基于遗传算法神经网络的地源热泵夏季低负荷运行性能预测分析  

Analysis on Low Load Performance Prediction of Ground Source Heat Pumps in Summer Based on Genetic Algorithm and Back Propagation Neural Network

作　　者：董艳芳 朱辉[1] 曾召田[2,3] 门玉葵 梁秒梦 DONG Yan-fang;ZHU Hui;ZENG Zhao-tian;MEN Yu-kui;LIANG Miao-meng(College of Energy Engineering and Building Environment, Guilin University of Aerospace Technology, Guilin 541004, China;College of Civil Engineering and Architecture, Guilin University of Technology, Guilin 541004, China;Guangxi Key Laboratory of New Energy and Building Energy Saving, Guilin 541004, China)

机构地区：[1]桂林航天工业学院能源与建筑环境学院,桂林541004 [2]桂林理工大学土木与建筑工程学院,桂林541004 [3]广西建筑新能源与节能重点实验室,桂林541004

出　　处：《科学技术与工程》2022年第12期4984-4992,共9页Science Technology and Engineering

基　　金：国家自然科学基金(51568014);广西中青年教师基础能力提升项目(2017KY0259);广西建筑新能源与节能重点实验室项目(19-J-21-8)。

摘　　要：为了探索夏热冬冷地区岩溶地质条件下地热能应用能效,通过运用遗传算法优化的反向传播(genetic algorithm-back propagation,GA-BP)神经网络模型预测了夏季系统负荷率低于30%运行工况下地源热泵系统的系统能效比和机组能效比,分析了预测值的预测误差评价指标,验证了GA-BP模型具有较高的预测精度,并应用此模型研究了地源热泵短期能效测试与中长期能效测评的关系。结果表明:GA-BP模型预测的系统能效比COPsys及机组能效比COP与计算值的相对误差为±5%,各项预测误差评价指标均比反向传播神经网络(back propagation neural network,BPNN)模型更小,可见GA-BP模型可用于预测岩溶地质条件下地源热泵系统能效。基于此模型,短期能效测试的最佳时期为一天中14:00—16:00或7、8月累计13 d,且满足机组负荷率达到60%~70%,COP_(sys)及COP预测值可以作为中长期能效比评估,其产生的相对误差在允许的范围内。In order to explore the energy efficiency of geothermal energy application under karst geological conditions in hot-summer and cold-winter zone,the system energy efficiency ratio and unit energy efficiency ratio of ground source heat pump system were predicted based on the optimized back propagation neural network model by genetic algorithm(GA-BP)under the condition that system load rate was below 30%in summer.It was verified that the GA-BP model has higher prediction accuracy by the prediction error evaluation indicators.The relationship between short-term test and medium and long-term evaluation of energy efficiency ratio was studied by the prediction model.The results show that the relative error of the predicted value and the calculated value is±5%.The predicted values of system energy efficiency ratio COP_(sys) and unit energy efficiency ratio COP are predicted by the GA-BP neural network model.All prediction error evaluation indicators are smaller than back propagation neural network(BPNN)prediction model.This shows that GA-BP model can be used to predict energy efficiency of ground source heat pump system under karst geological conditions.The optimal time for conducting short-term monitoring is 14:00—16:00 of the day or 13 days in total in July and August and meet the unit load rate of 60%~70%based on the prediction model.The short-term predicted values of COP_(sys) and COP for are applied to the medium and long-term efficiency ratio evaluation.The relative error of the evaluated energy efficiency ratio is within the allowable range.

关 键 词：地源热泵系统 机组负荷率 遗传算法(GA) 反向传播神经网络(BPNN) 能效比 

分 类 号：TU831.6[建筑科学—供热、供燃气、通风及空调工程]