Impact of Ambient Temperature on Short-term Maximum Electrical Demand through the Performance of Forecast Models generated with Prophet

The maximum short-term electrical demand is affected by climatic factors, including ambient temperature. To incorporate it into the forecast models, it is necessary to generate an indicator that represents the ambient temperature of the area under study. The objective of this research is to determin...

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Autor principal: Yajure-Ramírez, César A.
Formato: Articulo
Lenguaje:Inglés
Publicado: 2025
Materias:
Ciencias Informáticas
correlation
electrical demand
forecast
performance metrics
temperature
Correlación
demanda eléctrica
métricas de desempeño
pronóstico
Temperatura
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/178720
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-178720
record_format dspace
spelling I19-R120-10915-1787202025-05-06T20:06:44Z http://sedici.unlp.edu.ar/handle/10915/178720 Impact of Ambient Temperature on Short-term Maximum Electrical Demand through the Performance of Forecast Models generated with Prophet Impacto de la temperatura ambiente en la demanda eléctrica máxima de corto plazo a través del desempeño de modelos de pronóstico generados con Prophet Yajure-Ramírez, César A. 2025-04 2025-05-06T15:27:32Z en Ciencias Informáticas correlation electrical demand forecast performance metrics temperature Correlación demanda eléctrica métricas de desempeño pronóstico Temperatura The maximum short-term electrical demand is affected by climatic factors, including ambient temperature. To incorporate it into the forecast models, it is necessary to generate an indicator that represents the ambient temperature of the area under study. The objective of this research is to determine the impact of ambient temperature on the short-term maximum electrical demand through the performance of the forecast models, integrating into a single indicator the temperature measurements from different points of the geographical area under analysis, using as weighting factors to the proportions of regional demands with respect to total demand. The Prophet forecasting technique is used, with historical data on electrical demand and daily ambient temperature from November 2022 to November 2024. To evaluate the models, the MAE, RMSE, and MAPE metrics are used, with data outside the historical period. The forecast model considering the Weighted High Temperature indicator as a regressor variable was the one that had the greatest improvements in the metrics when comparing them with those coming from the model that did not consider temperature as a regressor variable, with improvements of 25%, 21%, and 15%, in MAPE, MAE, and RMSE, respectively. La demanda eléctrica máxima de corto plazo se ve afectada por factores climáticos, entre ellos la temperatura ambiente. Para incorporarla en los modelos de pronóstico, se hace necesario generar un indicador que represente a la temperatura ambiente del área bajo estudio. El objetivo de esta investigación es determinar el impacto de la temperatura ambiente en la demanda eléctrica máxima de corto plazo a través del desempeño de los modelos de pronóstico, integrando en un solo indicador las mediciones de temperatura de distintos puntos del área geográfica bajo análisis, utilizando como factores de ponderación a las proporciones de las demandas regionales con respecto a la demanda total. Se hace uso de la técnica de pronóstico Prophet, con datos históricos de demanda eléctrica y temperatura ambiente diaria desde noviembre del 2022 hasta noviembre del 2024. Para evaluar los modelos se utilizan las métricas MAE, RMSE, y MAPE, con datos fuera del período histórico. El modelo de pronóstico considerando el indicador Temperatura Alta Ponderada como variable regresora fue el que tuvo las mayores mejoras de las métricas al compararlas con aquellas provenientes del modelo que no consideró a la temperatura como variable regresora, con mejoras del 25%, 21%, y 15%, en MAPE, MAE, y RMSE, respectivamente. Facultad de Informática Articulo Articulo http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 16-24
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
correlation
electrical demand
forecast
performance metrics
temperature
Correlación
demanda eléctrica
métricas de desempeño
pronóstico
Temperatura
spellingShingle Ciencias Informáticas
correlation
electrical demand
forecast
performance metrics
temperature
Correlación
demanda eléctrica
métricas de desempeño
pronóstico
Temperatura
Yajure-Ramírez, César A.
Impact of Ambient Temperature on Short-term Maximum Electrical Demand through the Performance of Forecast Models generated with Prophet
topic_facet Ciencias Informáticas
correlation
electrical demand
forecast
performance metrics
temperature
Correlación
demanda eléctrica
métricas de desempeño
pronóstico
Temperatura
description The maximum short-term electrical demand is affected by climatic factors, including ambient temperature. To incorporate it into the forecast models, it is necessary to generate an indicator that represents the ambient temperature of the area under study. The objective of this research is to determine the impact of ambient temperature on the short-term maximum electrical demand through the performance of the forecast models, integrating into a single indicator the temperature measurements from different points of the geographical area under analysis, using as weighting factors to the proportions of regional demands with respect to total demand. The Prophet forecasting technique is used, with historical data on electrical demand and daily ambient temperature from November 2022 to November 2024. To evaluate the models, the MAE, RMSE, and MAPE metrics are used, with data outside the historical period. The forecast model considering the Weighted High Temperature indicator as a regressor variable was the one that had the greatest improvements in the metrics when comparing them with those coming from the model that did not consider temperature as a regressor variable, with improvements of 25%, 21%, and 15%, in MAPE, MAE, and RMSE, respectively.
format Articulo
Articulo
author Yajure-Ramírez, César A.
author_facet Yajure-Ramírez, César A.
author_sort Yajure-Ramírez, César A.
title Impact of Ambient Temperature on Short-term Maximum Electrical Demand through the Performance of Forecast Models generated with Prophet
title_short Impact of Ambient Temperature on Short-term Maximum Electrical Demand through the Performance of Forecast Models generated with Prophet
title_full Impact of Ambient Temperature on Short-term Maximum Electrical Demand through the Performance of Forecast Models generated with Prophet
title_fullStr Impact of Ambient Temperature on Short-term Maximum Electrical Demand through the Performance of Forecast Models generated with Prophet
title_full_unstemmed Impact of Ambient Temperature on Short-term Maximum Electrical Demand through the Performance of Forecast Models generated with Prophet
title_sort impact of ambient temperature on short-term maximum electrical demand through the performance of forecast models generated with prophet
publishDate 2025
url http://sedici.unlp.edu.ar/handle/10915/178720
work_keys_str_mv AT yajureramirezcesara impactofambienttemperatureonshorttermmaximumelectricaldemandthroughtheperformanceofforecastmodelsgeneratedwithprophet
AT yajureramirezcesara impactodelatemperaturaambienteenlademandaelectricamaximadecortoplazoatravesdeldesempenodemodelosdepronosticogeneradosconprophet
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