基于AP-BP神经网络的建筑能耗分析与预测  

Analysis and prediction of the energy consumption of buildings based on a back propagation-affinity propagation(A P-BP)neural network

作　　者：黄晓一 赵田[2] 楚纪正[1] HUANG XiaoYi;ZHAO Tian;CHU JiZheng(College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029;Beijing Aerospace Command Center,Beijing 100094,China)

机构地区：[1]北京化工大学信息科学与技术学院,北京100029 [2]北京航天飞行控制中心,北京100094

出　　处：《北京化工大学学报（自然科学版）》2020年第4期101-107,共7页Journal of Beijing University of Chemical Technology(Natural Science Edition)

基　　金：国家自然科学基金(21676012);中央高校基本科研业务费(XK1802-4)。

摘　　要：建筑行业对能源的节约是当前节约能耗的重要途径之一,在对能源浪费现状和建筑物能源绩效调研和分析的基础上,提出了一种基于affinity propagation(AP)聚类的back propagation(BP)神经网络建筑能耗分析与预测方法。通过AP聚类算法对影响建筑能耗的多维因素进行聚类分析,得到影响建筑能耗的主要因素并作为BP神经网络的输入,然后将建筑能耗指标热负荷和冷负荷作为BP神经网络的输出,建立建筑能耗分析与预测模型。均方根误差(RMSE)和平均相对泛化误差(ARGE)评价指标分析结果表明,本文所提方法对能耗值预测的拟合程度优于经典的BP神经网络,且通过建筑能耗输入输出的结构调整能够节约能耗,提高能效。Energy saving is one of the most important ways to reduce the energy consumption of buildings.Based on an analysis and investigation of energy waste and the energy efficiency of buildings,a back propagation(BP)neural network based on the affinity propagation(AP)clustering algorithm(A P-BP)for analysis and prediction of the energy consumption of buildings is proposed.By means of the AP clustering algorithm,the key factors affecting the energy consumption of buildings can be obtained,and these are used as inputs of the BP neural network.The thermal load and the cold load of buildings are used as outputs of the BP neural network.Finally,a model for analyzing and predicting the energy consumption of the building is obtained.By evaluating the root mean square error(RMSE)and the mean relative generalization error(ARGE),the experimental results show that the proposed method is better than the classical BP neural network in terms of the degree of fitting of the energy consumption prediction.Using the A P-BP can save energy and improve energy efficiency by adjusting the inputs and outputs of buildings.

关 键 词：BP神经网络 AP聚类 能耗分析 能耗预测 建筑行业 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]