Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis

Carolina Deina, João Lucas Ferreira dos Santos, Lucas Henrique Biuk, Mauro Lizot, Attilio Converti (), Hugo Valadares Siqueira and Flavio Trojan
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Carolina Deina: Graduate Program in Industrial Engineering (PPGEP), Federal University of Rio Grande do Sul (UFRGS), Av. Paulo Gama, 110, Porto Alegre 90040-060, Brazil
João Lucas Ferreira dos Santos: Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology-Parana (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Ponta Grossa 84017-220, Brazil
Lucas Henrique Biuk: Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology-Parana (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Ponta Grossa 84017-220, Brazil
Mauro Lizot: Department of General and Applied Administration (DAGA), Federal University of Parana (UFPR), Avenue Prefeito Lothário Meissner, 632, Jardim Botânico 80210-170, Brazil
Attilio Converti: Department of Civil, Chemical and Environmental Engineering, University of Genoa, Pole of Chemical Engineering, Via Opera Pia 15, 15145 Genoa, Italy
Hugo Valadares Siqueira: Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology-Parana (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Ponta Grossa 84017-220, Brazil
Flavio Trojan: Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology-Parana (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Ponta Grossa 84017-220, Brazil

Energies, 2023, vol. 16, issue 4, 1-24

Abstract: The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting.

Keywords: electricity demand; multi-criteria forecasting model; dependent variable; artificial neural networks; forecasting models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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