Machine Learning from Schools about Energy Efficiency
Open accessAbstract
In the United States, consumers invest billions of dollars annually in energy efficiency, often 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.
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Burlig et al. used Lasso regression on hourly electricity consumption data from California schools to find that energy efficiency interventions fall short of expected savings.
“For example, Burlig et al. [102] used Lasso regression on hourly electricity consumption data from schools in California to find that energy efficiency interventions fall short of the expected savings.”
From Tackling climate change with machine learning (2023) · cites this paper as [102]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Burlig, F., Knittel, C., Rapson, D., Reguant, M., & Wolfram, C.
- Affiliations
- University of Chicago (US)
- National Bureau of Economic Research (US)
- Northwestern University (US)
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2017-10
- OpenAlex
- W2762742523
- Cited by
- 39
- References
- 31
- Citations by year
- 2024: 4 · 2023: 3 · 2022: 7 · 2021: 4 · 2020: 9 · 2019: 6 · 2018: 5 · 2017: 1
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- Funders
- National Science Foundation (1106400)
- California Public Utilities Commission
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy Efficiency and Management
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Keywords
- energy efficiency, K-12 schools, energy efficiency upgrades, electricity consumption, treatment effect estimation, HVAC upgrades, lighting upgrades, energy conservation, targeting, school characteristics, California
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