Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities
Abstract The use of distributed energy resources (DERs) in a city contributes to the net zero CO2 of a city. However, the spatially uneven distribution of power demand and surplus electricity causes congestion in the grid system, making wide‐area operation difficult. The concept of local energy self...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-06-01
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| Series: | IET Smart Cities |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/smc2.12011 |
| _version_ | 1811303640448630784 |
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| author | Ayumu Miyasawa Shogo Akira Yu Fujimoto Yasuhiro Hayashi |
| author_facet | Ayumu Miyasawa Shogo Akira Yu Fujimoto Yasuhiro Hayashi |
| author_sort | Ayumu Miyasawa |
| collection | DOAJ |
| description | Abstract The use of distributed energy resources (DERs) in a city contributes to the net zero CO2 of a city. However, the spatially uneven distribution of power demand and surplus electricity causes congestion in the grid system, making wide‐area operation difficult. The concept of local energy self‐sufficiency via energy management, in which batteries or electric vehicles are charged using power generated by DERs and discharged to neighbouring consumers, is expected to be a way to avoid grid conjunction while maximizing the use of DERs. For efficient local energy self‐sufficiency, it is necessary to identify where and when future power surpluses and shortages will occur within a city and optimize battery operation according to demand. Forecasts that focus only on representative points of a city may be less reproducible in diversity in the power demand transition for individual consumers in local parts of cities. Electricity smart meters that monitor power demand every 30 min from each consumer are expected to help predict the spatiotemporal distribution of power demand to achieve efficient local energy self‐sufficiency. The significance of reflecting regional characteristics in forecasting spatiotemporal distribution of power demand is demonstrated using actual data obtained by smart meters installed in Japanese cities. The results suggest that the forecast approach, which considers the daily periodicity of power demand and weather conditions, obtains high prediction accuracy in predicting power demand in meshed local areas in the city and derives results precisely reproducing the spatiotemporal behaviours of power demand. |
| first_indexed | 2024-04-13T07:50:39Z |
| format | Article |
| id | doaj.art-b54f9d9b6bdf4d489f449090baced5d4 |
| institution | Directory Open Access Journal |
| issn | 2631-7680 |
| language | English |
| last_indexed | 2024-04-13T07:50:39Z |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Cities |
| spelling | doaj.art-b54f9d9b6bdf4d489f449090baced5d42022-12-22T02:55:32ZengWileyIET Smart Cities2631-76802021-06-013210712010.1049/smc2.12011Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart citiesAyumu Miyasawa0Shogo Akira1Yu Fujimoto2Yasuhiro Hayashi3Department of Electrical Engineering and Bioscience Waseda University Shinjuku JapanDepartment of Electrical Engineering and Bioscience Waseda University Shinjuku JapanAdvanced Collaborative Research Organization for Smart Society Waseda University Shinjuku JapanDepartment of Electrical Engineering and Bioscience Waseda University Shinjuku JapanAbstract The use of distributed energy resources (DERs) in a city contributes to the net zero CO2 of a city. However, the spatially uneven distribution of power demand and surplus electricity causes congestion in the grid system, making wide‐area operation difficult. The concept of local energy self‐sufficiency via energy management, in which batteries or electric vehicles are charged using power generated by DERs and discharged to neighbouring consumers, is expected to be a way to avoid grid conjunction while maximizing the use of DERs. For efficient local energy self‐sufficiency, it is necessary to identify where and when future power surpluses and shortages will occur within a city and optimize battery operation according to demand. Forecasts that focus only on representative points of a city may be less reproducible in diversity in the power demand transition for individual consumers in local parts of cities. Electricity smart meters that monitor power demand every 30 min from each consumer are expected to help predict the spatiotemporal distribution of power demand to achieve efficient local energy self‐sufficiency. The significance of reflecting regional characteristics in forecasting spatiotemporal distribution of power demand is demonstrated using actual data obtained by smart meters installed in Japanese cities. The results suggest that the forecast approach, which considers the daily periodicity of power demand and weather conditions, obtains high prediction accuracy in predicting power demand in meshed local areas in the city and derives results precisely reproducing the spatiotemporal behaviours of power demand.https://doi.org/10.1049/smc2.12011battery powered vehiclescarbon compoundsdemand forecastingdistributed power generationenergy management systemsload forecasting |
| spellingShingle | Ayumu Miyasawa Shogo Akira Yu Fujimoto Yasuhiro Hayashi Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities IET Smart Cities battery powered vehicles carbon compounds demand forecasting distributed power generation energy management systems load forecasting |
| title | Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities |
| title_full | Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities |
| title_fullStr | Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities |
| title_full_unstemmed | Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities |
| title_short | Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities |
| title_sort | spatial demand forecasting based on smart meter data for improving local energy self sufficiency in smart cities |
| topic | battery powered vehicles carbon compounds demand forecasting distributed power generation energy management systems load forecasting |
| url | https://doi.org/10.1049/smc2.12011 |
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