Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approach
Abstract Compared to load forecasting at the national level, two challenges arise in providing accurate forecasting for LV and MV networks: (1) customers within LV and MV networks are much less, implying greater volatility within those load profiles; (2) not all customers have smart metres. Particul...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Smart Grid |
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| Online Access: | https://doi.org/10.1049/stg2.12181 |
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| author | Junlong Li Lurui Fang Xiangyu Wei Mengqiu Fang Yue Xiang Peipei You Chao Zhang Chenghong Gu |
| author_facet | Junlong Li Lurui Fang Xiangyu Wei Mengqiu Fang Yue Xiang Peipei You Chao Zhang Chenghong Gu |
| author_sort | Junlong Li |
| collection | DOAJ |
| description | Abstract Compared to load forecasting at the national level, two challenges arise in providing accurate forecasting for LV and MV networks: (1) customers within LV and MV networks are much less, implying greater volatility within those load profiles; (2) not all customers have smart metres. Particularly, the two challenges would exacerbate forecasting performance under unexpected events, such as extreme weather and COVID‐19 outbreaks. To secure accurate short‐term load forecasting for LV and MV networks, this paper customised a Spatio‐Temporal Edge‐Cloud‐coordinated (STEC) approach on a loop training structure—LV networks to MV networks to LV networks. For each LV network, this approach utilises XGboost to learn the relationship between weather data, substation‐side loads, and a few accessible customer‐side load data to deliver rough forecasting. Then, it adopts the rough forecasting results and accessible data for all LV networks within an MV network to train the convolutional neural networks and gated recurrent unit (CNN‐GRU) network. This step provides load forecasting for MV networks and simultaneously refines load forecasting for LV networks by generating the interacting relationship between LV substations of different locations. Case studies reveal that the STEC approach successfully extrapolates the demand‐varying information from long‐term datasets and improves short‐term forecasting performance under both normal scenarios and newly occurring unexpected scenarios for LV and MV networks. The loop training structure halves the forecasting error, compared to classic methodology by utilising the local data only for MV networks. |
| format | Article |
| id | doaj-art-4d573d2abebe4674a1eaa9b4d891a313 |
| institution | OA Journals |
| issn | 2515-2947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj-art-4d573d2abebe4674a1eaa9b4d891a3132025-08-20T02:35:35ZengWileyIET Smart Grid2515-29472024-12-017682984210.1049/stg2.12181Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approachJunlong Li0Lurui Fang1Xiangyu Wei2Mengqiu Fang3Yue Xiang4Peipei You5Chao Zhang6Chenghong Gu7State Grid Energy Research Institute Co. Ltd. Beijing ChinaSchool of Advanced Technology Xi’an Jiaotong‐Liverpool University Suzhou ChinaCollege of Electrical Engineering Sichuan University Chengdu ChinaCollege of Electrical Engineering Sichuan University Chengdu ChinaCollege of Electrical Engineering Sichuan University Chengdu ChinaState Grid Energy Research Institute Co. Ltd. Beijing ChinaState Grid Energy Research Institute Co. Ltd. Beijing ChinaDepartment of Electronic & Electrical Engineering University of Bath Bath UKAbstract Compared to load forecasting at the national level, two challenges arise in providing accurate forecasting for LV and MV networks: (1) customers within LV and MV networks are much less, implying greater volatility within those load profiles; (2) not all customers have smart metres. Particularly, the two challenges would exacerbate forecasting performance under unexpected events, such as extreme weather and COVID‐19 outbreaks. To secure accurate short‐term load forecasting for LV and MV networks, this paper customised a Spatio‐Temporal Edge‐Cloud‐coordinated (STEC) approach on a loop training structure—LV networks to MV networks to LV networks. For each LV network, this approach utilises XGboost to learn the relationship between weather data, substation‐side loads, and a few accessible customer‐side load data to deliver rough forecasting. Then, it adopts the rough forecasting results and accessible data for all LV networks within an MV network to train the convolutional neural networks and gated recurrent unit (CNN‐GRU) network. This step provides load forecasting for MV networks and simultaneously refines load forecasting for LV networks by generating the interacting relationship between LV substations of different locations. Case studies reveal that the STEC approach successfully extrapolates the demand‐varying information from long‐term datasets and improves short‐term forecasting performance under both normal scenarios and newly occurring unexpected scenarios for LV and MV networks. The loop training structure halves the forecasting error, compared to classic methodology by utilising the local data only for MV networks.https://doi.org/10.1049/stg2.12181artificial intelligence and data analyticsdistribution networksload forecasting |
| spellingShingle | Junlong Li Lurui Fang Xiangyu Wei Mengqiu Fang Yue Xiang Peipei You Chao Zhang Chenghong Gu Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approach IET Smart Grid artificial intelligence and data analytics distribution networks load forecasting |
| title | Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approach |
| title_full | Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approach |
| title_fullStr | Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approach |
| title_full_unstemmed | Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approach |
| title_short | Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approach |
| title_sort | short term load forecasting facilitated by edge data centres a coordinated edge cloud approach |
| topic | artificial intelligence and data analytics distribution networks load forecasting |
| url | https://doi.org/10.1049/stg2.12181 |
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