| Título: | Forecasting Electric Load Demand through Advanced Statistical Techniques |
| Autores: | Silva, Jesus ; Senior Naveda, Alexa ; García Guliany, Jesús ; Niebles Nú?z, William ; Hernández Palma, Hugo |
| Tipo de documento: | texto impreso |
| Editorial: | Institute of Physics Publishing, 2020-07-02T16:24:49Z |
| Dimensiones: | application/pdf |
| Nota general: |
Journal of Physics: Conference Series 1432 1 info:eu-repo/semantics/openAccess Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| Idiomas: | Inglés |
| Palabras clave: | Facultad de Negocios , Pregrado , Artículos científicos , Administración y Finanzas |
| Resumen: | 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. |
| En línea: | 17426588 |
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