期刊
IEEE ACCESS
卷 6, 期 -, 页码 59141-59151出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2872589
关键词
University campus buildings; HVAC systems; distributed model predictive control; ADMM; energy cost; thermal comfort
资金
- National Natural Science Foundation of China [61502252, 61729101, 61572262, 61522109, 61671253, 91738201, 61771258]
- Major Program of the National Natural Science Foundation of Hubei in China [2016CFA009]
- Natural Science Foundation of Jiangsu Province [BK20150869, BK20150040, BK20171446]
- Key Project of Natural Science Research of Higher Education Institutions of Jiangsu Province [15KJA510003]
- Fundamental Research Funds for the Central Universities [2015ZDTD012]
- Open Project of the State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information System [BK218002]
Educational buildings consume about 2% of the total energy in a country, which leads to energy cost concerns for building operators. To reduce the building energy cost, an effective way is to intelligently schedule heating, ventilation, and air conditioning (HVAC) systems, which account for above 40% of the total energy consumption in educational buildings. In this paper, we investigate an energy optimization problem for HVAC systems in university campus buildings. To be specific, we first formulate a total cost optimization problem that minimizes the sum of energy cost related to HVAC systems and thermal discomfort cost associated with occupants considering zone occupancy pattern and thermal preference of occupants in each zone. Due to the existence of uncertain parameters as well as spatially and temporally coupled constraints, it is very challenging to solve the formulated problem. To this end, we propose a distributed model predictive control algorithm based on the alternating direction method of multipliers, which has high scalability with an increasing number of zones and can protect user privacy. Extensive simulations based on the real-world traces show that the proposed distributed algorithm could effectively reduce the total cost and offer a flexible tradeoff between energy cost and thermal discomfort.
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