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 determi...
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| Format: | Article |
| Language: | English |
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Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
2025-04-01
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| Series: | Journal of Computer Science and Technology |
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| Online Access: | https://journal.info.unlp.edu.ar/JCST/article/view/3799 |
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| _version_ | 1850041743560409088 |
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| author | César A. Yajure-Ramírez |
| author_facet | César A. Yajure-Ramírez |
| author_sort | César A. Yajure-Ramírez |
| collection | DOAJ |
| 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.
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| format | Article |
| id | doaj-art-54e1cd896c244815a80fbd4676fca22d |
| institution | DOAJ |
| issn | 1666-6046 1666-6038 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata |
| record_format | Article |
| series | Journal of Computer Science and Technology |
| spelling | doaj-art-54e1cd896c244815a80fbd4676fca22d2025-08-20T02:55:42ZengPostgraduate Office, School of Computer Science, Universidad Nacional de La PlataJournal of Computer Science and Technology1666-60461666-60382025-04-0125110.24215/16666038.25.e02Impact of ambient temperature on short-term maximum electrical demand through the performance of forecast models generated with ProphetCésar A. Yajure-Ramírez0Universidad Central de Venezuela 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. https://journal.info.unlp.edu.ar/JCST/article/view/3799Correlation, electrical demand, forecast, performance metrics, temperature. |
| spellingShingle | César A. Yajure-Ramírez Impact of ambient temperature on short-term maximum electrical demand through the performance of forecast models generated with Prophet Journal of Computer Science and Technology Correlation, electrical demand, forecast, performance metrics, temperature. |
| title | 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_short | 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 |
| topic | Correlation, electrical demand, forecast, performance metrics, temperature. |
| url | https://journal.info.unlp.edu.ar/JCST/article/view/3799 |
| work_keys_str_mv | AT cesarayajureramirez impactofambienttemperatureonshorttermmaximumelectricaldemandthroughtheperformanceofforecastmodelsgeneratedwithprophet |