Maximum Entropy Modeling for Photovoltaic Optimization: A Spatial Analysis of California

Adoption of renewable energy for electricity generation holds significant potential to produce emission reduced power, in addition to other benefits. Great promise has been expected for solar energy, specifically photovoltaic (PV) solar panels, which harness the photoelectric effect to produce electricity. Over the past 20 years, the average number of PV facilities in the United States have increased nearly 40% per year. Even with this growth, as of 2011, PV provided only .2% of all national electric generating capacity. However, if historical growth trends can continue, solar energy may stand on the threshold of much larger adoption rates. Within this context, a holistic understanding of the social, environmental and economic elements that play a role in aiding PV growth may prove fruitful. Questions, which have remained unanswered, include what parties adopt, what kind of sites are most common, where development most frequent and what are the drivers of PV adoption? In this paper, a literature review of quantitative and social studies, related to PV adoption is conducted. The output of the literature review is used to select environmental, economic and social variables, which guide a spatial model building process. The model goal is to predict PV adoption hotspots. The spatial boundary of this study is limited to California, chosen due to a national dominance of the PV market. A maximum entropy based model, Maxent, was selected due to its high regard within the field of species distribution modeling, its ability to predict unoccupied habitat, the complex relationships it fits between indicator variables, and the ease at which it integrates and visualizes spatial data. Verification is conducted by comparing model output with historical adoption trends and remote sensing. Model results are analyzed for potential PV utilization market segment and policy implications. The study concludes with suggestions for further research.