平均绝对百分比误差
人工神经网络
均方误差
计算机科学
能源消耗
算法
差异(会计)
数据挖掘
消费(社会学)
能量(信号处理)
机器学习
统计
数学
工程类
社会科学
会计
社会学
电气工程
业务
作者
Lingling Zhang,Jiran Zhang,Panpan Ren,L. K. Ding,Wengang Hao,Chaofeng An,Ao Xu
标识
DOI:10.1016/j.csite.2023.103445
摘要
To gain building energy consumption information during the design phase, the variance analysis to identify significant factors affecting energy consumption in China cold-region office buildings are carried out in this study. Key factors are selected, and prediction models for energy consumption in cold-region office buildings are established using BP and GA-BP algorithms. Three prediction model evaluation indexes are introduced to evaluate the prediction accuracy of the models. The results show that the maximum RMSE of the BP neural network prediction model is 0.498, and the maximum MAPE is 0.797%. Furthermore, the GA algorithm is used to optimize the BP neural network, resulting in a prediction model with a maximum RMSE of 0.359 and a maximum MAPE of 0.289%. The prediction accuracy of the GA-BP algorithm is better than that of the BP algorithm.
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