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计算机系统应用英文版:2019,28(8):142-147

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基于GIHCMAC神经网络的建筑电负荷预测方法

吴盼红^1,2, 段培永³, 丁绪东^1,2, 尹春杰^1,2, 姬晓娃^1,2, 邱钟^1,2

(1.山东建筑大学信息与电气工程学院, 济南 250101;2.山东省智能建筑技术重点实验室, 济南 250101;3.山东师范大学信息科学与工程学院, 济南 250358)

Building Electric Load Prediction Based on Improved GIHCMAC Neural Network

WU Pan-Hong^1,2, DUAN Pei-Yong³, DING Xu-Dong^1,2, YIN Chun-Jie^1,2, JI Xiao-Wa^1,2, QIU Zhong^1,2

(1.School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China;2.Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China;3.School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China)

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本文已被：浏览 1510次下载 2352次
Received:December 18, 2018 Revised:January 10, 2019

中文摘要: 随着能源供应与经济快速发展的矛盾日益加剧，建筑节能成为可持续发展战略的一个关键环节，研究一种快速、精准的建筑用电量预测方法是实现建筑节能优化控制的重要前提.本文将遗传算法与蚁群聚类算法相融合，对基于聚类的IHCMAC （Improvement Hyperball CMAC）神经网络的网络节点进行改进，将GIHCMAC （Genetic Algorithm Ant Colony Clustering Algorithm based on IHCMAC）作为建筑电力负荷预测模型，对潍坊某一办公建筑用电负荷进行预测.研究结果表明，该预测模型迭代次数最小、准确度较高，其迭代次数、训练误差、泛化误差分别为9、0.0045、0.0014，较IHCMAC、KHCMAC （K-means Hyperball CMAC）、IKHCMAC （Improvement K-means Hyperball CMAC）模型的收敛速度更快，精度更高，泛化能力更强.

中文关键词: 建筑电负荷预测 IHCMAC神经网络 GIHCMAC神经网络遗传算法蚁群聚类算法

Abstract:With the increasing contradiction between energy supply and rapid economic development, building energy conservation has become a key link in sustainable development strategy. It is an important prerequisites for optimal control of building energy conservation that fast and accurate method research for predicting building electricity consumption. In this study, genetic algorithms and ant colony clustering algorithms are combined to improve the network node of IHCMAC (Improvement Hyperball CMAC) neural network based on clustering. As a building power load forecasting model, GIHCMAC (Genetic Algorithm Ant Colony Clustering Algorithm based on IHCMAC) is used to predict the electrical load of an office building in Weifang. The research results show that the prediction model has the smallest number of iterations and high accuracy. Its iteration number, training error, and generalization error are 9, 0.0045, and 0.0014 respectively. Compared with IHCMAC, KHCMAC (K-means Hyperball CMAC) and IKHCMAC (Improvement K-means Hyperball CMAC) model, GIHCMAC has faster convergence speed, higher accuracy, and better generalization.

keywords: prediction of electric load of building IHCMAC neural work GIHCMAC neural work genetic algorithm ant colony clustering algorithm work

文章编号： 中图分类号： 文献标志码：

基金项目:国家自然科学基金（61773246，61374187）

Author Name	Affiliation	E-mail
WU Pan-Hong	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China
DUAN Pei-Yong	School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
DING Xu-Dong	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China	xdding@sdjzu.edu.cn
YIN Chun-Jie	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China
JI Xiao-Wa	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China
QIU Zhong	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China

Author Name	Affiliation	E-mail
WU Pan-Hong	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China
DUAN Pei-Yong	School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
DING Xu-Dong	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China	xdding@sdjzu.edu.cn
YIN Chun-Jie	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China
JI Xiao-Wa	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China
QIU Zhong	School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China

引用文本：
吴盼红,段培永,丁绪东,尹春杰,姬晓娃,邱钟.基于GIHCMAC神经网络的建筑电负荷预测方法.计算机系统应用,2019,28(8):142-147
WU Pan-Hong,DUAN Pei-Yong,DING Xu-Dong,YIN Chun-Jie,JI Xiao-Wa,QIU Zhong.Building Electric Load Prediction Based on Improved GIHCMAC Neural Network.COMPUTER SYSTEMS APPLICATIONS,2019,28(8):142-147