# A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks

arXiv: https://arxiv.org/abs/1910.02217 · HTML: https://mlsanity.com/paper/1910.02217/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data](https://mlsanity.com/paper/2109.06486/) (2021)
- [CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training](https://mlsanity.com/paper/2108.11368/) (2021)
- [Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response](https://mlsanity.com/paper/2104.14670/) (2021)
- [Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs](https://mlsanity.com/paper/2108.06594/) (2021)
- [Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control](https://mlsanity.com/paper/2111.06025/) (2021)
- [Conditional Synthetic Data Generation for Personal Thermal Comfort Models](https://mlsanity.com/paper/2203.05242/) (2022)
- [Machine Learning for Smart and Energy-Efficient Buildings](https://mlsanity.com/paper/2211.14889/) (2022)
- [Why Reinforcement Learning in Energy Systems Needs Explanations](https://mlsanity.com/paper/2405.18823/) (2024)

## Abstract (arXiv)

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.