Use of scalable fuzzy time series methods to predict electrical demand
Electric power is one of the main engines of humanity since the late eighteenth century, therefore, understanding the behavior of the demand of this corresponds to a very important study. Within this field, electricity demand prediction has been one of the most developed activities in several resear...
Guardado en:
| Autores principales: | , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/155439 |
| Aporte de: |
| Sumario: | Electric power is one of the main engines of humanity since the late eighteenth century, therefore, understanding the behavior of the demand of this corresponds to a very important study. Within this field, electricity demand prediction has been one of the most developed activities in several researches, where the use of fuzzy time series models has had good results. This paper presents the use of scalable fuzzy time series methods, which were developed by Petronio Silva [1] to predict the Spanish electricity demand collected by the Spanish TSO, whose computational implementation uses a Mamdani fuzzy inference system, which directly processes a time series containing the demand data and its register.
It should be noted that the database also includes forecasts made by the same Spanish TSO, so comparisons are made with these forecasts. |
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