Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China
The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land us...
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Elsevier
2024-12-01
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| Series: | Advances in Applied Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666792424000350 |
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| author | Haizhi Luo Yiwen Zhang Xinyu Gao Zhengguang Liu Xiangzhao Meng Xiaohu Yang |
| author_facet | Haizhi Luo Yiwen Zhang Xinyu Gao Zhengguang Liu Xiangzhao Meng Xiaohu Yang |
| author_sort | Haizhi Luo |
| collection | DOAJ |
| description | The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land use was proposed. This model achieves land-use subdivision to provide highly correlated variables; exhibits strong interpretability, thereby revealing even marginal effects of land use on electricity consumption; and demonstrates high performance, thereby enabling large-scale simulations and predictions. Using 297 cities and 2,505 counties as case studies, the key findings are as follows: (1) The model demonstrates strong generalization ability (R2 = 0.91), high precision (Kappa = 0.77), and robustness, with an overall prediction accuracy exceeding 80 %; (2) The marginal impact of industrial land on electricity consumption is more complex, with more efficiency achieved by limiting its area to either 104.3 km2 or between 288.2 and 657.3 km2; (3) The marginal impact of commercial and residential land on electricity consumption exhibits a strong linear relationship (R2 > 0.80). Restricting the scale to 11.3 km2 could effectively mitigate this impact. Mixed commercial and residential land is advantageous for overall electricity consumption control, but after exceeding 43.5 km2, separate layout considerations for urban residential land are necessary; (4) In 2030, Shanghai's electricity consumption is projected to reach 155,143 million kW·h, making it the highest among the 297 cities. Meanwhile, Suzhou Industrial Park leads among the 2,505 districts with a consumption of 30,996 million kW·h; (5) Identify future electricity consumption hotspots and clustering characteristics, evaluate the renewable energy potential in these hotspot areas, and propose targeted strategies accordingly. |
| format | Article |
| id | doaj-art-ecc0570ab6144c1fab2edd756b01bdcc |
| institution | DOAJ |
| issn | 2666-7924 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Advances in Applied Energy |
| spelling | doaj-art-ecc0570ab6144c1fab2edd756b01bdcc2025-08-20T02:50:20ZengElsevierAdvances in Applied Energy2666-79242024-12-011610019710.1016/j.adapen.2024.100197Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of ChinaHaizhi Luo0Yiwen Zhang1Xinyu Gao2Zhengguang Liu3Xiangzhao Meng4Xiaohu Yang5Institute of the Building Environment & Sustainability Technology, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaSchool of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, ChinaInstitute of the Building Environment & Sustainability Technology, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaDepartment of Chemical Engineering, School of Engineering, The University of Manchester, Manchester, M13 9PL, UKInstitute of the Building Environment & Sustainability Technology, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Corresponding authors.Institute of the Building Environment & Sustainability Technology, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China; School of Sustainable Development of Society and Technology, Mälardalen University, 721 23 Västerås, Sweden; Corresponding authors.The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land use was proposed. This model achieves land-use subdivision to provide highly correlated variables; exhibits strong interpretability, thereby revealing even marginal effects of land use on electricity consumption; and demonstrates high performance, thereby enabling large-scale simulations and predictions. Using 297 cities and 2,505 counties as case studies, the key findings are as follows: (1) The model demonstrates strong generalization ability (R2 = 0.91), high precision (Kappa = 0.77), and robustness, with an overall prediction accuracy exceeding 80 %; (2) The marginal impact of industrial land on electricity consumption is more complex, with more efficiency achieved by limiting its area to either 104.3 km2 or between 288.2 and 657.3 km2; (3) The marginal impact of commercial and residential land on electricity consumption exhibits a strong linear relationship (R2 > 0.80). Restricting the scale to 11.3 km2 could effectively mitigate this impact. Mixed commercial and residential land is advantageous for overall electricity consumption control, but after exceeding 43.5 km2, separate layout considerations for urban residential land are necessary; (4) In 2030, Shanghai's electricity consumption is projected to reach 155,143 million kW·h, making it the highest among the 297 cities. Meanwhile, Suzhou Industrial Park leads among the 2,505 districts with a consumption of 30,996 million kW·h; (5) Identify future electricity consumption hotspots and clustering characteristics, evaluate the renewable energy potential in these hotspot areas, and propose targeted strategies accordingly.http://www.sciencedirect.com/science/article/pii/S2666792424000350Electricity consumptionLand useHigh-performance prediction modelInterpretable machine learningMulti scale spatial characterization |
| spellingShingle | Haizhi Luo Yiwen Zhang Xinyu Gao Zhengguang Liu Xiangzhao Meng Xiaohu Yang Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China Advances in Applied Energy Electricity consumption Land use High-performance prediction model Interpretable machine learning Multi scale spatial characterization |
| title | Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China |
| title_full | Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China |
| title_fullStr | Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China |
| title_full_unstemmed | Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China |
| title_short | Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China |
| title_sort | multi scale electricity consumption prediction model based on land use and interpretable machine learning a case study of china |
| topic | Electricity consumption Land use High-performance prediction model Interpretable machine learning Multi scale spatial characterization |
| url | http://www.sciencedirect.com/science/article/pii/S2666792424000350 |
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