Smart building energy efficiency via social game: a robust utility learning framework for closing–the–loop

Abstract

Given a non-cooperative, continuous game, we describe a framework for parametric utility learning. Using heteroskedasticity inference, we adapt a Constrained Feasible Generalized Least Squares (cFGLS) utility learning method in which estimator variance is reduced, unbiased, and consistent. We extend our utility learning method using bootstrapping and bagging. We show the performance of the proposed method using data from a social game experiment designed to encourage energy efficient behavior amongst building occupants. Using occupant voting data we simulate the game defined by the estimated utility functions and show that the performance of our robust utility learning method and quantify its improvement over classical methods such as Ordinary Least Squares (OLS).