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To better understand what persuades people to buy photovoltaic (PV) systems for their homes, Sandia researchers are gathering data on consumer motivations that can feed sophisticated computer models and thus lead to greater use of solar energy. A primary project goal is to help increase the nation’s share of solar energy in the electricity market (currently at < 0.5%) to at least 14% by 2030. This is the second year of a three-year effort.
“If we can develop effective and accurate predictive models, we can help identify policy variables that could increase residential PV system purchases and ultimately help advance the mission of the SunShot Initiative,” said Kiran Lakkaraju, Sandia’s project lead (in Sandia’s Cognitive Modeling Dept.). Specifically, he said, an effective solar purchase dynamics model can be used to predict and even influence consumer purchasing decisions.
The modeling project, part of the Solar Energy Evolution and Diffusion Studies (SEEDS) program, is one of many SunShot Initiative activities that seeks to make solar energy cost-competitive with other forms of electricity generation. SEEDS projects are designed to investigate methods for transforming the operations of solar researchers, manufacturers, developers, installers and policymakers.
The projects were discussed at a May 22 workshop at the DOE’s SunShot Grand Challenge Summit. The event took place May 19–22 in Anaheim, California. Sandia’s solar energy program includes deep research and development in areas such as PV systems, concentrating solar power, grid integration, and solar codes and standards.
Sandia is collecting and analyzing large data sets, said Jerry McNeish (Manager of Sandia’s Predictive Simulation Research and Development Dept.)—leading to two different models, one that predicts how likely an individual is to buy a PV system and one that predicts how long that individual will take to make the investment. “We’re essentially creating a model that predicts household solar energy system purchases based on such variables as price, energy savings, environmental concerns, and other factors,” said Lakkaraju. “But then we’re also running experiments that feed results back into the model. We have a cycle where we use the model to test and generate hypotheses about solar panel purchases, but then we test these hypotheses through experiments to improve the model.” The Sandia-developed models, Lakkaraju said, have already predicted purchasing behavior 200%–500% better than current models.
The team also is investigating novel financing structures, such as third-party ownership through leasing or power-purchasing agreements, seen as key to the industry’s growth. “This work’s significance is that it will help identify those likely to purchase PV systems and help forecast future market trends,” said Lakkaraju. “Ultimately, it will help those in the solar industry to more effectively bring solar energy to consumers.”
Read the Sandia news release.