针对太阳能-土壤源热泵复合系统在水箱直接供暖模式下运行,存在热泵机组频繁启停以及供暖负荷分配不合理的问题,提出一种基于BP神经网络的动态供暖策略。以沈阳某建筑为对象,设计太阳能-土壤源热泵复合系统,通过TRNSYS和Matlab软件进行仿真。通过与常规水箱直接供暖策略对比,该策略能在典型供暖日中将机组的启停次数从9次减少为2次,机组COP从3.90提高至4.02。在供暖负荷较大的供暖季中期,平均每天提高机组COP 2.37%。
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.
关键词
地源热泵 /
太阳能 /
神经网络 /
供暖策略
Key words
ground source heat pumps /
solar energy /
neural networks /
heating strategy
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基金
华设设计集团股份有限公司开放基金（8503008464）
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