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

doi:10.15302/J-FEM-2017045

Front. Eng

2017, Vol. 4

Issue (3) : 304-314 https://doi.org/10.15302/J-FEM-2017045

RESEARCH ARTICLE

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

Zhaohui ZHANG¹, Ruilong DENG², Tao YUAN¹, S. Joe QIN³(

)

¹. Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
². Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9 , Canada
³. Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA; The Chinese University of Hong Kong, Shenzhen 518172, China

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Abstract

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.

Keywords game theory demand response HVAC control multi-building system

Corresponding Author(s): S. Joe QIN

Just Accepted Date: 25 August 2017 Online First Date: 26 September 2017 Issue Date: 30 October 2017

Cite this article:

Zhaohui ZHANG,Ruilong DENG,Tao YUAN, et al. Sliding window games for cooperative building temperature control using a distributed learning method[J]. Front. Eng, 2017, 4(3): 304-314.

URL:

https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2017045
https://academic.hep.com.cn/fem/EN/Y2017/V4/I3/304

Fig.1 Schematic of the payoff-based distributed learning algorithm

Fig.2 Global utility distributed (blue dot) versus centralized optimum (red cross).

Fig.3 Global utility components 1 and 2: Distributed (blue dot) versus centralized optimum (red cross)

Fig.4 Global utility component 3: Distributed (blue dot) versus centralized optimum (red cross)

Fig.5 Sliding window size= 4 (black cross) vs. window size= 1 (blue dot)

Fig.6 Outdoor (red), indoor (black cross) temperature, and pre-cooling

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