-- This project is inactive --
National Renewable Energy Laboratory, along with Portland State University, the University of Arizona, Clean Power Finance, and other partners, under the Solar Energy Evolution and Diffusion Studies (SEEDS) program, performed micro-level studies in four representative U.S. regions to identify generalizable household-level motivations for adopting residential photovoltaics (PV), and to refine computational modeling frameworks for simulating current and future solar adoption trends.
Decisions to adopt new technologies are influenced by more than price alone. Whether a household adopts a rooftop PV system may depend on access to trusted information, established social norms, context-specific motivations and concerns, and the experiences from a customer's peer group. Each of these factors offers a potential leverage point for accelerating solar adoption. This research project focused on understanding and simulating the complex household-level decision making processes that underlie residential PV market demand, and capturing the interactions between decision variables and their evolution over time.
First, the project team selected four representative solar markets for in-depth investigation and formed partnerships with local solar businesses and decision makers. They collected rich datasets to characterize PV market dynamics within each region, and synthesized it into a computational framework that simulates the impact of specific market modifications on PV demand, such as introducing innovative new financing structures, new methods for framing the benefits or risks associated with a solar investment, or new ways to bundle solar with energy efficiency technologies. Finally, pilot experiments were performed to test new strategies for accelerating solar diffusion processes.
This project paired experts in psychology and behavioral science with those in data science to evaluate why people become interested in solar and what prompts them to ultimately adopt it. The data collected was tested in computational models and in real-world pilots. Researchers found that with fewer than 5% of leads ultimately converting to sales, small improvements in the sales process could translate into big reductions in customer acquisition costs.