Design, Benchmarking and Graphical Lasso based Explainability Analysis of an Energy Game-Theoretic Framework (Papers Track)

Hari Prasanna Das (UC Berkeley ); Ioannis C. Konstantakopoulos (UC Berkeley); Aummul Baneen Manasawala (UC Berkeley); Tanya Veeravalli (UC Berkeley); Huihan Liu (UC Berkeley ); Costas J. Spanos (University of California at Berkeley)

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Abstract

Energy use in buildings account for approximately half of global electricity consumption and a significant amount of CO2 emissions. The occupants of a building typically lack the independent motivation necessary to optimize their energy usage. In this paper, we propose a novel energy game-theoretic framework for smart building which incorporates human-in-the-loop modeling by creating an interface to allow interaction with occupants and potentially incentivize energy efficient behavior. We present open-sourced dataset and benchmarked results for forecasting of energy resource usage patterns by leveraging classical machine learning and deep learning methods including deep bi-directional recurrent neural networks. Finally, we use graphical lasso to demonstrate the explainable nature on human decision making towards energy usage inherent in the dataset.