TY - JOUR
T1 - Forecasting electricity demand of municipalities through artificial neural networks and metered supply point classification
AU - Mateo-Barcos, S.
AU - Ribó-Pérez, D.
AU - Rodríguez-García, J.
AU - Alcázar-Ortega, M.
PY - 2024
Y1 - 2024
N2 - This study develops a methodology to characterise and forecast large consumers’ electricity demand, particularly municipalities, with hundreds of different metered supply points based on the previous characterisation of facilities’ consumption. Demand forecasting allows consumers to improve their participation in electricity markets and manage their electricity consumption. The method considers a classification by different types of metered supply points combined with artificial neural networks to obtain hourly forecasts using well-known parameters such as day types, hourly temperature, the last hour of electricity consumption, and sunrise and sunset time. We apply the methodology to the municipality of Valencia using over five hundred hourly load profiles for a year during 2017 and 2018. Our results present aggregated forecasts with a maximum Mean Absolute Percentage Error of 3.8% per day, outperforming the same forecast without classifying Metered Supply Points. We conclude that a correct electricity demand forecast for a consumer with different types of consumption does not need submetering, but characterising Metered Supply Points is an option with lower costs that allows for better predictions.
AB - This study develops a methodology to characterise and forecast large consumers’ electricity demand, particularly municipalities, with hundreds of different metered supply points based on the previous characterisation of facilities’ consumption. Demand forecasting allows consumers to improve their participation in electricity markets and manage their electricity consumption. The method considers a classification by different types of metered supply points combined with artificial neural networks to obtain hourly forecasts using well-known parameters such as day types, hourly temperature, the last hour of electricity consumption, and sunrise and sunset time. We apply the methodology to the municipality of Valencia using over five hundred hourly load profiles for a year during 2017 and 2018. Our results present aggregated forecasts with a maximum Mean Absolute Percentage Error of 3.8% per day, outperforming the same forecast without classifying Metered Supply Points. We conclude that a correct electricity demand forecast for a consumer with different types of consumption does not need submetering, but characterising Metered Supply Points is an option with lower costs that allows for better predictions.
KW - Artificial neural networks
KW - Load forecasting
KW - Metered supply points
KW - Municipalities
UR - http://www.scopus.com/inward/record.url?scp=85188824471&partnerID=8YFLogxK
U2 - 10.1016/j.egyr.2024.03.023
DO - 10.1016/j.egyr.2024.03.023
M3 - Article
AN - SCOPUS:85188824471
SN - 2352-4847
VL - 11
SP - 3533
EP - 3549
JO - Energy Reports
JF - Energy Reports
ER -