基于PCA-PSO-BP神经网络的住宅供热逐时负荷预测
摘要：为了准确预测供热负荷，提出了一种基于主成分分析法和粒子群优化算法改进的BP神经网络(PCA-PSO-BP)预测模型。首先利用主成分分析法融合影响热负荷的特征指标，消除指标之间的冗余性和相关性；同时采用粒子群算法优化BP神经网络的初始权值和阈值，克服了BP神经网络容易陷入局部最优的缺陷，提高了BP神经网络的预测精度。基于北京某居住建筑供热系统的实际运行数据，对模型的性能进行了验证。仿真结果表明，改进的模型预测精度提高了4.07%。
关键词：供热负荷预测模型BP神经网络主成分分析PCA)粒子群算法PSO)特征变量
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[2] 李峥嵘，李璨君，朱晗.基于SVR空调负荷预测模型的参数优化研究[J].建筑节能(中英文),2021,49(2):43- 48.
[3] GAO X,QI C,XUE G,et al.Forecasting the heat load of residential buildings with heat metering based on CEEMDAN-SVR[J].Energies,2020,13(22):6079- 6079.
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[5] GOUDA M M,DANAHER S,UNDERWOOD C P.Application of an artificial neural network for modelling the thermal dynamics of a building's space and its heating system[J].Mathematical modelling of systems,2002,8(3):333- 344.
[6] 郝有志，李德英，郝斌.基于神经网络的供热计量系统热负荷短期预测[J].暖通空调，2003,33(6):105- 107.
[7] KATO K,SAKAWA M,ISHIMARU K,et al.Heat load prediction through recurrent neural network using particle swarm optimization in district heating and cooling systems[C]//2008 IEEE International Conference on Systems,Man and Cybernetics,2008:23279749.
[8] DONG W,LONG Z,XI L.Research on neural network based real-time thermal load prediction[C]//2010 International Conference on Electrical and Control Engineering,2010:1718- 1720.
[9] XUAN Z,LIU Q D,LIU G Q,et al.Multi-variable time series forecasting for thermal load of air-conditioning system on SVR[C]//Proceedings of the 34th Chinese Control Conference(CCC 2015),2015:8276- 8280.
[10] JIE P,ZHU N,LI D.Operation optimization of existing district heating systems[J].Applied thermal engineering,2015,78:278- 288.
[11] WOJDYGA K.An influence of weather conditions on heat demand in district heating systems[J].Energy and buildings,2008,40(11):2009- 2014.
[12] 狄洪发，陈盛，江亿.供暖收费与热网调节[J].区域供热，2000,88(5):4- 8.
[13] 武松，潘发明.SPSS统计分析大全[M].北京：清华大学出版社，2014:334- 335.
[14] 焦李成，杨淑媛，刘芳，等.神经网络七十年：回顾与展望[J].计算机学报，2016,39(8):1697- 1716.
[15] 徐丽娜.神经网络控制[M].北京：电子工业出版社，2003:18- 21.
[16] QIN Q,FENG Y W,LI F.Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network[J].Journal of systems engineering and electronics,2018,29(6):1317- 1326.
[17] 郭涛，魏勇，熊杰.PSO-BP神经网络预测模型在智慧课堂中的应用研究[J].计算机时代，2021,345(3):52- 56.
Hourly heating load forecasting for residences based on PCA-PSO-BP neural network
Abstract: In order to accurately forecast the heating load, an improved BP neural network forecasting model based on principal component analysis(PCA) and particle swarm optimization(PSO) is proposed. Firstly, the PCA is used to fuse the characteristic indexes affecting the heating load to eliminate the redundancy and correlation between the indexes. At the same time, the PSO is used to optimize the initial weights and thresholds of BP neural network, which overcomes the defect that BP neural network is easy to fall into local optimization and improves the prediction accuracy of BP neural network. Based on the actual operation data of the heating system of a residential building in Beijing, the performance of the model is verified. The simulation results show that the forecasting accuracy of the improved model is improved by 4.07%.
Keywords: heating load; forecasting model; BP neural network; principal component analysis(PCA); particle swarm optimization(PSO); characteristic variable;
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