| 基于支持向量回归和小波包的供热负荷预澳 |
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| 引用本文： | 黎展求 朱栋华 刘冬岩. 基于支持向量回归和小波包的供热负荷预澳[J]. 暖通空调, 2007, 37(2): 1-5 |
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| 作者姓名： | 黎展求 朱栋华 刘冬岩 |
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| 作者单位： | [1]沈阳建筑大学 [2]沈阳辰字建筑有限责任公司 |
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| 基金项目： | 建设部研究开发项目（编号：06-K9-63） |
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| 摘 要： | 通过分析影响热网负荷变化的各种因素，对热负荷数据进行预处理，运用小波包变换对负荷序列进行分解，对各子序列分别建立支持向量回归预测模型，最后通过序列重构，得出预测结果。仿真结果表明，该方法比传统BP神经网络和未作小波包分解的支持向量回归法具有更高的预测精度。
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| 关 键 词： | 供热系统 热负荷预测 支持向量回归 小波包 |
| 修稿时间： | 2006-08-28 |
Heating load prediction for heating systems based on support vector regression and wavelet packet |
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| By Li Zhonqiu, Zhu Donghuo, Liu Dongyon. Heating load prediction for heating systems based on support vector regression and wavelet packet[J]. Journal Heating Ventilating and Airconditioning, 2007, 37(2): 1-5 |
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| Authors: | By Li Zhonqiu Zhu Donghuo Liu Dongyon |
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| Abstract: | Based on an analysis of the factors causing changes in the load of heat-supply network, preprocesses the data of the heating load, and decomposes the load sequences into different scales through the wavelet packet transform. Develops respective support vector regression predicting models for these sub-sequences. After reconstructing sequences, obtains the predicting results. Simulation results show that the method is superior in predicting accuracy to the traditional BP neural network and the support vector regression method without wavelet packet decomposition. |
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| Keywords: | heating system heating load prediction support vector regression wavelet packet |
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