基于奇异谱分析的CNN-BiLSTM短期空调负荷预测模型
CNN-BiLSTM Short-term Air Conditioning Load Prediction Model Based on Singular Spectrum Analysis
杨心宇 1任中俊 2周国峰 3易检长 4何影4
作者信息
- 1. 华北水利水电大学 环境与市政工程学院,郑州 450046;深圳市紫衡技术有限公司,广东 深圳 518071;广东省建筑设备智慧控制与运维工程技术研究中心,广东 深圳 518071
- 2. 深圳市紫衡技术有限公司,广东 深圳 518071;广东省建筑设备智慧控制与运维工程技术研究中心,广东 深圳 518071;同济大学 机械与能源工程学院,上海 200082
- 3. 华北水利水电大学 环境与市政工程学院,郑州 450046
- 4. 深圳市紫衡技术有限公司,广东 深圳 518071;广东省建筑设备智慧控制与运维工程技术研究中心,广东 深圳 518071
- 折叠
摘要
空调负荷的精准预测对建筑空调系统优化控制具有重要意义.为提高空调负荷预测精度,提出了一种基于奇异谱分析(SSA,Singular Spectrum Analysis)的卷积神经网络(CNN,Convolutional Neural Network)和双向长短时记忆网络(BiLSTM,Bidirectional Long Short Term Memory)短期空调负荷预测模型.使用皮尔森相关系数选取与空调负荷高相关性特征.针对空调负荷的波动性和随机性,采用 SSA 将空调负荷分解为多个分量,同时将各个分量带入 CNN-BiLSTM模型进行预测,该模型利用了CNN的特征提取和BiLSTM的双向学习能力,并将各个分量预测结果进行重构.通过不同建筑类型的空调数据对该模型进行验证分析,发现所提出模型在预测办公建筑空调负荷中RMSE、MAPE和MAE为 19.47RT、14.72RT和 2.33%,在预测商业建筑空调负荷中RMSE、MAPE和MAE为 82.5RT、34.21RT和 0.87%.结果表明,所提出的模型具有普适性且精度较高,可进行推广应用.
Abstract
Accurate prediction of air conditioning load is of great significance for optimizing control of building air conditioning systems.In order to improve the accuracy of air conditioning load forecasting,a short-term air conditioning load forecasting model based on Singular Spectrum Analysis(SSA,Singular Spectrum Analysis)Convolutional Neural Network(CNN,Convolutional Neural Network)and Bidirectional Long and Short Memory Network(BiLSTM,Bidirectional Long Short Term Memory)is proposed.Pearson correlation coefficient is applied to select features with high correlation with air conditioning load.In response to the volatility and randomness of air conditioning load,SSA is used to decompose the air conditioning load into multiple components,and each component is brought into the CNN-BiLSTM model for prediction.This model utilizes the feature extraction of CNN and the bidirectional learning ability of BiLSTM,and reconstructs the prediction results of each component.The model is validated and analyzed using air conditioning data from different building types,and it is found that the RMSE,MAPE,and MAE of the proposed model are 19.47RT,14.72RT,and 2.33%in predicting office building air conditioning loads,and 82.5RT,34.21RT,and 0.87%in predicting commercial building air conditioning loads.The results indicate that the proposed model has universality and high accuracy,and can be widely applied.
关键词
空调负荷预测/双向长短时记忆网络/奇异谱分析/卷积神经网络Key words
air conditioning load prediction/Bidirectional Long and Short Term Memory Network(BiLSTM)/Singular Spectrum Analysis(SSA)/Convolutional Neural Network(CNN)引用本文复制引用
基金项目
厦门市建设科技计划(XJK2022-1-6)
出版年
2024
NETL
NSTL
万方数据