Research

Energy Institute at Haas

Research

EI @ Haas Working Paper WP-283 Abstract

WP-283: Fiona Burlig, Christopher Knittel, David Rapson, Mar Reguant, and Catherine Wolfram, "Machine Learning from Schools about Energy Efficiency" (September 2017) Full Paper

Abstract:
In the United States, consumers invest billions of dollars annually in energy efficiency, of-ten on the assumption that these investments will pay for themselves via future energy cost reductions. We study energy efficiency upgrades in K-12 schools in California. We develop and implement a novel machine learning approach for estimating treatment effects using high-frequency panel data, and demonstrate that this method outperforms standard panel fixed effects approaches. We find that energy efficiency upgrades reduce electricity consumption by 3 percent, but that these reductions total only 24 percent of ex ante expected savings. HVAC and lighting upgrades perform better, but still deliver less than half of what was expected. Finally, beyond location, school characteristics that are readily available to policymakers do not appear to predict realization rates across schools, suggesting that improving realization rates via targeting may prove challenging.