Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models

José Rubio-León, José Rubio-Cienfuegos, Cristian Vidal-Silva (), Jesennia Cárdenas-Cobo and Vannessa Duarte
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José Rubio-León: Escuela de Computación e Informática, Universidad Bernardo O’Higgins, Av. Viel 1497, Santiago 8320000, Chile
José Rubio-Cienfuegos: Departamento de Ingeniería Eléctrica, Universidad de Chile, Av. Tupper 2007, Santiago 8320000, Chile
Cristian Vidal-Silva: School of Videogame Development and Virtual Reality Engineering, Faculty of Engineering, University of Talca, Talca 3480260, Chile
Jesennia Cárdenas-Cobo: Facultad de Ciencias e Ingenierías, Universidad Estatal de Milagro, Milagro 091706, Ecuador
Vannessa Duarte: Escuela de Ciencias Empresariales, Universidad Católica del Norte, Coquimbo 1781421, Chile

Mathematics, 2023, vol. 11, issue 17, 1-18

Abstract: Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.

Keywords: electricity; ENTSO-E; fuzzy logic and models; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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