Evaluation of Factors that Influence Residential Solar Panel Installations
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Though rooftop photovoltaic (PV) systems are the fastest growing source of distributed generation, detailed information about where they are located and who their owners are is often known only to installers and utility companies. This lack of detailed information is a barrier to policy and financial assessment of solar energy generation and use. To bridge the described data gap, Oak Ridge National Laboratory (ORNL) was sponsored by the Department of Energy (DOE) Office of Energy Policy and Systems Analysis (EPSA) to create an automated approach for detecting and characterizing buildings with installed solar panels using high-resolution overhead imagery. Additionally, ORNL was tasked with using machine learning techniques to classify parcels on which solar panels were automatically detected in the Washington, DC, and Boston areas as commercial or residential, and then providing a list of recommended variables and modeling techniques that could be combined with these results to identify attributes that motivate the installation of residential solar panels. This technical report describes the methodology, results, and recommendations in greater detail, including lessons learned and future work.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of General Counsel (GC), Energy Policy (GC-70)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1427606
- Report Number(s):
- ORNL/TM-2018/780
- Country of Publication:
- United States
- Language:
- English
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