What makes people go solar? Is it the incentives, the price drops in solar, a feeling of environmental responsibility, etc.? Those are the questions that Sandia National Laboratories are trying to answer through part of their Solar Energy Evolution and Diffusion Studies (SEEDS) projects. While still in early stages the models are already predicted purchasing behavior 200-500 percent better than current models, Sandia said.
The national lab is gathering data on what motivated homeowners to solar and is using sophisticated computer models in an attempt to understand how to help more people go solar. “A primary goal of the project is to help increase the nation’s share of solar energy in the electricity market from its current share of less than .05 percent to at least 14 percent by 2030,” Sandia said.
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“If we can develop effective and accurate predictive models, we can help identify policy variables that could increase purchases of residential PV systems and ultimately help advance the mission of the SunShot Initiative,” explained Sandia Project Lead Kiran Lakkaraju. By creating a computer model that can predict consumer purchasing decisions and could influence those decisions.
Thus far the project, which is working with project partners at the National Renewable Energy Laboratory (NREL) and the California Center for Sustainable Energy (CCSE), is surveying 2,000 San Diego County, Calif., residents, half of whom purchased solar arrays and another half that had not yet. The information has created 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.”
The researchers will present their early findings—this is in the second year of a three-year project, tomorrow (May 22) at the Sunshot Grand Challenge Summit. Now data from the surveys are being reviewed by quantitative modeling experts at Sandia and Vanderbilt University and are being fed into modeling tools.
“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.”
This article was originally posted on SolarReviews by Chris Meehan.