重庆大学学报Issue(1)：55-64,10.DOI:10.11835/j.issn.1000-582X.2016.01.008
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
摘要
Abstract
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Key words
air-conditioning load/forecasting/adaptive weighted/least squares/support vector machines/particle swarm optimization (PSO)分类
建筑与水利引用本文复制引用
赵超,戴坤成..自适应加权最小二乘支持向量机的空调负荷预测[J].重庆大学学报,2016,(1):55-64,10.基金项目
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）。Supported by the National Natural Science Foundation of China （6080402，61374133）and Specialized Research Fund for the Doctoral Program of Higher Education （20133314120004）． （）
