Forecasting electric load demand through advanced statistical techniques
Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their a...
- Autores:
-
silva d, jesus g
Senior Naveda, Alexa
García Guiliany, Jesús Enrique
Niebles Nuñez, William
Hernández Palma, Hugo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5960
- Acceso en línea:
- https://hdl.handle.net/11323/5960
https://repositorio.cuc.edu.co/
- Palabra clave:
- Electric charge
Electrical demand
Forecasting models
- Rights
- openAccess
- License
- CC0 1.0 Universal
| Summary: | Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods. |
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