Forecasting Electric Load Demand through Advanced Statistical Techniques

• Autores: Silva, Jesús, Senior Naveda, Alexa, García Guliany, Jesús, Niebles Núẽz, William, Hernández Palma, Hugo
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/652142
• Enlace del recurso: http://hdl.handle.net/10757/652142
• Nivel de acceso: acceso abierto
• Materia: Bayesian networks; Decision making; Bayesian methods; Colombia; Electric load demands; Forecasting models; Statistical techniques; Electric load forecasting

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.