基于神经网络的建筑能耗混合预测模型

doi:10.3785/j.issn.1008-973X.2022.06.021

浙江大学学报(工学版)

2022, Vol. 56

Issue (6): 1220-1231 DOI: 10.3785/j.issn.1008-973X.2022.06.021

建筑与交通工程

基于神经网络的建筑能耗混合预测模型

于军琪1(

),杨思远2,赵安军1,*(

),高之坤2

1. 西安建筑科技大学建筑设备科学与工程学院，陕西西安 710055
2. 西安建筑科技大学信息与控制工程学院，陕西西安 710055

Hybrid prediction model of building energy consumption based on neural network

Jun-qi YU1(

),Si-yuan YANG2,An-jun ZHAO1,*(

),Zhi-kun GAO2

1. School of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
2. School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China

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摘要：

为了提升建筑能耗预测的精度、鲁棒性和泛化能力，提出树种算法（TSA）优化的径向基函数（RBF）神经网络与长短时记忆（LSTM）神经网络结合的混合预测模型. 采用基于自适应噪声的完全集成经验模态分解算法，将建筑能耗数据分解为1组本征模态函数（IMF）分量和1个残余分量，利用样本熵算法将各分量划分为高频分量和低频分量. 采用最小绝对收缩与选择算子（LASSO）方法进行特征选择. 分别利用TSA算法优化后的RBF模型与LSTM模型对低频分量和高频分量进行预测，并叠加重构得到最终预测结果. 模型评估结果表明，混合预测模型的精度为98. 72%. 相比于RBF、TSA-RBF、LSTM模型，所提模型的预测效果更好，且具有较强的鲁棒性和泛化能力，能够更为有效地用于建筑逐时电力能耗预测.

关键词： 建筑能耗预测; 神经网络; 混合预测模型; 集成经验模态分解; 特征选择

Abstract:

A hybrid prediction model which combining radial basis function (RBF) neural network optimized by tree-seed algorithm (TSA) and long short-term memory (LSTM) neural network was proposed, in order to improve the accuracy, robustness, and generalization ability of building energy consumption prediction. Firstly, the complete ensemble empirical mode decomposition with adaptive noise algorithm was used to decompose the building energy consumption data into a group of intrinsic mode function (IMF) components and a residual component, and the components were divided into high-frequency components and low-frequency components by sample entropy algorithm. Then, least absolute contraction and selection operator (LASSO) method was used for feature selection. Finally, the RBF model optimized by the TSA algorithm and the LSTM model were used to predict the low-frequency components and high-frequency components respectively, and the final prediction results were obtained by superposition and reconstruction. Model evaluation results showed that the accuracy of the hybrid prediction model was 98.72%. Compared with RBF, TSA-RBF, and LSTM models, the prediction effect of the hybrid model is better. Meanwhile, the model has strong robustness and generalization ability, and can be more effectively used for hourly building electricity consumption prediction.

Key words: building energy consumption prediction neural network hybrid prediction model ensemble empirical mode decomposition feature selection

收稿日期: 2021-11-16 出版日期: 2022-06-30

CLC:

TP 183

基金资助: 咸阳机场三期扩建工程绿色能源站系统智能管控咨询与顾问项目（20210103）；国家重点研发计划项目（2017YFC0704100）

通讯作者: 赵安军 E-mail: junqiyu@126.com;zhao_anjun@163.com

作者简介: 于军琪（1969—），男，教授，从事智能建筑及建筑节能研究. orcid.org/0000-0002-6727-2938. E-mail： junqiyu@126.com

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引用本文:

于军琪,杨思远,赵安军,高之坤. 基于神经网络的建筑能耗混合预测模型[J]. 浙江大学学报(工学版), 2022, 56(6): 1220-1231.

Jun-qi YU,Si-yuan YANG,An-jun ZHAO,Zhi-kun GAO. Hybrid prediction model of building energy consumption based on neural network. Journal of ZheJiang University (Engineering Science), 2022, 56(6): 1220-1231.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.06.021 或 https://www.zjujournals.com/eng/CN/Y2022/V56/I6/1220

图 1 CEEMDAN能耗数据分解流程图

图 2 RBF神经网络结构

图 3 TSA算法流程图

图 4 LSTM神经网络结构

图 5 TSA-RBF-LSTM建模过程

表 1 样本数据中的输入变量和输出变量

图 6 建筑能耗样本数据

图 7 建筑能耗数据的CEEMDAN分解结果

表 2 LASSO特征选择结果

图 8 调整参数 $\lambda $与系数向量各分量 ${\beta _i}$的关系

表 3 TSA-RBF-LSTM模型参数设置

图 9 4种预测模型的预测结果对比

图 10 4种模型的预测值与实际值相关性分析

表 4 4种模型预测精度对比

表 5 2种模型预测精度对比

图 11 RBF与LSTM对各阶IMF分量的预测结果对比

图 12 不同优化算法迭代对比

图 13 4种模型的绝对误差箱线图

图 14 泛化能力证明

1	中国建筑节能协会中国建筑能耗研究报告2020[J]. 建筑节能(中英文), 2021, 49 (2): 1- 6 China Association of Building Energy Efficiency China building energy consumption annual report 2020[J]. Building Energy Efficiency, 2021, 49 (2): 1- 6
2	DONG Z, LIU J, LIU B, et al Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification[J]. Energy and Buildings, 2021, 241: 110929 doi: 10.1016/j.enbuild.2021.110929
3	AMASYALI K, EL-GOHARY N M A review of date-driven building energy consumption prediction studies[J]. Renewable and Sustainable Energy Reviews, 2018, 81: 1192- 1205 doi: 10.1016/j.rser.2017.04.095
4	ILBEIGI M, GHOMEISHI M, DEHGHANBANADAKI A Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm[J]. Sustainable Cities and Society, 2020, 61: 102325 doi: 10.1016/j.scs.2020.102325
5	MOHANDES S R, ZHANG X, MAHDIYAR A A comprehensive review on the application of artificial neural networks in building energy analysis[J]. Neurocomputing, 2019, 340: 55- 75 doi: 10.1016/j.neucom.2019.02.040
6	BOROWSKI M, ZWOLIŃSKA K Prediction of cooling energy consumption in hotel building using machine learning techniques[J]. Energies, 2020, 13 (23): 1- 19
7	JOVANOVIĆ R Ž, SRETENOVIĆ A A, ŽIVKOVIĆ B D Ensemble of various neural networks for prediction of heating energy consumption[J]. Energy and Buildings, 2015, 94: 189- 199 doi: 10.1016/j.enbuild.2015.02.052
8	WANG Z, HONG T, PIETTE M A Data fusion in predicting internal heat gains for office buildings through a deep learning approach[J]. Applied Energy, 2019, 240: 386- 398 doi: 10.1016/j.apenergy.2019.02.066
9	邵光成, 章坤, 王志宇, 等基于IABC-RBF神经网络的地下水埋深预测模型[J]. 浙江大学学报: 工学版, 2019, 53 (7): 1323- 1330 SHAO Guang-cheng, ZHANG Kun, WANG Zhi-yu, et al Groundwater depth prediction model based on IABC-RBF neural network[J]. Journal of Zhejiang University: Engineering Science, 2019, 53 (7): 1323- 1330
10	LI K, SU H, CHU J Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: a comparative study[J]. Energy and Buildings, 2011, 43 (10): 2893- 2899 doi: 10.1016/j.enbuild.2011.07.010
11	ZHOU G, MOAYEDI H, BAHIRAEI M, et al Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings[J]. Journal of Cleaner Production, 2020, 254: 120082 doi: 10.1016/j.jclepro.2020.120082
12	HUANG Y, LI C Accurate heating, ventilation and air conditioning system load prediction for residential buildings using improved ant colony optimization and wavelet neural network[J]. Journal of Building Engineering, 2021, 35: 101972 doi: 10.1016/j.jobe.2020.101972
13	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 doi: 10.3390/en13226079
14	DING Y, ZHANG Q, YUAN T, et al Model input selection for building heating load prediction: a case study for an office building in Tianjin[J]. Energy and Buildings, 2018, 159: 254- 270 doi: 10.1016/j.enbuild.2017.11.002
15	LIU M D, DING L, BAI Y L Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction[J]. Energy Conversion and Management, 2021, 233: 113917 doi: 10.1016/j.enconman.2021.113917
16	KIRAN M S TSA: tree-seed algorithm for continuous optimization[J]. Expert Systems with Applications, 2015, 42 (19): 6686- 6698 doi: 10.1016/j.eswa.2015.04.055
17	XI Z, XU A, KOU Y, et al Target maneuver trajectory prediction based on RBF neural network optimized by hybrid algorithm[J]. Journal of Systems Engineering and Electronics, 2021, 32 (2): 498- 516 doi: 10.23919/JSEE.2021.000042
18	CHEN G, TANG B, ZENG X, et al Short-term wind speed forecasting based on long short-term memory and improved BP neural network[J]. International Journal of Electrical Power and Energy Systems, 2022, 134: 107365 doi: 10.1016/j.ijepes.2021.107365
19	TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise [C]// 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague: IEEE, 2011: 4144-4147.
20	TIBSHIRANI R Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodological), 1996, 58 (1): 267- 288 doi: 10.1111/j.2517-6161.1996.tb02080.x
21	HUANG N E, SHEN Z, LONG S R, et al The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society Series A: Mathematical, Physical and Engineering Sciences, 1998, 454 (1971): 903- 995 doi: 10.1098/rspa.1998.0193
22	SUN H, ZHAI W, WANG Y, et al Privileged information-driven random network based non-iterative integration model for building energy consumption prediction[J]. Applied Soft Computing, 2021, 108: 107438 doi: 10.1016/j.asoc.2021.107438
23	RICHMAN J S, MOORMAN J R Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology: Heart and Circulatory Physiology, 2000, 278 (6): H2039- H2049 doi: 10.1152/ajpheart.2000.278.6.H2039
24	施莹, 林建辉, 庄哲, 等基于振动信号时频分解-样本熵的受电弓裂纹故障诊断[J]. 振动与冲击, 2019, 38 (8): 180- 187 SHI Ying, LIN Jian-hui, ZHUANG Zhe, et al Fault diagnosis for pantograph cracks based on time-frequency decomposition and sample entropy of vibration signals[J]. Journal of Vibration and Shock, 2019, 38 (8): 180- 187
25	WU Q, LIN H Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network[J]. Sustainable Cities and Society, 2019, 50: 101657 doi: 10.1016/j.scs.2019.101657
26	SUN Y, HAGHIGHAT F, FUNG B C M A review of the-state-of-the-art in data-driven approaches for building energy prediction[J]. Energy and Buildings, 2020, 221: 110022 doi: 10.1016/j.enbuild.2020.110022
27	GAO Z, YU J, ZHAO A, et al A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine[J]. Energy, 2022, 238: 122073 doi: 10.1016/j.energy.2021.122073
28	韩波, 李衡, 王志波, 等气溶胶光学厚度估测中的LASSO特征选择方法[J]. 武汉大学学报:信息科学版, 2018, 43 (4): 536- 541 HAN Bo, LI Heng, WANG Zhi-bo, et al A feature selection approach via LASSO for aerosol optical thickness estimation[J]. Geomatics and Information Science of Wuhan University, 2018, 43 (4): 536- 541
29	李鱼强, 潘天红, 李浩然, 等近红外光谱LASSO特征选择方法及其聚类分析应用研究[J]. 光谱学与光谱分析, 2019, 39 (12): 3809- 3815 LI Yu-qiang, PAN Tian-hong, LI Hao-ran, et al NIR spectral feature selection using LASSO method and its application in the classification analysis[J]. Spectroscopy and Spectral Analysis, 2019, 39 (12): 3809- 3815
30	EFRON B, HASTIE T, JOHNSTONE I, et al. Least angle regression [EB/OL]. [2021-10-27]. https://hastie.su.domains/Papers/LARS/LeastAngle_2002.pdf.
31	甘敏, 彭晓燕, 彭辉 RBF神经网络参数估计的两种混合优化算法[J]. 控制与决策, 2009, 24 (8): 1172- 1176 GAN Min, PENG Xiao-yan, PENG Hui Two hybrid parameter optimization algorithms for RBF neural networks[J]. Control and Decision, 2009, 24 (8): 1172- 1176 doi: 10.3321/j.issn:1001-0920.2009.08.010
32	YANG Z, MOURSHED M, LIU K, et al A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting[J]. Neurocomputing, 2020, 397: 415- 421 doi: 10.1016/j.neucom.2019.09.110
33	KOSE E Optimal control of AVR system with tree seed algorithm-based PID controller[J]. IEEE Access, 2020, 8: 89457- 89467 doi: 10.1109/ACCESS.2020.2993628
34	DUAN J, WANG P, MA W, et al A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting[J]. International Journal of Electrical Power and Energy Systems, 2022, 134: 107452 doi: 10.1016/j.ijepes.2021.107452
35	KINGMA D P, BA J L. Adam: a method for stochastic optimization [EB/OL]. [2021-10-27]. https://arxiv.org/pdf/1412.6980.pdf.
36	BALAJI E, BRINDHA D, ELUMALAI V K, et al Automatic and non-invasive Parkinson's disease diagnosis and severity rating using LSTM network[J]. Applied Soft Computing, 2021, 108: 107463 doi: 10.1016/j.asoc.2021.107463
37	CHANG Z, ZHANG Y, CHEN W Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform[J]. Energy, 2019, 187: 115804 doi: 10.1016/j.energy.2019.07.134
38	尹爱军, 赵磊, 吴宏钢相关法动平衡校正中的3σ准则误差处理方法[J]. 重庆大学学报, 2013, 36 (10): 22- 26 YIN Ai-jun, ZHAO Lei, WU Hong-gang Error process based on 3σ rule used in balancing by correlation theory[J]. Journal of Chongqing University, 2013, 36 (10): 22- 26
39	胡沛然, 陈少辉权重归一化拉格朗日插值及其空间降尺度应用[J]. 遥感信息, 2019, 34 (6): 63- 71 HU Pei-ran, CHEN Shao-hui Weight normalization based Lagrange interpolation and its application in downscaling[J]. Remote Sensing Information, 2019, 34 (6): 63- 71 doi: 10.3969/j.issn.1000-3177.2019.06.011

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