自适应加权最小二乘支持向量机的空调负荷预测  

Modeling air-conditioning load forecasting based on adaptive weighted least squares support vector machine

作　　者：赵超[1] 戴坤成[1] 

机构地区：[1]福州大学石油化工学院,福州350108

出　　处：《重庆大学学报（自然科学版）》2016年第1期55-64,共10页Journal of Chongqing University

基　　金：国家自然科学基金资助项目(6080402;61374133);高校博士点专项科研基金(20133314120004)~~

摘　　要：为了提高建筑空调负荷的预测精度,在分析空调负荷主要影响因素的基础上提出了一种基于自适应加权最小二乘支持向量机(AWLS-SVM)的建筑空调负荷预测方法。该方法根据预测误差的统计特性,采用基于改进正态分布加权规则,自适应地赋予每个建模样本不同的权值,以克服异常样本点对模型性能的影响。建模过程中采用粒子群优化(PSO)算法对模型参数进行优化,以进一步提高模型预测精度。基于DeST模拟数据将AWLS-SVM方法应用于南方地区某办公建筑的逐时空调负荷预测中,并与径向基神经网络(RBFNN)模型、LS-SVM模型及WLS-SVM模型作比较,其平均预测绝对误差分别降低了51.84%、13.95%和3.24%,并进一步基于实际空调负荷数据将该方法应用于另一办公建筑的逐日空调负荷预测中。预测结果表明:AWLS-SVM预测的累积负荷误差为4.56MW,亦优于其他3类模型,证明了AWLS-SVM具有较高的预测精度和较好的泛化能力,是建筑空调负荷预测的一种有效方法。To improve the accuracy of building air-conditioning load prediction,we proposed an adaptive weighted least squares support vector machine（AWLS-SVM）algorithm based on analyzing the main related factors.This algorithm uses least squares support vector machine regression to develop the model and obtain the sample data fitting error,and calculates the initial weight according to the statistical characteristics of the prediction errors.The particle swarm optimization（PSO）is applied to obtain the optimal parameters of the AWLS-SVM,so as to improve its forecasting accuracy.Based on the simulated data from the DeST platform,we used the AWLS-SVM model to predict the hourly air-conditioning load of an office building in South China.The simulated results show that the AWLS-SVM has better accuracy compared with RBFNN model,LS-SVM model and WLS-SVM model,with a mean absolute error reduced by 51.84 %,13.95 % and 3.24 %,respectively.We also used the AWLS-SVM method to build a prediction model for the air-conditioning load of another office building based on real measurements.The results show that AWLS-SVM method outperform the other three models in terms of accumulated error,demonstrating that AWLS-SVM is effective for building air-conditioning load prediction.

关 键 词：空调负荷 预测 自适应加权 最小二乘 支持向量机 粒子群优化 

分 类 号：TU831[建筑科学—供热、供燃气、通风及空调工程]