City's and communities wishing to plan and implement community-scale energy reduction, energy efficiency, and clean energy system (ex. district energy, community-scale solar, etc.) deployment lack information on their community’s energy use. Efforts in NYC and San Francisco-using benchmarking laws for data collection-have been able to spatially understand the use of energy resources (ex. elec, ng, fuel oil) throughout their jurisdiction’s built environment. Available data required to complete these analyses across the US are sparse, resource-intensive and politically challenging to collect. This submission suggests a "wish list" dataset of sub-community level (i.e. parcel, census block, etc.) energy-use datasets be collected and distributed to communities to evaluate and use for low-energy consuming and resiliency planning. Combined with NOAA weather data, NREL renewable energy availability data, FERC transmission data, Energy Information Administration (EIA) sectoral energy use statistics, census information, and combined with third-party organization data (ex. PJM, etc.), communities could begin to map the energy use of their communities. These analyses, communities would be empowered to understand energy reliability and security issues at their local levels, plan for deployment of community-scale energy reduction solutions including applicable renewable and clean energy systems and district energy systems.
Ontario (Canada) and several European Union member countries (ex. Germany) conduct such analysis. In addition to support community energy system efficiency and reliability, such analysis supports a community’s efforts to effectively address climate change mitigation. Moreover, in the wake of Hurricane Sandy, the mapping of energy using at a sub-community level will provide important information to communities–particularly urban communities–to develop resiliency strategies for their local energy and critical infrastructure.
Collecting sub-community-level energy use data is primarily focused on energy system planning and implementation. However, the potential to utilize these data in combination with the open datasets available from OpenEI, NREL, EIA, FERC, etc. is significant. These data, mashed together, could inform regulatory paradigms in US states, inform incentives to implement energy efficiency and renewable energy systems as scale, reduce barriers to homeowners to implement energy efficiency, and identify opportunities for public-private energy partnerships.
Providing a mechanism to release these data in a fashion that does not breach privacy, but affords communities the opportunity to plan and develop in a resilient and efficient way is a catalyst to increase the efficiency, security, and effectiveness of the US energy system.