2 research outputs found
A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks
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.Comment: Proceedings of the Special Session on Machine Learning in Energy
Application, International Conference on Machine Learning and Applications
(ICMLA) 2019. arXiv admin note: text overlap with arXiv:1810.1053