Título:
Electricity demand forecasting : the uruguayan case
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Autor:
Castrillejo, Andrés
Cugliari, Jairo
Massa, Fernando
Ramirez, Ignancio
Cugliari, Jairo
Massa, Fernando
Ramirez, Ignancio
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Ponencia
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Año de publicación:
2018
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Resumen:
The development of new electricity generation technologies has given new opportunities to developing economies. These economies are often highly dependent on fossil sources and so on the price of petrol. Uruguay has finished the transformation of its energetic mix, presenting today a very large participation of renewable sources among its production mix. This rapid change has demanded new mathematical and computing methods for the administration and monitoring of the system load. In this work we present enercast, a R package that contains prediction models that can be used by the network operator. The prediction models are divided in two groups, exogenous and endogenous models, that respectively uses external covariates or not. Each model is used to produce daily prediction which are then combined using a sequential aggregation algorithm. We show by numerical experiments the appropriateness of our end-to-end procedure applied to real data from the Uruguayan electrical system.
Descripción:
Presentado y publicado en Renewable Energy: Forecasting and Risk Management. FRM 2017. Springer Proceedings in Mathematics
Statistics, vol 254. Springer
Statistics, vol 254. Springer
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Citación:
Castrillejo, A., Cugliari, J., Massa, F., Ramirez, I. "Electricity demand forecasting : the uruguayan case. Publicado en:: Drobinski, P., Mougeot, M., Picard, D., Plougonven, R., Tankov, P. (eds) Renewable Energy: Forecasting and Risk Management. FRM 2017. Springer Proceedings in Mathematics
Statistics, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-99052-1_6
Statistics, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-99052-1_6
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Departamento académico:
Procesamiento de Señales
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Tratamiento de Imágenes
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Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| CCMR2018.pdf | — | 5 MB | Adobe PDF |