福州大学学报（自然科学版）2018，Vol.46Issue(3)：416-421,6.DOI:10.7631/issn.1000-2243.17133
基于K均值小波神经网络的二阶段空调负荷预测
A two-stage prediction for air-conditioning load base on K-means wavelet neural network
摘要
Abstract
In order to improve the accuracy of air conditioning load prediction, a two-stage predictive model based on K-means clustering and wavelet neural networks ( WNN) was proposed. Aiming at the strongly coupling nonlinear characteristics of the air conditioning load data, K-means clustering meth-od was employed to divide the historical load data into several clusters which could reduce the interfer-ence between samples and eliminate the noise in load sample data. Then, the wavelet neural network model was constructed with the training samples of the identified cluster. Based on the simulated data from the DeST platform, the two-stage WNN model was used to predict the hourly air-conditioning load of an office building in South China. Experiment results shown that the proposed model performed significantly higher prediction accuracy than the traditional single WNN model and BP model in terms of the root mean square error ( RMSE) and the mean absolute percentage error ( MAPE) .关键词
空调负荷/预测/K均值聚类算法/小波神经网络Key words
air-conditioning load/forecasting/K-means clustering algorithm/wavelet neural network分类
建筑与水利引用本文复制引用
赵超,郑守锦..基于K均值小波神经网络的二阶段空调负荷预测[J].福州大学学报（自然科学版）,2018,46(3):416-421,6.基金项目
国家自然科学基金资助项目(6080402,61374133) （6080402,61374133）
高校博士点专项科研基金资助项目(20133314120004) （20133314120004）
