| 几种集中供热负荷预测模型对比 |
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| 引用本文： | 于晓娟,顾吉浩,齐承英,孙春华.几种集中供热负荷预测模型对比[J].暖通空调,2019(2):96-99. |
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| 作者姓名： | 于晓娟 顾吉浩 齐承英 孙春华 |
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| 作者单位： | 河北工业大学 |
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| 基金项目： | "十三五"国家重点研发计划"既有公共建筑综合性能提升与改造关键技术"(编号:2016YFC070070702) |
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| 摘 要： | 为了精确地预测供热负荷,在预测模型中增加了室内温度影响因子,并采用多元线性回归(MLR)、BP神经网络和基于网格搜索优化支持向量机回归(GS-SVR)的方法,对未来7 d的供热负荷进行了预测。研究结果表明,GS-SVR预测模型的精度最高,其预测精度明显优于MLR和BP神经网络,可用于指导工程实践。
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| 关 键 词： | 集中供热 热负荷预测 多元线性回归 BP神经网络 支持向量机回归 |
Comparison of several centralized heating load forecasting models |
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| Yu Xiaojuan,Gu Jihao,Qi Chengying,Sun Chunhua.Comparison of several centralized heating load forecasting models[J].Journal Heating Ventilating and Airconditioning,2019(2):96-99. |
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| Authors: | Yu Xiaojuan Gu Jihao Qi Chengying Sun Chunhua |
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| Affiliation: | (Hebei University of Technology,Tianjin,China) |
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| Abstract: | In order to improve the accuracy of the heating load prediction, adds indoor temperature factor into the prediction models. In addition, applies multivariable linear regression(MLR), BP neural network and support vector machine regression based on grid search(GS-SVR) to the heating load prediction in the next seven days. The obtained results show that GS-SVR model prediction is more accurate than MLR and BP neural network, which can be adopted to guide the engineering practice. |
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| Keywords: | central heating heating load prediction multivariable linear regression back propagation (BP)neural network support vector machine regression |
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