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In this paper, we propose a gamification approach as a novel framework forsmart building infrastructure with the goal of motivating human occupants toreconsider personal energy usage and to have positive effects on theirenvironment. Human interaction in the context of cyber-physical systems is acore component and consideration in the implementation of any smart buildingtechnology. Research has shown that the adoption of human-centric buildingservices and amenities leads to improvements in the operational efficiency ofthese cyber-physical systems directed towards controlling building energyusage. We introduce a strategy in form of a game-theoretic framework thatincorporates humans-in-the-loop modeling by creating an interface to allowbuilding managers to interact with occupants and potentially incentivize energyefficient behavior. Prior works on game theoretic analysis typically rely onthe assumption that the utility function of each individual agent is known apriori. Instead, we propose novel utility learning framework for benchmarkingthat employs robust estimations of occupant actions towards energy efficiency.To improve forecasting performance, we extend the utility learning scheme byleveraging deep bi-directional recurrent neural networks. Using the proposedmethods on data gathered from occupant actions for resources such as roomlighting, we forecast patterns of energy resource usage to demonstrate theprediction performance of the methods. The results of our study show that wecan achieve a highly accurate representation of the ground truth for occupantenergy resource usage. We also demonstrate the explainable nature on humandecision making towards energy usage inherent in the dataset using graphicallasso and granger causality algorithms. Finally, we open source thede-identified, high-dimensional data pertaining to the energy game-theoreticframework.

Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure