Demand-driven power saving by multiagent negotiation for HVAC control

Authors:
Yi-ting Tsao;Jane Yung-jen Hsu
Affiliations:
National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan
Venue:
Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities
Year:
2013

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Abstract

Buildings account for roughly 40% of all U.S. energy use, and HVAC systems are a major culprit. The goal of this research is to reduce power consumption without sacrificing human comfort. This paper presents a cooling demand estimation from heat generation to assess the quantity of cooling supply, which helps diagnose potential problems in the HVAC system. A negotiation-based approach is proposed to balance power consumption, cooling for human comfort, and smooth operation for equipment health. Experiments were conducted with the NTU CSIE July 2012 dataset [6] as well as online live experiments in the computer science building on campus. The experiments demonstrated that the proposed method reduced 3.81% to 5.96% of power consumption with consideration of smoothness.