A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China
The high energy consumption characteristic of multi-span glass greenhouses significantly limits their widespread adoption. Optimizing energy strategies and implementing predictive models for energy consumption are essential for more efficient management and reduction of greenhouse operational energy...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525000590 |
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| _version_ | 1823861101260963840 |
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| author | Yangda Chen Aiqun Bao Yapeng Li Yingfeng Xiang Wanlong Cai Zhaoqiang Xia Jialei Li Mingyang Ning Jing Sun Haixi Zhang Xianpeng Sun Xiaoming Wei |
| author_facet | Yangda Chen Aiqun Bao Yapeng Li Yingfeng Xiang Wanlong Cai Zhaoqiang Xia Jialei Li Mingyang Ning Jing Sun Haixi Zhang Xianpeng Sun Xiaoming Wei |
| author_sort | Yangda Chen |
| collection | DOAJ |
| description | The high energy consumption characteristic of multi-span glass greenhouses significantly limits their widespread adoption. Optimizing energy strategies and implementing predictive models for energy consumption are essential for more efficient management and reduction of greenhouse operational energy costs. Existing methods rely on single-element approaches to predict energy consumption, but this reliance often results in severe performance limitations. Therefore, energy consumption prediction methods that incorporate multi-source data are necessary. To overcome the challenges concerning heterogeneity, redundancy, and interdependence among different data sources, this paper proposed a novel energy consumption method that integrates multi-source data through feature engineering and machine learning techniques, which significantly enhances the efficiency of data utilization and improves prediction accuracy. The final experimental results indicated that the proposed energy consumption prediction method demonstrates excellent performance with high prediction accuracy (R² = 0.9388) and low computational resource consumption (runtime = 926.91s), outperforming other models. Finally, the model was interpreted using SHAP (SHapley Additive exPlanations) values, and ablation experiments were conducted to validate the effectiveness of the proposed method in greenhouse energy consumption prediction, thereby providing strong support for greenhouse management. |
| format | Article |
| id | doaj-art-5fc961aad6614a94afe9e8f5eb807690 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-5fc961aad6614a94afe9e8f5eb8076902025-02-10T04:35:29ZengElsevierSmart Agricultural Technology2772-37552025-03-0110100825A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in ChinaYangda Chen0Aiqun Bao1Yapeng Li2Yingfeng Xiang3Wanlong Cai4Zhaoqiang Xia5Jialei Li6Mingyang Ning7Jing Sun8Haixi Zhang9Xianpeng Sun10Xiaoming Wei11College of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi Province, ChinaCollege of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi Province, ChinaCollege of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi Province, ChinaCollege of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi Province, ChinaSchool of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi Province, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi Province, ChinaCollege of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi Province, ChinaCollege of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi Province, ChinaCollege of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi Province, ChinaCollege of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi Province, ChinaCollege of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi Province, China; Key Laboratory of Horticultural Engineering in Northwest Facilities, Ministry of Agriculture, Yangling 712100, Shaanxi Province, China; Facility Agriculture Engineering Technology Research Center of Shaanxi Province, Yangling 712100, Shaanxi Province, China; Corresponding authors.Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China; Corresponding authors.The high energy consumption characteristic of multi-span glass greenhouses significantly limits their widespread adoption. Optimizing energy strategies and implementing predictive models for energy consumption are essential for more efficient management and reduction of greenhouse operational energy costs. Existing methods rely on single-element approaches to predict energy consumption, but this reliance often results in severe performance limitations. Therefore, energy consumption prediction methods that incorporate multi-source data are necessary. To overcome the challenges concerning heterogeneity, redundancy, and interdependence among different data sources, this paper proposed a novel energy consumption method that integrates multi-source data through feature engineering and machine learning techniques, which significantly enhances the efficiency of data utilization and improves prediction accuracy. The final experimental results indicated that the proposed energy consumption prediction method demonstrates excellent performance with high prediction accuracy (R² = 0.9388) and low computational resource consumption (runtime = 926.91s), outperforming other models. Finally, the model was interpreted using SHAP (SHapley Additive exPlanations) values, and ablation experiments were conducted to validate the effectiveness of the proposed method in greenhouse energy consumption prediction, thereby providing strong support for greenhouse management.http://www.sciencedirect.com/science/article/pii/S2772375525000590Greenhouse energy consumptionMulti-source data integrationFeature engineeringPredictive modeling |
| spellingShingle | Yangda Chen Aiqun Bao Yapeng Li Yingfeng Xiang Wanlong Cai Zhaoqiang Xia Jialei Li Mingyang Ning Jing Sun Haixi Zhang Xianpeng Sun Xiaoming Wei A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China Smart Agricultural Technology Greenhouse energy consumption Multi-source data integration Feature engineering Predictive modeling |
| title | A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China |
| title_full | A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China |
| title_fullStr | A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China |
| title_full_unstemmed | A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China |
| title_short | A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China |
| title_sort | novel multi source data driven energy consumption prediction model for venlo type greenhouses in china |
| topic | Greenhouse energy consumption Multi-source data integration Feature engineering Predictive modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525000590 |
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