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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        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
变量名称 符号 单位
流动人员数量 β 1 人/m 2
照明使用 β 2 kW·h
电气使用 β 3 kW·h
室外干球温度 β 4
室外湿球温度 β 5
室外露点温度 β 6
室外相对湿度 β 7 %
风速 β 8 m/s
风向 β 9 (°)
太阳辐照度 β 10 W/m 2
上一时刻太阳辐照度 β 11 W/m 2
上一时刻能耗 β 12 kW·h
能耗 W S kW·h
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
IMF分量 所选特征
IMF 1 β 1β 2β 3β 4β 7β 8β 10β 11β 12
IMF 2 β 1β 2β 3β 4β 7β 11β 12
IMF 3 β 1β 2β 3β 4β 5β 7β 11β 12
IMF 4 β 1β 2β 3β 4β 11β 12
IMF 5 β 1β 2β 3β 11β 12
IMF 6 β 1β 3β 11β 12
IMF 7 β 1β 3β 11β 12
IMF 8 β 1β 2β 3β 4β 7β 11β 12
IMF 9 β 1β 2β 3β 4β 7β 11β 12
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
子算法 参数符号 说明 数值
TSA N pop 种群规模 50
ST 搜索趋势 0. 1
d 优化维数 n+( n+1) m
M iter 最大迭代次数 500
RBF n 输入层神经元数量 特征数
m 隐含层神经元数量 n
c 中心取值范围 [?5.0,5.0]
b 基宽取值范围 [0. 01,10.00]
w 权值取值范围 [?2.0,2.0]
LSTM N IU 输入层神经元数量 特征数
N HU 隐含层神经元个数 16 N IU
R IL 初始学习率 0. 005
M E 最大训练次数 200
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
预测模型 E RMSE E MAPE/%
RBF 26.5549 5.37
TSA-RBF 15.7670 2.96
LSTM 18.6225 3.36
TSA-RBF-LSTM 6.8925 1.28
表 4  4种模型预测精度对比
预测模型 是否CEEMDAN分解 E RMSE E MAPE/%
RBF 27.254 7 5.41
42.362 5 8.79
LSTM 19.392 1 3.32
28.745 9 5.68
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
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