基于支持向量回归和小波包的供热负荷预测
Heating load prediction for heating systems based on support vector regression and wavelet packet
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。
关键词：供热系统 热负荷预测 支持向量回归 小波包
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.
Keywords:heating system, heating load prediction, support vector regression, wavelet packet