基于PCA-SOA-ELM的空调系统负荷预测

Load Prediction of Air Conditioning System Based on PCA-SOA-ELM

针对参与需求响应的空调系统负荷预测方法存在预测精度低、预测时间长等问题,提出一种基于主成分分析(principal component analysis,PCA)与海鸥优化算法(seagull optimization algorithm,SOA)优化极限学习机(extreme learning machine,ELM)空调负荷预测模型。通过PCA提取影响空调系统负荷数据的主要特征,建立空调系统ELM负荷预测模型,并采用SOA对模型参数进行迭代寻优。为了验证算法的有效性,以某办公建筑的空调负荷数据为例进行实例分析,实验结果表明:经PCA特征提取后得到包含98.00%原信息的6项主成分,SOA-ELM模型的预测结果与实际值基本吻合,其均方根误差为0.0137,平均绝对百分比误差为0.8392%,决定系数高达0.9910,训练时长为3.482s,相较于其他3种对比模型性能更优。证明了所建模型泛化性能强、预测精度高,能够有效预测空调系统需求响应时段负荷的变化情况。

Aiming at the problems of low prediction accuracy and long prediction time in the load forecasting method of air conditioning system participating in demand response, an air conditioning load forecasting model based on extreme learning machine(ELM) optimized by principal component analysis(PCA) and seagull optimization algorithm(SOA) is proposed. The main characteristics affecting the load data of air conditioning system are extracted through PCA, the ELM load forecasting model of air conditioning system is established, and the model parameters are iteratively optimized by SOA. In order to verify the effectiveness of the algorithm, taking the air conditioning load data of an office building in Xi’an as an example, the experimental results show that six principal components containing 98.00% of the original information are obtained after PCA feature extraction. The prediction results of SOA-ELM model are basically consistent with the actual values, with root mean square error of 0.013 7, average absolute percentage error of 0.839 2%, determination coefficient of 0.991 0 and training time of 3.482 s. Compared with the other three comparison models, the performance of the model is better. It is proved that the model has strong generalization performance and high prediction accuracy, and can effectively predict the load change in the demand response period of the air conditioning system.