Data to Decisions: NREL’s Latest Cities-LEAP Work Provides Unique Solutions to Local Governments
November 14, 2018
The Department of Energy’s Cities Leading through Energy Analysis and Planning (Cities-LEAP) project published three new pathways to use data to inform more strategic energy decisions at the local government level.
Using assessor data to estimate rooftop PV potential in Hutchinson, Minnesota.
NREL partnered with the City of Hutchinson, Minnesota to create a pathway to estimate rooftop PV potential to inform energy goals. Hutchinson didn’t have access to LIDAR data to estimate rooftop photovoltaic potential and Google Project Sunroof’s Data Explorer had not yet mapped their city’s rooftop PV potential. Together, NREL and Hutchinson developed a way to use the city’s geographic information system (GIS) data on buildings, paired with research and analysis at NREL to develop an estimate of rooftop PV potential within the city. The simple approach explained in this Data to Decisions publication can be used by other cities lacking LIDAR data to understand their own rooftop PV potential.
Figure 1. Example of GIS buildings data for Hutchinson, Minnesota.
Mapping energy burden to geospatially target energy programs and outreach in Rochester, New York.
Using the Department of Energy’s Low-Income Energy Affordability Data (LEAD) tool, NREL partnered with the City of Rochester, New York to map energy burden, or the portion of income spent on utility bills, by U.S. Census tract. Low-income families often spend a larger percentage of their income than non-low-income families on energy. The LEAD tool calibrates U.S. Census housing data, including primary heating fuel type, household energy expenditures, tenure (whether housing units are owner or renter occupied), building year of first construction, number of units, to household area median income and electric and natural gas data from the Energy Information Administration. The result allows a mapping of estimated energy burden and other household characteristics at the Census tract level. Rochester sought to use the data to more strategically target households for efficiency interventions and information campaigns based not simply on income but on energy burden. Focusing on energy burden takes the inefficiency of the building and more expensive heating fuels into account in addition to household income.
Figure 2. Estimated number of housing units with greater than 10% energy burden by U.S. Census tract in Rochester, New York.
Understanding the drivers of change in city emissions in Bellevue, Washington.
Through DOE Cities-LEAP funding, a team led by the City of Bellevue, Washington developed a tool to help cities understand the various factors that drive increases and decreases in greenhouse gas emissions from one inventory to the next. The tool helps cities understand how weather, state renewable portfolio standards, national policy, the economy and other exogenous factors contribute to changes in emissions. By accounting for these external influences, cities can more clearly chart changes in emissions attributable to city actions and adjust their plans and programs accordingly.
Figure 3. Drivers of change for City of Bellevue, Washington.