文章摘要
引用本文:
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
投稿时间:2017-04-13  修订日期:2017-07-03
DOI:
中文关键词: 空调负荷  预测  k均值聚类算法  小波神经网络
英文关键词: air-conditioning load  forecasting  k-means clustering algorithm  wavelet neural network
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 Reseach Fund for the Doctoral Program of Higher Education (20133314120004).
作者单位E-mail
赵超 福州大学石油化工学院 seasky76@163.com 
郑守锦 福州大学石油化工学院  
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中文摘要:
      为提高空调负荷预测的精度和稳定性,本文提出了基于k均值小波神经网络的二阶段空调负荷预测方法。根据数据的相似统计分布特征,利用k均值聚类算法将历史负荷数据划分成多个簇类以减小样本数据相关性之间的干扰,消除样本数据中的噪声。进一步,利用每个簇类的训练样本和测试数据集构造相应小波神经网络模型。基于DeST平台模拟数据,将二阶段的小波神经网络预测模型运用于中国南部某办公大楼的逐时空调负荷预测。通过对比均方根误差(RMSE)和平均绝对误差(MAPE),结果表明该模型的预测精度明显优于传统单一的小波神经网络和BP神经网络模型。
英文摘要:
      In order to improve the accuracy of building air conditioning load prediction, a two-stage model based on K-means clustering and wavelet neural networks (WNN) is proposed to predict air conditioning load. According to the similarity statistics, K-means clustering method was employed to divide the historical load data into several clusters which could reduce the interference between samples and eliminate the noise in load sample data. Then, the wavelet neural network of the identified cluster was constructed with the training samples and test data set. Based on the simulated data from the DeST platform, the two-stage WNN model is 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).
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