基于小波分解和支持向量机的办公建筑空调负荷预测  

Office building air conditioning load forecasting method based on wavelet decomposition and support vector machine

作　　者：周璇[1] 刘庆典 闫军威[1] 

机构地区：[1]华南理工大学

出　　处：《暖通空调》2016年第5期114-118,107,共6页Heating Ventilating & Air Conditioning

基　　金：广东省科技厅支撑项目"中央空调运行能效在线检测与优化控制"(编号:2011B061200043)

摘　　要：提出了一种基于小波分解-支持向量机(WD-SVM)的办公建筑空调负荷预测建模方法,利用小波分解将具有较强随机性和非线性的空调负荷信号进行分解,然后利用支持向量机对分解后不同频率下的分支数据进行预测建模,从很大程度上避免了由于训练样本不完备而导致的支持向量机预测精度波动。仿真结果表明,WD-SVM方法的预测精度评价指标EEP比单支持向量机法降低33.6%,预测精度有明显提升。Proposes an air conditioning load forecasting modeling method based on wavelet decomposing and support vector machine （SVM）. Decomposes strongly stochastic and nonlinear air conditioning load series into different frequency sub-series. Each of sub-series is modeled separately using support vector regression. This method largely avoids the fluctuation of SVM forecasting accuracy caused by the incompleteness of training data. Simulation results show comparing with that of the expected error percentage is reduced by 33.6% comparing with that of the simple SVM method, and the accuracy is improved significantly.

关 键 词：小波分解 支持向量机 办公建筑 空调负荷预测 期望误差百分比EEP 

分 类 号：TU831.2[建筑科学—供热、供燃气、通风及空调工程]