A Novel Graphical Lasso Based Approach Towards Segmentation Analysis in Energy Game-Theoretic Frameworks
Open accessAbstract
Energy game-theoretic frameworks have emerged to be a successful strategy to encourage energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incentives for energy efficient players. However, prior works involve an incentive design mechanism which is dependent on knowledge of utility functions for all the players in the game, which is hard to compute especially when the number of players is high, common in energy game-theoretic frameworks. Our research proposes that the utilities of players in such a framework can be grouped together to a relatively small number of clusters, and the clusters can then be targeted with tailored incentives. The key to above segmentation analysis is to learn the features leading to human decision making towards energy usage in competitive environments. We propose a novel graphical lasso based approach to perform such segmentation, by studying the feature correlations in a real-world energy social game dataset. To further improve the explainability of the model, we perform causality study using grangers causality. Proposed segmentation analysis results in characteristic clusters demonstrating different energy usage behaviors. We also present avenues to implement intelligent incentive design using proposed segmentation method.
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Energy game-theoretic frameworks can incentivize occupants to minimize energy demand, and ML can help develop tailored incentives.
“Energy game-theoretic frameworks can also incentivize occupants to actively minimize their energy demand, and ML can help here to develop tailored incentives based on different energy usage behaviors [159].”
From Tackling climate change with machine learning (2023) · cites this paper as [159]
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- Authors
- Das, H. P., Konstantakopoulos, I. C., Manasawala, A. B., Veeravalli, T., Liu, H., & Spanos, C. J.
- Affiliations
- University of California, Berkeley (US)
- Venue
- 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
- Publisher
- IEEE
- Published
- 2019-12
- OpenAlex
- W2978534880
- Cited by
- 17
- References
- 30
- Citations by year
- 2024: 4 · 2023: 1 · 2022: 5 · 2021: 7
- FWCI
- 4.77
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Conference on Machine Learning and Applications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation
- Alexander S. Onassis Public Benefit Foundation
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Education and Sustainability
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Keywords
- graphical lasso, customer segmentation, incentive design, energy efficiency, human-in-the-loop, feature correlation, Granger causality, social games
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