Yang Guo , Mengyao Chen , Hong Wang , Pan Chu , Yingjie Sheng , Hao Li
{"title":"基于CEEMDAN和WTCN-GRU的中央空调负荷混合预测模型","authors":"Yang Guo , Mengyao Chen , Hong Wang , Pan Chu , Yingjie Sheng , Hao Li","doi":"10.1016/j.ijrefrig.2025.05.011","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate load prediction is an important foundation for the energy-saving and optimized control of central air conditioning systems, and it is crucial for energy conservation and emissions reduction in buildings. To address the issues of low accuracy in existing load forecasting models, this paper proposes a load prediction method of air conditioning based on hybrid model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and an improved Temporal Convolutional Network (WTCN) -Gated Recurrent Unit (GRU). Firstly, Pearson correlation analysis is used to select highly correlated influencing factors as feature inputs. CEEMDAN is then applied to decompose and reconstruct the original data to mitigate data non-stationarity and improve data quality. Secondly, the first-layer convolution of each residual block in the TCN is improved to enhance feature extraction capability. Thirdly, the gating mechanism in the GRU is utilized to handle the temporal relationships in the data and predict the air conditioning load. Finally, experiments are conducted using the central air conditioning load data from office building for validation. The results show that the proposed model outperforms other benchmark models, significantly improving the accuracy of building air conditioning load forecasting. It holds promising application prospects in optimizing building energy consumption control.</div></div>","PeriodicalId":14274,"journal":{"name":"International Journal of Refrigeration-revue Internationale Du Froid","volume":"176 ","pages":"Pages 373-385"},"PeriodicalIF":3.5000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid forecasting model for central air conditioning load based on CEEMDAN and WTCN-GRU\",\"authors\":\"Yang Guo , Mengyao Chen , Hong Wang , Pan Chu , Yingjie Sheng , Hao Li\",\"doi\":\"10.1016/j.ijrefrig.2025.05.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate load prediction is an important foundation for the energy-saving and optimized control of central air conditioning systems, and it is crucial for energy conservation and emissions reduction in buildings. To address the issues of low accuracy in existing load forecasting models, this paper proposes a load prediction method of air conditioning based on hybrid model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and an improved Temporal Convolutional Network (WTCN) -Gated Recurrent Unit (GRU). Firstly, Pearson correlation analysis is used to select highly correlated influencing factors as feature inputs. CEEMDAN is then applied to decompose and reconstruct the original data to mitigate data non-stationarity and improve data quality. Secondly, the first-layer convolution of each residual block in the TCN is improved to enhance feature extraction capability. Thirdly, the gating mechanism in the GRU is utilized to handle the temporal relationships in the data and predict the air conditioning load. Finally, experiments are conducted using the central air conditioning load data from office building for validation. The results show that the proposed model outperforms other benchmark models, significantly improving the accuracy of building air conditioning load forecasting. It holds promising application prospects in optimizing building energy consumption control.</div></div>\",\"PeriodicalId\":14274,\"journal\":{\"name\":\"International Journal of Refrigeration-revue Internationale Du Froid\",\"volume\":\"176 \",\"pages\":\"Pages 373-385\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Refrigeration-revue Internationale Du Froid\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0140700725001999\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Refrigeration-revue Internationale Du Froid","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140700725001999","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Hybrid forecasting model for central air conditioning load based on CEEMDAN and WTCN-GRU
Accurate load prediction is an important foundation for the energy-saving and optimized control of central air conditioning systems, and it is crucial for energy conservation and emissions reduction in buildings. To address the issues of low accuracy in existing load forecasting models, this paper proposes a load prediction method of air conditioning based on hybrid model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and an improved Temporal Convolutional Network (WTCN) -Gated Recurrent Unit (GRU). Firstly, Pearson correlation analysis is used to select highly correlated influencing factors as feature inputs. CEEMDAN is then applied to decompose and reconstruct the original data to mitigate data non-stationarity and improve data quality. Secondly, the first-layer convolution of each residual block in the TCN is improved to enhance feature extraction capability. Thirdly, the gating mechanism in the GRU is utilized to handle the temporal relationships in the data and predict the air conditioning load. Finally, experiments are conducted using the central air conditioning load data from office building for validation. The results show that the proposed model outperforms other benchmark models, significantly improving the accuracy of building air conditioning load forecasting. It holds promising application prospects in optimizing building energy consumption control.
期刊介绍:
The International Journal of Refrigeration is published for the International Institute of Refrigeration (IIR) by Elsevier. It is essential reading for all those wishing to keep abreast of research and industrial news in refrigeration, air conditioning and associated fields. This is particularly important in these times of rapid introduction of alternative refrigerants and the emergence of new technology. The journal has published special issues on alternative refrigerants and novel topics in the field of boiling, condensation, heat pumps, food refrigeration, carbon dioxide, ammonia, hydrocarbons, magnetic refrigeration at room temperature, sorptive cooling, phase change materials and slurries, ejector technology, compressors, and solar cooling.
As well as original research papers the International Journal of Refrigeration also includes review articles, papers presented at IIR conferences, short reports and letters describing preliminary results and experimental details, and letters to the Editor on recent areas of discussion and controversy. Other features include forthcoming events, conference reports and book reviews.
Papers are published in either English or French with the IIR news section in both languages.