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Impact of ambient temperature on short-term maximum electrical demand through the performance of forecast models generated with Prophet
DOI:
https://doi.org/10.24215/16666038.25.e02Keywords:
Correlation, electrical demand, forecast, performance metrics, temperature.Abstract
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.
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2025-04-30
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Copyright (c) 2025 César A. Yajure-Ramírez
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How to Cite
[1]
“Impact of ambient temperature on short-term maximum electrical demand through the performance of forecast models generated with Prophet”, JCS&T, vol. 25, no. 1, p. e02, Apr. 2025, doi: 10.24215/16666038.25.e02.
