  In this paper, we propose a gamification approach as a novel framework for
smart building infrastructure with the goal of motivating human occupants to
reconsider personal energy usage and to have positive effects on their
environment. Human interaction in the context of cyber-physical systems is a
core component and consideration in the implementation of any smart building
technology. Research has shown that the adoption of human-centric building
services and amenities leads to improvements in the operational efficiency of
these cyber-physical systems directed towards controlling building energy
usage. We introduce a strategy in form of a game-theoretic framework that
incorporates humans-in-the-loop modeling by creating an interface to allow
building managers to interact with occupants and potentially incentivize energy
efficient behavior. Prior works on game theoretic analysis typically rely on
the assumption that the utility function of each individual agent is known a
priori. Instead, we propose novel utility learning framework for benchmarking
that employs robust estimations of occupant actions towards energy efficiency.
To improve forecasting performance, we extend the utility learning scheme by
leveraging deep bi-directional recurrent neural networks. Using the proposed
methods on data gathered from occupant actions for resources such as room
lighting, we forecast patterns of energy resource usage to demonstrate the
prediction performance of the methods. The results of our study show that we
can achieve a highly accurate representation of the ground truth for occupant
energy resource usage. We also demonstrate the explainable nature on human
decision making towards energy usage inherent in the dataset using graphical
lasso and granger causality algorithms. Finally, we open source the
de-identified, high-dimensional data pertaining to the energy game-theoretic
framework.
