Sliding window games for cooperative building temperature control using a distributed learning method

In practice, an energy consumer often consists of a set of residential or commercial buildings, with individual units that are expected to cooperate to achieve overall optimization under modern electricity operations, such as time-of-use price. Global utility is decomposed to the payoff of each player, and each game is played over a prediction horizon through the design of a series of sliding window games by treating each building as a player. During the games, a distributed learning algorithm based on game theory is proposed such that each building learns to play a part of the global optimum through state transition. The proposed scheme is applied to a case study of three buildings to demonstrate its effectiveness.