基于支持向量机的建筑物空调负荷预测模型
Building air conditioning load prediction model based on support vector machine
摘要：
建立了基于支持向量机（SVM）理论的建筑物空调负荷预测模型。对广州地区某办公楼夏季不同月份的逐时空调负荷，分别用SVM模型和BP神经网络模型进行了训练和预测。仿真结果表明，SVM模型具有更高的预测精度和更好的泛化能力，是建筑物空调负荷预测的一种有效方法。
关键词：空调负荷,预测,支持向量机,BP神经网络
Abstract：
Based on the theory of support vector machine (SVM), establishes a prediction model for building air conditioning load. An SVM model and back-propagation （BP） neural network model are both used for the hourly air conditioning load prediction of an office building in summer months in Guangzhou area. The simulation results show that the SVM model shows better accuracy and generalization ability, and is effective for building air conditioning load prediction.
Keywords:air conditioning load, prediction, support vector machine, back-propagation neural network