SIMULATION ON A HEATING STRATEGY BASED ON BP NEURAL NETWORK OF SOLAR-GROUND SOURCE HEAT PUMP COMPOSITE SYSTEM

Yang Zhen, Chen Xiangyan, Liu Cheng, Xu Bo, Lyu Zhongling, Chen Zhenqian

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 224-229.

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 224-229. DOI: 10.19912/j.0254-0096.tynxb.2020-1424

SIMULATION ON A HEATING STRATEGY BASED ON BP NEURAL NETWORK OF SOLAR-GROUND SOURCE HEAT PUMP COMPOSITE SYSTEM

  • Yang Zhen1, Chen Xiangyan2, Liu Cheng2, Xu Bo1, Lyu Zhongling3, Chen Zhenqian1
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Abstract

Aiming at the problems of frequent shutdown and unreasonable heating load distribution when running direct heating mode using water tank in the solar-ground source heat pump composite system, a dynamic heating strategy based on BP neural network was proposed. Taking a building in Shenyang as an research object, a solar-ground source heat pump composite system was designed, and simulated by using TRNSYS and Matlab software. Compared with the traditional direct heating mode using water tank, this strategy can reduce the on/off number of the unit from 9 to 2, and increase the COP of the unit from 3.90 to 4.02 during a typical heating day. In the middle of heating season, the COP of the unit is increased by 2.37% on average per day.

Key words

ground source heat pumps / solar energy / neural networks / heating strategy

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Yang Zhen, Chen Xiangyan, Liu Cheng, Xu Bo, Lyu Zhongling, Chen Zhenqian. SIMULATION ON A HEATING STRATEGY BASED ON BP NEURAL NETWORK OF SOLAR-GROUND SOURCE HEAT PUMP COMPOSITE SYSTEM[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 224-229 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1424

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