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
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TU831.6

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Analysis on low load performance prediction of ground source heat pumps in summer based on Genetic Algorithm and BP Neural Network
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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    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(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COPsys及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(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时~16时或7、8月累计13天,且满足机组负荷率达到60%~70%,COPsys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。

    Abstract:

    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 are predicted based on the optimized back propagation neural network model by genetic algorithm (GA-BP) under the condition that system load rate is below 30% in summer. It is 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 is 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 and unit energy efficiency ratio are predicted by the GA-BP neural network model. All prediction error evaluation indices are smaller than 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 system energy efficiency ratio and unit energy efficiency ratio 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.

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董艳芳,朱辉,曾召田,等. 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[J]. 科学技术与工程, 2022, 22(12): 4984-4992.
Dong Yanfang, Zhu Hui, Zeng Zhaotian, et al. Analysis on low load performance prediction of ground source heat pumps in summer based on Genetic Algorithm and BP Neural Network[J]. Science Technology and Engineering,2022,22(12):4984-4992.

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  • 收稿日期:2021-08-16
  • 最后修改日期:2022-04-02
  • 录用日期:2021-12-03
  • 在线发布日期: 2022-05-07
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