Multi-objective Evolutionary Learning for Near Pareto-Optimal Optimization of Solar Deployment
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3736425.3770115.pdf
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1.6 MB
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Author(s)
Sigrist, Cooper
Li, Archimedes
Zhang, Alice
Lechowicz, Adam
Bashir, Noman
Lertsaroj, Pichsinee
Bahlous-Boldi, Ryan
Hajiesmaili, Mohammad
Date Issued
November 11, 2025
Publisher
ACM|The 12th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
Citation
Cooper Sigrist, Archimedes Li, Alice Zhang, Adam Lechowicz, Noman Bashir, Pichsinee Lertsaroj, Ryan Bahlous-Boldi, and Mohammad Hajiesmaili. 2025. Multi-objective Evolutionary Learning for Near Pareto-Optimal Optimization of Solar Deployment. In Proceedings of the 12th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '25). Association for Computing Machinery, New York, NY, USA, 213–223.
Version
Final published version
Abstract
Existing residential rooftop photovoltaic (PV) installations in the United States are inequitable, as they are concentrated in high-income neighborhoods, and carbon-inefficient because they are often not located in electric grids dominated by fossil-fuel generators. Prior work, however, shows that prioritizing socioeconomic equity can also significantly increase the carbon efficiency of new installations. In this paper, we formalize the problem of site selection for rooftop PV installations as a multi-objective optimization problem, with metrics including energy generation, carbon offsetting, and demographic equity. We introduce a novel method called Evolutionary Value Assignment (EVA) that uses a neural network trained via evolutionary learning to select ideal sites for deployment. We evaluate our proposed approach in a case study using a dataset of U.S. solar generation and demographic information. Compared to projections of current installation trends, our method improves Carbon Efficiency by 43%, Income Equity by 41%, and Racial Equity by 24%, while increasing Energy Generation Potential by up to 10%. Therefore, our optimized placement can achieve the estimated carbon offset needed for net-zero emissions from electricity generation earlier than current deployment trends.
Description
BUILDSYS ’25, Golden, CO, USA
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3736425.3770115
