A free webinar on Thursday, July 20, 2017, from 1–2 p.m. ET, will share exciting new research by Sandia National Laboratories, National Renewable Energy Laboratory (NREL), and Vanderbilt University on how data analytics, machine learning, and social science can improve marketing and customer acquisition processes.
Kiran Lakkaraju, senior member of the technical staff at Sandia National Laboratories, will present “Solar Technology Diffusion through Data-Driven Behavior Modeling”: Increasing use of clean, renewable energy can help reduce dependence on fossil fuels. Given that residential energy use is 21% of energy consumption in the United States, 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. This talk will provide an overview of efforts to develop a computational model of residential consumer behavior. Many factors influence this complex decision, from economic, attitudinal, demographic and technical. We have 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. Predictive models can help inform decisions by allowing “what if” analyses. We will discuss how to use the predictive model to explore financial incentive schemes, and to study how different marketing approaches can increase adoption.