基于神经网络的地铁车站冷负荷预测对比研究
Research on Cooling Load Prediction of Subway Stations Based on Backpropagation Neural Network and Convolutional Neural Network
刘舸争 1李舒宏 2胡远洋 2李新美 3李媛 1于德涌 1王珩 1刘守超 3陈诚3
作者信息
- 1. 深圳地铁建设集团有限公司 深圳 518000
- 2. 东南大学能源与环境学院 南京 210096
- 3. 南京福加自动化科技有限公司 南京 210046
- 折叠
摘要
冷负荷预测是建筑节能的基础,然而输入的不同会影响神经网络的预测精度,且对新建筑进行负荷预测时需要数据的积累.为得到地铁站冷负荷预测的最佳输入并评估数据库迁移预测的可行性,基于实测数据,以常用的时间变量、气象变量和历史负荷作为待选输入,比较了反向传播神经网络和卷积神经网络在不同输入下及数据库逐步更新时的预测精度.结果表明:最佳输入变量与冷负荷的皮尔逊相关系数需大于 0.5;另一方面,同类型建筑在预测初期可以基于数据库逐步替换实现预测,预测精度随着数据库的更新逐渐提升,且卷积神经网络表现出更好的预测表现.
Abstract
Cooling load prediction forms the foundation of building energy conservation.However,the variation in input variables significantly influences the predictive accuracy of neural networks.Furthermore,predicting loads for new buildings necessitates the accumulation of data.This study aims to determine the optimal inputs for predicting subway station cooling loads and evaluate the feasibility of database migration predictions.Leveraging empirical data and considering commonly used time variable,meteorological factors,and historical loads as potential inputs,this research compares the predictive accuracy of Backpropagation Neural Network(BPNN)and Convolutional Neural Network(CNN)under different inputs and during progressive database updates.Results indicate that the optimal input variables for cooling load prediction should exhibit Pearson correlation coefficient with the load greater than 0.5.Moreover,for similar building types,initial predictions can be made by gradually replacing the database,showcasing an enhancement in predictive accuracy with each update.Notably,the CNN demonstrate superior prediction performance throughout this process.
关键词
负荷预测/地铁站/神经网络/输入组合/数据库更新Key words
Load prediction/Subway station/Neural network/Input combination/Database update引用本文复制引用
出版年
2024
NETL
NSTL
万方数据