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...

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Autores principales:	Ayumu Miyasawa, Shogo Akira, Yu Fujimoto, Yasuhiro Hayashi
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9
Acceso en línea:	https://doaj.org/article/b54f9d9b6bdf4d489f449090baced5d4
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spelling	oai:doaj.org-article:b54f9d9b6bdf4d489f449090baced5d42021-11-22T16:30:56ZSpatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities2631-768010.1049/smc2.12011https://doaj.org/article/b54f9d9b6bdf4d489f449090baced5d42021-06-01T00:00:00Zhttps://doi.org/10.1049/smc2.12011https://doaj.org/toc/2631-7680Abstract 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.Ayumu MiyasawaShogo AkiraYu FujimotoYasuhiro HayashiWileyarticleEngineering (General). Civil engineering (General)TA1-2040City planningHT165.5-169.9ENIET Smart Cities, Vol 3, Iss 2, Pp 107-120 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9 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
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.
format	article
author	Ayumu Miyasawa Shogo Akira Yu Fujimoto Yasuhiro Hayashi
author_facet	Ayumu Miyasawa Shogo Akira Yu Fujimoto Yasuhiro Hayashi
author_sort	Ayumu Miyasawa
title	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_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_sort	spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/b54f9d9b6bdf4d489f449090baced5d4
work_keys_str_mv	AT ayumumiyasawa spatialdemandforecastingbasedonsmartmeterdataforimprovinglocalenergyselfsufficiencyinsmartcities AT shogoakira spatialdemandforecastingbasedonsmartmeterdataforimprovinglocalenergyselfsufficiencyinsmartcities AT yufujimoto spatialdemandforecastingbasedonsmartmeterdataforimprovinglocalenergyselfsufficiencyinsmartcities AT yasuhirohayashi spatialdemandforecastingbasedonsmartmeterdataforimprovinglocalenergyselfsufficiencyinsmartcities
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Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities

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