A data set for electric power consumption forecasting based on socio-demographic features: Data from an area of southern Colombia. Parraga-Alava, J., Moncayo-Nacaza, J., D., Revelo-Fuelagán, J., Rosero-Montalvo, P., D., Anaya-Isaza, A., & Peluffo-Ordóñez, D., H. Data in Brief, 29:105246, 4, 2020. Website doi abstract bibtex 15 downloads In this article, we introduce a data set concerning electric-power consumption-related features registered in seven main municipalities of Nariño, Colombia, from December 2010 to May 2016. The data set consists of 4427 socio-demographic characteristics, and 7 power-consumption-referred measured values. Data were fully collected by the company Centrales Eléctricas de Nariño (CEDENAR) according to the client consumption records. Power consumption data collection was carried following a manual procedure wherein company workers are in charge of manually registering the readings (measured in kWh) reported by the electric energy meters installed at each housing/building. Released data set is aimed at providing researchers a suitable input for designing and assessing the performance of forecasting, modelling, simulation and optimization approaches applied to electric power consumption prediction and characterization problems. The data set, so-named in shorthand PCSTCOL, is freely and publicly available at https://doi.org/10.17632/xbt7scz5ny.3.
@article{
title = {A data set for electric power consumption forecasting based on socio-demographic features: Data from an area of southern Colombia},
type = {article},
year = {2020},
pages = {105246},
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abstract = {In this article, we introduce a data set concerning electric-power consumption-related features registered in seven main municipalities of Nariño, Colombia, from December 2010 to May 2016. The data set consists of 4427 socio-demographic characteristics, and 7 power-consumption-referred measured values. Data were fully collected by the company Centrales Eléctricas de Nariño (CEDENAR) according to the client consumption records. Power consumption data collection was carried following a manual procedure wherein company workers are in charge of manually registering the readings (measured in kWh) reported by the electric energy meters installed at each housing/building. Released data set is aimed at providing researchers a suitable input for designing and assessing the performance of forecasting, modelling, simulation and optimization approaches applied to electric power consumption prediction and characterization problems. The data set, so-named in shorthand PCSTCOL, is freely and publicly available at https://doi.org/10.17632/xbt7scz5ny.3.},
bibtype = {article},
author = {Parraga-Alava, Jorge and Moncayo-Nacaza, Jorge Dario and Revelo-Fuelagán, Javier and Rosero-Montalvo, Paul D. and Anaya-Isaza, Andrés and Peluffo-Ordóñez, Diego Hernán},
doi = {10.1016/j.dib.2020.105246},
journal = {Data in Brief}
}
Downloads: 15
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