that can reveal who those recipients are, revealing that. So we've broken it down simply by county for that, so Halifax County has 78, Edgecombe 63, and we can look at a summary of that information. So the Weatherization Assistance Program, 63 total jobs, and there may be multiple jobs at one home.
We also – they collected data on blower readings, pre and post intervention, for the measures, so what the total measure costs were, so $1,300.00 here, material costs, and this is for the whole county, the total materials cost was $105,000.00. So you can see what's being contributed to a county in this particular case.
Now for environmental quality, it would be specifically – they would be able to see the individual homes and pull up the data affiliated with that, and they would also be able to pull up the exact materials that were used, the costs, and who the contractor was. So that's something that they saw as a benefit. And we can't share the confidential information here, but on their access to it, they would be able to see that information and look at it, and potentially identify which contractors have better outcomes, is one goal I think that we discussed.
Laura Langham: And I'll see to that, so if they had agreement with the area utility company in this case, then not only would they be able to look at each of those individual homes and see the data that they provided about the different measures that were done on each home, but they would also know the pre and post-retrofit energy usage through the utility bills for those homes.
So all of a sudden they don't need to go to the homeowner and get utilities even for an application, but also for identifying pre and post-retrofit analysis. They would all of that in this tool.
Charlynne Smith: Mm-hmm. So the next one, Department of Health and Human Services, we're looking at the Low Income Energy Assistance Program, LIEAP, depends on who you're talking to, I hear different versions of the acronym. So this is looking at funding by census block group. So again, we were able – we had enough data that we could look at it by census block group and provide the total for each one. So the darker ones have received more assistance, in this case, for the data that we have. So again, this is the non-confidential data. You can pull up the summary. There were 99 cases. And this one, they collect a lot more information on the residents who lived in the home. So 196 household members, average household size was 2, and the maximum household size in this area was 7.
Again, we can also look at total benefits. The average benefit received, minimum, and maximum, were the general statistics that we could provide.
We can also start to look at – we'll get to the energy data, but let's look at – we can zoom into the Town of Enfield, and as we zoom in, the roads will disappear, so you cannot see individual homes, again, to protect confidentiality. But we can look at energy use. The streets will disappear. Mark, I'm just letting you know.
[Laughter]
Charlynne Smith: So we have these represented not by – you cannot see the exact use of a home, but divided through – within a confidential _____, they ran the analysis, and this is part of the aggregating data, blurring the data by decile. So for each provider – in this case, the Town of Enfield's utility provider – is divided into ten equal groups of the total, and the higher energy users are in the greater than 90 percent, or the darker red areas, all the way down to green for lower energy use homes.
So the spatial aspect of it allows you to see maybe where there are patterns of use, and if we click on one, for example, we can see total usage, so this particular home is in the 70th percentile, and then the range within that decile, so it's from 11,000 kilowatt hours per year to the maximum use in that decile is 13,000 – just over 13,000 kilowatts per year.
Energy use intensity, that looks at the kilowatt hours per square foot per year, and we're able to see the – again, the rank for this home in that decile, and a minimum and maximum in that range.
Then we also wanted to include home characteristics. Now this is data that comes most often from the tax parcel data, and Halifax County has – they have great information in their tax parcel data that's available through their GIS. So again, this home was built in 1920. There's seven rooms, two baths. For some areas, we actually have the primary heating fuel and the home type, again, which was either like single family home, double wide, single wide mobile home.
So we're starting to be able to overlay data. This is a non-confidential information of energy that DHHS, a program provider, can view, and compare with their information, what's going on.
Crisis Intervention Program is another assistance program that we looked at and displayed, and I think the first go round, I had these together, and it's like, no, we need to look at those two programs separately. I think one's tied to federal funding. And they needed to be able to look at those separately.
So if we zoom in to the Wilson area, we can see, again, total – this one I just have displayed is total number of cases in a census block group. And in areas where you don't see anything, a color, that means there were no cases in that area.
In this case, you can see this block group there are – you can get a summary of assistance provided. But when we turn on the energy information, one thing that we noticed was – you say, oh, we didn't have any cases in this area, but then when we pull over the utility data, which they didn't have access to, you can see there are quite a few homes in that area, and some that have higher energy usage than others.
So this goes to the penetration rate that Al talked about. Now some of those we don't necessarily – we weren't able to map all of the data. This goes back to the condition of the data when we received it, what was – DHHS data was probably over 30,000 records, at least, for the years that we had it, and providing that data to map it, to geo-code it, if addresses are entered differently, or if there's a PO box in that first address field, a PO box is the location of the Post Office box, not the location of the home, so that might have been a error. There's spelling errors and that type of thing.
So we mapped the ones we could, that we had clean _____, and we've gone back and added quite a few. I know Josh has worked on that, still going, as we work through that.
So with the energy providers in the region, again, we have three energy providers. Then we looked at the energy use metrics that we've already looked at a few of those. So from Roanoke Electric Cooperative, Town of Enfield, Wilson Energy, we can zoom in and see that information. And we talked about the deciles and how those are represented.
Now for – if you're an energy provider, so Roanoke Electric would see their data in more detail. Rather than deciles, they would see the actual energy use of that home, and energy use intensity, if we had square footage, and I think we've gone back and gathered square footage for all of the data that we've collected.
Just to summarize, here is all the data that we've collected. The red and green areas are our energy usage decile. You can see, again, those areas _____ here under these programs. The gray is program assistance from Department of Health and Human Services. Blue is Department of Environment Quality Weatherization Program. So this also – this spatial representation helps us see where we need to target. You can see there's a lot of assistance here in this region, in Rocky Mount region, but we don't have utility data from there. So the next phase, that might be something where we focus on those areas, so we can get to seeing – and the preference is to go before and after, so so many months or a year before the retrofit, and after the retrofit, for energy use, so identifying those timelines and the type of data. I think we'll probably talk about the type of data from the energy company, because I know that varies.
Some of the research questions that we've had, certainly what spatial methods may be applied to evaluate cost effectiveness. Laura spoke to some of those questions. How do census block group visualizations help or assist? I'm sorry. I'm distracted by the pop-op on here. I don't know if that's from – if we've lost anybody – is everybody still online?
[Crosstalk]
Charlynne Smith: Okay. I will ignore that. Got you. And so what processes can we really use to build and improve collaboration? And that's something I've worked at quite a bit with GIS. I find these type of systems allow you to collaborate, build support and partnerships, and basically help you make better decisions through spatial decision support tools. And I'll wrap it up with that, Ron.
Ron Townley: All right. Thank you, Charlynne. So here's a reminder to folks out there to, again, you can start entering your questions through the online tool, where we'll take some questions, and I'll sit here – we've got about eight people around the table that we've introduced today, so I'll let other people jump in here with other comments that we felt – points that might need to be emphasized.
I guess one of the questions that I always start off with is this slide here, and I'll ask Daniel Kauffman from ResiSpeak – ResiSpeak is our private company that has been a part of our process here. Daniel's president and principal at ResiSpeak, and they're a company that performs energy data analytics and visualization tools. And they've worked with North Carolina State University's optimization lab to look at kind of different optimization scenarios in this tool to see what they can learn. So Daniel, you want to talk about this slide for just a second?
Daniel Kauffman: Sure. Thanks, Ron. So one of the questions we've been asking as part of the project isn't just a matter of can we identify high energy use homes and funnel them to the right programs, but can we identify homes that have high energy savings opportunity, or consume a lot of energy as a comparison to what we think they ought to be consuming, a predicted value. So we're creating an analytic for the tool that predicts energy usage on a basis of characteristics of the home, such as year built, square footage, and type of home, and any other inputs that are pertinent to this, creating a predicted energy usage, and then looking at that prediction versus actual, and this is a mechanism that we're trying to build into a tool into order to identify homes with high energy savings opportunities.
Ron Townley: Great. And I guess that segues a little bit into getting up to that higher elevation question, which – to pose to the group, which is how are residents really directly impacted by this tool? We've talked about beneficiaries being DHHS and DEQ and the utilities, and obviously some of the partners around the table, but how does this translate to helping our low income residents that may be in older homes, and as we know, like much of rural America, our region – some of these residents are spending in excess of 30 percent of their income, disposable income, on energy bills.
So for me, my interest as a community developer and economic developer, a municipal manager, and someone just generally concerned, is I want dollars in their pockets to spend on Main Street, not on $1,000.00 heating bills in the wintertime, and $600.00 cooling bills in the summertime.
Tad: So Ron, I can say – this is Tad with _____, and I know some of the colleagues in the room as well would have thoughts also, but I think that one aspect of this is that when we look at homes that are using a lot of electricity, we certainly want to think about weatherization, energy efficiency, and urgent repair programs can be deployed to help those homeowners improve the energy efficiency of their homes.
Certainly, our hope would be that if we can deploy those programs in a better way, and monitor their effectiveness, that we would also see corresponding reduction in the reliance on LIEAP and crisis intervention programs. And from a broad policy standpoint, that could lead to the suggestions that we want to invest more in WAP and other related programs on the front end in the hope that we can reduce reliance on LIEAP and CIP programs on the back end.
But nevertheless, this is I think an example of how having access to this data and being able to look at how these programs work together and impact each other makes it a very valuable tool for that type of analysis.
Laura Langham: I would add two things briefly. One is you also want to consider the burden for the resident of providing the different agencies all of this information. You have to imagine that someone who does not have a car, does not have a printer, does not have a computer, trying to get utility information to one of these agencies, and other documents to them, so the more that these things can all be on a tool and the resident can come in and just sign a waiver, and all of a sudden not just one agency, but all of them have this information. That's going to create a great environment for getting assistance for removing barriers for low income.
Ron Townley: Great. And then a related question that we've received online, do you see the tool becoming a platform for investment prioritization, for proactive program support, or do you keep the platform as a warehouse to verify proposed program targets? I think that's a little dual question – related question.
Laura Langham: So part of what I'm reading into that is asking if we're going to be using this tool to help increase the efficacy of the programs. Would you all agree that's one of the –
Tad: Absolutely. I think on the – take WAP, for example. If we can in an easy way look at data and demonstrate the effectiveness of WAP programs, then we are in a position to be able to make improvements to those programs, increase resources to those programs, and track effectiveness. So I think those are definitely things there.
And I see that some folks are having trouble hearing. It says we've lost audio.
[Crosstalk]
Tad: Was that earlier? Okay. All right. Hopefully, that's been resolved.
Anne Tazewell: I'd like to add to – this is Anne Tazewell with the Clean Tech Center – with regards to the weatherization program and the heating and repair and replacement program that North Carolina DEQ is running. They're very interested, and we hope we can perform with new utility data that we have, actually some M&D, some before and after, as proof of concept. As mentioned, we don't have a huge number of homes, but I think by the end of this project with our new utility data that we will be able to do that monitoring and verification of the efficacy of their before and after, as well as one of the things I know that they're interested in, and hopefully, we will be able to accommodate, is like looking at different – the contractors that are actually performing the upfits, and how similar upfits may be done by different contractors may yield different energy efficiency results, and that could lead to analyzing and providing additional training for contractors that may need it.
Ron Townley: Great. Great. Moving along, I see we've – again, encouraging folks to use your questions bar to enter questions. Have we seen participating utilities utilizing this tool already? And I think – well, who wants to try and take that one on around the table? Yeah, there you go, Daniel.
Daniel Kauffman: I would say we're at the early stages of that. So one of the utilities in particular is going to be using it just in the next few weeks I believe to help plan a meter testing program to identify meters that might need inspection because they might be running a little too fast or too slow, and that's best done in a GIS tool such as this one.
Charlynne Smith: Just to add, we're not actually at the point – we're very close to the point where we're going to be able to give all of the data providing _____ North Caroline DEQ, the community action agencies, the utilities, DHHS, and the county agencies, access to their own private data. So I think that's going to spark a lot of – our hope is that that's going to spark a lot of insight into what they can possibly do with the tool. So at this point, they haven't actually utilized it.
[Background voices]
Ron Townley: Right. And so the question about have we got any plans to scale this out to more North Carolina agencies, other utilities, other counties, and the answer to that is hopefully, yes, we'd love to see this scaled out nationwide. The reason DOE funded this pilot project as one of only three across America, and the only one east of the West Coast, is because the vision of this project to take some pretty complex subject matter, not just big data, publicly available data and private data overlays, to try and make a useful tool, but we've also, as we talked about early in the presentation, really tried to tackle some of the various legal issues in that data sharing formula, and what that sort of framework may look like, and pilot looks like.
Obviously, as the world moves forward with smart grid technology and these different programs, we've got to do more with less, and become more efficient, and impact more and more communities. So yes, we hope to scale out this tool, and any listener who wants to know more about how they may get access or procure services at NC State University that's providing the firewall, and the backside services, and things like that, please at the end of the presentation feel free to contact us.
Charlynne Smith: Okay, and just to add into the design of this, one of the things we talked about at the beginning on how we structured this, because there are questions about using open source software, because the platform you saw is ESRI, so it's proprietary software, but that was something we evaluated. So the COG is going to be the leader in this. It was their region, their – would take over kind of managing it through NC State. But they already used that platform or are familiar with it. So if we want someone to adopt a technology that's being implemented, certainly being familiar with it and having access to it is a reason to select that platform.
And also, the other COGs in the state have access to that same thing. So our hope there was that we can then scale it out, at least into the other COGs.
Ron Townley: Right. Right. So for us, we hope to tackle it across North Caroline on a region by region basis.
[Crosstalk]
Ron Townley: As opposed to 100 counties and –
Charlynne Smith: Right.
Ron Townley: – 650 municipalities, or whatever that number is today.
Laura Langham: Yeah, I see this question Bob Leger was asking about –
Ron Townley: Hi, Bob.
Charlynne Smith: Hi, Bob.
Laura Langham: Everybody knows Bob. About if we would be using – if we would be – if the utilities could go in and add information about the retrofits that were done, so that they could compare measures. And as Anne mentioned, we've talked with weatherization, but utilities could do it, too with their energy efficiency programs, where they're actually analyzing home by home to identify best practices, if that's the contract, or if it's the measure.
And the answer, Bob, is that we are putting in, as you saw, like with weatherization, there were some blower door before and after retrofit numbers in there, and we do have a lot of additional information from the weatherization program, and we can add information from the utility as well.
I want to pick up one aspect of what you're asking, and that is at this time, we don't have a way for a data provider to enter their own data. They can submit it to us and then we can add it to the tool, but at this time, the tool isn't set up for data providers to add their own data directly.
Josh Randall: So also one of the questions we had was on the data collection standards. This is Josh from NC State, by the way. One of the data collection standards, and I think in our discussion – well, as part of the data team, we hope it does, and we've provided some feedback, I think, to say, especially on the geospatial side of providing some sort of format in order to be able to address – or be able to take in this data in a digestible and easy way to turn around in a quick manner. That was one of the biggest issues we had, as we discussed.
And so as we start interacting with the energy providers and the assistance programs more on the back end of the projects, I think that's something that we want – we want to be able to recommend. That's definitely a recommendation coming from the project. And that can be – I think even nationally that's a huge thing, is just there's a wealth of data out there, and being able to use it is important.
Ron Townley: Great. Ian continues to ask great questions. Given what we've discussed, do you see this platform as being integrated into the existing program application processes for DHHS and DEQ programs? And I believe the – I'll start us off with the general answer to that, is we certainly hope so. One of the things we found in our meetings with the departments is that they were excited about the potential power of the product. They noted to us – there's of course the common issue of in February, the elderly resident in the old home walks in with the $600.00 heating bill and asks what the utility or DHHS or somebody can do for them to help them with it. They offer some kinds of relief through various programs. Maybe they've utilized a weatherization program a little bit, but often not. And then next year, that same person is back, and the year after that, the same person is back.
So they were excited to be able to look at this tool and see what programs they may have already tried to utilize or quality for, as well as to determine what sheets they might hand them and say, well, you need to go and investigate this to try and get your energy in control.
That said, there's a caveat, and that is these agents are, of course, lean and buried in work, right? The world is shifting and not always in the most positive direction in our country. And so these DHHS workers are very busy people. And so one of the pieces that we talk about is how to integrate this into their program of work on their desktop, on their computer, because the one thing they made perfectly clear, please don't give us more work to do.
Laura Langham: Right.
Ron Townley: Please don't give us additional systems to learn. Please make this easy and convenient, so we can actually add value as opposed to taking more time and creating more bureaucracy.
Al Ripley: So Ron, one quick thing to build on in regard to Ian's question is that we do recognize that by building a waiver for the applicant to sign at the time that they are applying to allow us to have access to utility data, and to allow us to have access to their program participation data, would make these processes much more easy to evaluate down the road. So that's one recommendation going forward, that we should have those waivers in place, so that we can easily access that data and use it on the behalf of the applicant. And that'll make the application process easier, quicker, and also make program evaluation a lot easier.
Ron Townley: We being the appropriate partners in the program that need access.
Al Ripley: The agencies that administer the programs.
Ron Townley: Right.
Charlynne Smith: And I'd also like to add, on the data side of it, one of the things we had sort of talked about at the beginning, and that became very clear, especially with I think the DHHS program, was with the retrofits and energy efficiency, that's tied to a home, so an actual structure. DHHS tracks assistance by person. And that person might move, so they might be in a different home the next year. So it's more of a transient population at times, I think. I think you got that clear, Al, when you met with them.
So then that's just a different scenario in what we're looking at. So DHHS, when they provide data, it's usually by the address at the time they got assistance rather than necessarily tied to a home that we can track. So that became something clear on our spatial side of it. We're not tracking people. We're looking at homes.
Ron Townley: Great. I see Ian also asked about program expansion, even in this early stage, if we have got other councils of governments or folks interested in the Triangle region, and I guess that kind of goes to some of the IOU work, independently – investor owned utility, sorry. IOU. I start – all kinds of little things start going off in my head.
But we have some general interest from the other councils. We have not yet presented across the state. Really, you are the first national audience, the first statewide audience, so we are literally opening up the box today. So congratulations on being the first to sneak a peek on this.
Laura Langham: I want to go to Kevin Martin's question. He's saying was effectiveness or ineffectiveness of contractors identified. And I know where he's headed with this question. Anne brought that up, and that's something that we are talking with Weatherization Program about, because they're wanting to be able to do this, and to identify training needs, as Anne stated.
And the only thing that is keeping that from happening is we can't find funding to do this work. But within PEEIF, we're hoping to be able to start looking at identifying effectiveness with measurement and verification, but to really go deep and to create training from that. I know you're interested in that, Kevin. That's going to be a next step, even bigger lift. So if you have ideas on that, you let us know.
Charlynne Smith: But with our new utility data that – in the next three months, we should be able to at least answer the preliminary question.
Laura Langham: Right.
Charlynne Smith: Because we have more utility data.
Laura Langham: Yeah.
Ron Townley: And how about Kevin Martin's other question up there? We talked about that, helping the effort to create data collection standards. And I know that we talked about just the example in house when we got all the –
[Crosstalk]
Ron Townley: – addresses, right? How some people say Martin Luther King Boulevard. Others enter it as MLK. Others enter it as –
Charlynne Smith: ML King.
Ron Townley: – ML King, or MLK Jr. And so you guys went through quite an effort of how do you normalize –
Charlynne Smith: And that goes to who's entering the data.
Mark James: Yeah.
Ron Townley: But do you see the need for standardization across agencies?
Laura Langham: I would also say something that surprised –
Al Ripley: I see people nodding.
[Crosstalk]
Laura Langham: Something that surprised us, and Daniel Kauffman could tell you a story or two, is getting – you assume that once a utility says that they're willing to provide their data, that then the next day you get the data, and Daniel Kauffman can tell you that is not the experience that we had. And they – in some cases, and I don't know how many people realize this, but they don't – these utilities don't always know how to access their own data in the way that we need it to be able to do these analytics.
Charlynne Smith: Or they don't have the permission to do it. They can go into the system and run reports for their needs, but actually dumping a set of the data was more difficult.
Laura Langham: Yeah.
Charlynne Smith: Or challenging, I suppose.
Daniel Kauffman: I mean, I can jump in with one sort of interesting story, and Josh can collaborate – he can further explain this one. But we had two data sets of presumably the same homes, and they both had latitude/longitude coordinates, that one was a latitude/longitude coordinates of the physical electric meter, and the other was I believe the mailbox on the street, which may or may not have been anywhere near the electric meter, depending on the size of the property, and this is a very agricultural area. And so even that created quite a problem in trying to overlay these data sets.
So there's – there was a great challenge in just lining up all of these data sets so that they corresponded with the same households.
[Background voices]
Ron Townley: Sure.
[Crosstalk]
Ron Townley: We're getting to them, folks. So let's go for Mikey's. Do you see the database as a pilot – oh, sorry, I lost my – as a pilot database for a national database?
[Laughter]
Laura Langham: The magic question. National database scares me. It's hard enough for a five county region with three utilities.
Charlynne Smith: Bill and I, we've done national data sets and standards before, and kind of what's appropriate at a national level, when you get down to the local user, those standards may not always fit.
Bill Slocum: Yeah. Hey, this is Bill Slocum from NC State. Good afternoon, everyone. Just as Charlynne has talked about, when it comes to developing these national data standards, a lot of times when you aggregate data from a local municipality or a local user or entity, up to a national database, a lot of times, you're going to be scaling those data sets, depending on what's needed.
So I think for looking at a national standard of a data set, like I do with National Park Service, we would have several attributes that would be standard across the nation that we could accommodate, but then, there may be, for some local municipalities, they may have some attributes that are very specific to them.
So the answer is a cautious yes, to where it could be a pilot for a national database, using the metrics that we put together, such as square footage, age –
Charlynne Smith: Home type.
Bill Slocum: – home type, those types of things. Those could start as a national database going out, but I would be cautious about exploding that database too much.
Charlynne Smith: So we also had variations in the utility data, where it was annual versus –
[Crosstalk]
Laura Langham: – 15 minute increments.
Charlynne Smith: Yeah.
Bill Slocum: And address – if you want to also create a national database, the address type would have to be some type of standard.
Ron Townley: Okay. I think we've got just about enough time for just one or two more questions, and I see that – going to the top one, this is a wonderful project that if funded can be – funded, will set up the opportunity for continuous improvement by the state, region, county, etcetera. And yeah, Kevin, I think I agree with that statement. I think in closing, though, in talking about data, independently or – investor owned utilities often have the resources to do a lot of this work internally, and have even started semi-similar efforts. But maybe there's different values to electric membership cooperatives, people who are in the ElectriCities programs, municipal owned utilities, and things like that.
So one of the reasons our rural region was picked was not just because we have some challenges with some aging housing stock and maybe some higher than average people in need here, but we also have a nice sampling of small town utilities, mid-sized utilities, you know, small town kind of issuing a monthly electric bill and using a consultant to help them purchase power, and that sort of thing, versus a larger city-owned utility that's getting 15 minute incremental smart grid data that can be weather normalized for evaluations, and then an electric co-op that's covering five counties.
So maybe just a couple of final comments on the different kind of utility users, since this is an energy efficiency project, much to their benefits as well.
Charlynne Smith: Well, I'd just like to say I think that rural electric co-ops are best positioned to take advantage of this, because really, it's their – they serve their member owners, versus an IOU, which is looking at the bottom line, to a certain extent. So I think moving forward, they would be the most natural target to bring on new electric _____.
Laura Langham: Which is kind of why we started in this territory rather than in a major city that had an IOU in it. But in order to do this nationally, we would need to have the IOUs on board and participating in this. So – but it's easier to do a proof of concept with rural communities and then kind of –
Charlynne Smith: Yeah, showing the value proposition. It's going back to the early part of the presentation. We needed to really – to all of our data provider partners, kind of show or demonstrate that there is a value proposition for them to get involved. And that would be true with investor owned _____.
Laura Langham: Yeah.
Charlynne Smith: Because everybody's interested in energy efficiency.
Ron Townley: Right. Right. Well, listen, folks, we're approaching the end. I would like to once again thank everybody for participating today out there, and I would of course like to thank all of our partners for all of the work that they've done. We're in the final phase of this project, which is about the sustainability of the tool. So again, across the country, here in the region, here in North Carolina, if you see value, if you have questions, if you want to know more, we encourage you to contact us. You've got a very long web link at the bottom of the current slide. Again, all registrants will be receiving this presentation, which will contain this link via email. So hopefully when you signed up, you provided an accurate email address.
And that folder will also contain other materials and ways to get in touch with me, Ron Townley, at the Upper Coastal Plain Council of Governments, as well as other partners around the table. And with that, I'd like to just leave the final closing comment to DOE or Ookie Ma, if he's still on the line out there, to close us out.
Ookie Ma: No, I don't have much to say. I thank everyone for participating. I thank the project team for providing a great presentation and handling the Q&A, and we look forward to hearing from folks from the audience, because we'd really like to understand what would be interesting out there and what the needs are. So thanks, everyone.
Ron Townley: Thank you, Ookie, and thank you to the Department of Energy for all of your fantastic support on this project, both fiscally and technically. I hope everyone has a great day, and enjoy your weekend. And with that, we'll be signing off.
Charlynne Smith: Bye, everybody.
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