基于长短期记忆网络的短期空调冷负荷预测
摘要：提出了一种基于长短期记忆网络(LSTM)的短期空调冷负荷预测模型，仅采用历史负荷数据预测未来1 d的逐时冷负荷。通过与传统的BP神经网络模型进行对比，验证其准确性。为了进一步提高模型预测精度，对网络结构(包括输入层、输出层及隐含层神经元数量)与预测策略进行了优化，获得最优的预测模型。结果表明，基于LSTM的预测模型可实现准确的负荷预测，且与BP神经网络模型相比，预测精度更高，均方根误差和均方根误差的变异系数分别降低116 kW和5.42%。对LSTM模型优化的结果表明：利用历史7 d负荷数据预测未来1 d的逐时空调负荷是最佳的输入输出组合选择；隐含层神经元数量为60时，模型精度较高且较为稳定；采用分步输出的预测策略能降低峰值负荷时的预测误差，提高负荷预测精度。
关键词：空调冷负荷预测预测模型长短期记忆网络神经网络神经元均方根误差
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参考文献参考文献
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[1] 彭献永，王波波，张骞．基于大数据的我国公共建筑空调负荷预测的研究进展[J]．安徽建筑，2019,26(4):11-14．
[2] WEI Y,ZHANG X,SHI Y,et al.A review of datadriven approaches for prediction and classification of building energy consumption[J].Renewable and sustainable energy reviews,2018,82:1027-1047．
[3] ROBINSON C,DILKINA B,HUBBS J,et al.Machine learning approaches for estimating commercial building energy consumption[J].Applied energy,2017,208:889-904．
[4] SMARRA F,JAIN A,DE RUBEIS T,et al.Datadriven model predictive control using random forests for building energy optimization and climate control[J].Applied energy,2018,226:1252-1272．
[5] QIANG G,ZHE T,YAN D,et al.An improved office building cooling load prediction model based on multivariable linear regression[J].Energy and buildings,2015,107:1252-1272．
[6] 樊丽军．基于多元线性回归模型的建筑能耗预测与建筑节能分析[J]．湘潭大学自然科学学报，2016,38(1):123-126．
[7] 聂子航，于学军．基于多元线性回归的办公建筑电力能耗评估预测模型的设计[J]．电子设计工程，2016,24(3):40-43,46．
[8] LUO X J.A novel clustering-enhanced adaptive artificial neural network model for predicting dayahead building cooling demand[J].Journal of building engineering,2020,32:101504．
[9] 刘玮，王智伟，袁照旺，等．人工神经网络在商场建筑物冷负荷预测中的应用[J]．建筑科学与工程学报，2004,4(4):58-61．
[10] 常晓珂，夏凊，晋欣桥．基于BP改进模型的空调系统负荷预测[J]．建筑热能通风空调，2003(1):5-7,10．
[11] FAN C,WANG J,GANG W,et al.Assessment of deep recurrent neural network-based strategies for short-term building energy predictions[J].Applied energy,2019,236:700-710．
[12] FU G.Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system[J].Energy,2018,148:269-282．
[13] 于晓娟，顾吉浩，齐承英，等．几种集中供热负荷预测模型对比[J]．暖通空调，2019,49(2):96-99．
[14] 孙育英，王丹，王伟，等．空调运行负荷预测方法的研究综述[J]．建筑科学，2016,32(6):142-150．
[15] LIU H,MI X,LI Y.Smart deep learning based wind speed prediction model using wavelet packet decomposition,convolutional neural network and convolutional long short term memory network[J].Energy conversion and management,2018,166:120-131．
[16] 祁鑫，王福忠，张丽，等．基于SVD-LSTM的高校学生宿舍空调负荷预测[J]．电子科技，2020,33(11):1-9．
[17] 何超．基于深度强化学习的建筑节能方法研究[D]．苏州：苏州科技大学，2019:1-61．
[18] 钱青，唐桂忠，张广明，等．基于AR-DBN的建筑分项能耗短期预测[J]．计算机工程，2019,45(6):290-296．
[19] SENDRA-ARRANZ R,GUTIÉRREZ A.A long short-term memory artificial neural network to predict daily HVAC consumption in buildings[J].Energy and buildings,2020,216:109952．
[20] 廖文强，王江宇，陈焕新，等．基于长短期记忆神经网络的暖通空调系统能耗预测[J]．制冷技术，2019,39(1):45-50,4．
[21] CHEN Y,TONG Z,ZHENG Y,et al.Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildings[J].Journal of cleaner production,2020,254:119866．
[22] WANG Z,HONG T,PIETTE M A.Building thermal load prediction through shallow machine learning and deep learning[J].Applied energy,2020,263:114683．
[23] 李香龙，马龙飞，赵向阳，等．基于LSTM网络的时间多尺度电采暖负荷预测[J]．电力系统及其自动化学报，2021,33(4):1-5.
Short-term air conditioning cooling load prediction based on long-short-term memory network
Abstract: This paper proposes a short-term air conditioning cooling load prediction model based on the long-short-term memory network(LSTM), which only uses historical load data to predict hourly cooling load in the next day. By comparing with the back-propagation neural network(BPNN) model, the accuracy of the model is testified. In order to further improve the prediction accuracy of the model, this study optimizes the network structure including the number of neurons in input layer, output layer and hidden layer and prediction strategies to obtain the optimal prediction model. The results show that the prediction model based on the LSTM can forecast cooling load accurately, and perform better than BPNN model, and the root mean squared error and its coefficient of variation reduce 116 kW and 5.42%, respectively. The optimized results show that the best combination of input and output is to use historical seven-day load data to predict the hourly air conditioning load of the next day. When the number of neurons in the hidden layer is 60, the prediction accuracy of the model is higher and more stable. The stepwise output prediction strategy can reduce the prediction error at the peak load and is helpful to improve the prediction accuracy.
Keywords: air conditioning; cooling load prediction; prediction model; long-short-term memory network; neural network; neuron; root mean squared error;
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