Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Social Factors
Analyzing the electric power consumption (EPC) patterns of China’s mega urban agglomerations is crucial for promoting sustainable development both domestically and globally. Utilizing 2017–2021 NPP/VIIRS low-light remote sensing imagery to extract total nighttime light data, this study proposed an E...
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2025-02-01
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| author | Cuiting Li Dongmei Yan Shuo Chen Jun Yan Wanrong Wu Xiaowei Wang |
| author_facet | Cuiting Li Dongmei Yan Shuo Chen Jun Yan Wanrong Wu Xiaowei Wang |
| author_sort | Cuiting Li |
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| description | Analyzing the electric power consumption (EPC) patterns of China’s mega urban agglomerations is crucial for promoting sustainable development both domestically and globally. Utilizing 2017–2021 NPP/VIIRS low-light remote sensing imagery to extract total nighttime light data, this study proposed an EPC prediction method based on the K-Means clustering algorithm combined with multiple indicators integrated with socio-economic factors. Combining IPAT theory, regional GDP and population density, the final EPC prediction models were developed. Using these models, the EPC distributions for Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations in 2017–2021 were generated at both the administrative district level and the 1 km × 1 km grid scale. The spatio-temporal dynamics of the EPC distribution in these urban agglomerations during this period were then analyzed, followed by EPC predictions for 2022. The models showed a significant improvement in prediction accuracy, with the average MARE decreasing from 30.52% to 7.60%, 25.61% to 11.08% and 18.24% to 12.85% for the three urban agglomerations, respectively; EPC clusters were identified in these areas, mainly concentrated in Langfang and Chengde, Shanghai and Suzhou, and Dongguan; from 2017 to 2021, the EPC values of the three urban agglomerations show a growth trend and the distribution patterns were consistent with their economic development and population density; the R<sup>2</sup> values and the statistical values for the 2022 EPC predictions using the improved classification EPC models reached 0.9692, 0.9903 and 0.9677, respectively, confirming that the proposed method can effectively predict the EPC of urban agglomerations and is applicable in various scenarios. This method provides a timely and accurate spatial update of EPC dynamics, offering fine-scale characterization of urban EPC patterns using night light images. |
| format | Article |
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| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-e1e4caa02a6e4bab89ac75d72ee257312025-08-20T02:53:02ZengMDPI AGRemote Sensing2072-42922025-02-0117586510.3390/rs17050865Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Social FactorsCuiting Li0Dongmei Yan1Shuo Chen2Jun Yan3Wanrong Wu4Xiaowei Wang5Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, ChinaAnalyzing the electric power consumption (EPC) patterns of China’s mega urban agglomerations is crucial for promoting sustainable development both domestically and globally. Utilizing 2017–2021 NPP/VIIRS low-light remote sensing imagery to extract total nighttime light data, this study proposed an EPC prediction method based on the K-Means clustering algorithm combined with multiple indicators integrated with socio-economic factors. Combining IPAT theory, regional GDP and population density, the final EPC prediction models were developed. Using these models, the EPC distributions for Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations in 2017–2021 were generated at both the administrative district level and the 1 km × 1 km grid scale. The spatio-temporal dynamics of the EPC distribution in these urban agglomerations during this period were then analyzed, followed by EPC predictions for 2022. The models showed a significant improvement in prediction accuracy, with the average MARE decreasing from 30.52% to 7.60%, 25.61% to 11.08% and 18.24% to 12.85% for the three urban agglomerations, respectively; EPC clusters were identified in these areas, mainly concentrated in Langfang and Chengde, Shanghai and Suzhou, and Dongguan; from 2017 to 2021, the EPC values of the three urban agglomerations show a growth trend and the distribution patterns were consistent with their economic development and population density; the R<sup>2</sup> values and the statistical values for the 2022 EPC predictions using the improved classification EPC models reached 0.9692, 0.9903 and 0.9677, respectively, confirming that the proposed method can effectively predict the EPC of urban agglomerations and is applicable in various scenarios. This method provides a timely and accurate spatial update of EPC dynamics, offering fine-scale characterization of urban EPC patterns using night light images.https://www.mdpi.com/2072-4292/17/5/865Electric Power Consumption (EPC)Nighttime light (NTL)NPP/VIIRSMega urban agglomerationsspatio-temporal dynamic analysisk-means clustering |
| spellingShingle | Cuiting Li Dongmei Yan Shuo Chen Jun Yan Wanrong Wu Xiaowei Wang Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Social Factors Remote Sensing Electric Power Consumption (EPC) Nighttime light (NTL) NPP/VIIRS Mega urban agglomerations spatio-temporal dynamic analysis k-means clustering |
| title | Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Social Factors |
| title_full | Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Social Factors |
| title_fullStr | Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Social Factors |
| title_full_unstemmed | Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Social Factors |
| title_short | Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Social Factors |
| title_sort | electric power consumption forecasting models and spatio temporal dynamic analysis of china s mega city agglomerations based on low light remote sensing imagery incorporating social factors |
| topic | Electric Power Consumption (EPC) Nighttime light (NTL) NPP/VIIRS Mega urban agglomerations spatio-temporal dynamic analysis k-means clustering |
| url | https://www.mdpi.com/2072-4292/17/5/865 |
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