Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models

Author:

Rubio-León José1,Rubio-Cienfuegos José2,Vidal-Silva Cristian3ORCID,Cárdenas-Cobo Jesennia4,Duarte Vannessa5ORCID

Affiliation:

1. Escuela de Computación e Informática, Universidad Bernardo O’Higgins, Av. Viel 1497, Santiago 8320000, Chile

2. Departamento de Ingeniería Eléctrica, Universidad de Chile, Av. Tupper 2007, Santiago 8320000, Chile

3. School of Videogame Development and Virtual Reality Engineering, Faculty of Engineering, University of Talca, Talca 3480260, Chile

4. Facultad de Ciencias e Ingenierías, Universidad Estatal de Milagro, Milagro 091706, Ecuador

5. Escuela de Ciencias Empresariales, Universidad Católica del Norte, Coquimbo 1781421, Chile

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.

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference51 articles.

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3. Green financing role on renewable energy dependence and energy transition in E7 economies;Wang;Renew. Energy,2022

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5. Twenergy (2023, August 10). La Demanda eléCtrica. Available online: https://twenergy.com/eficiencia-energetica/como-ahorrar-energia-casa/la-demandaelectrica-953/.

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