Solar Technology Diffusion Through Data-Driven Behavior Modeling
Lecture by: Dr. Kiran Lakkaraju, Sandia National Labs
Thursday, November 5th, 2:00 to 4:00 p.m.
SFEBB 3170 (Spencer Fox Eccles Business Building)
About the Lecture: Increasing use of clean, renewable energy can help reduce U.S. carbon emissions and oil dependence. Given that residential energy use is 21% of energy consumption in the US, there has been increased interest in understanding, and predicting, residential consumer behavior towards purchasing solar photovoltaic (PV) panels. Better prediction of consumer behavior can reduce customer acquisition “soft costs” which will reduce solar PV prices.
In this talk, Dr. Lakkaraju will provide an overview of efforts at Sandi National Labs to develop a computational model of residential consumer behavior. Many factors influence this complex decision, from economic, attitudinal, demographic and technical. Sandia Labs has created an agent-based model that incorporates a wide array of these features to predict solar PV purchasing trends based on household level data from San Diego County. They find their model performs better than existing models that only take into account peer effects and economic characteristics.
Significant financial incentives are provided at the local, state and federal level. Predictive models can help inform these decisions by providing a tool to evaluate different policies (i.e., a what-if analysis). This talk will also address how to use the developed model to understand the impact of increasing step-wise incentive programs and suggest alternative programs.