Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Matheus Henrique Dal Molin Ribeiro, Stéfano Frizzo Stefenon, José Donizetti de Lima, Ademir Nied, Viviana Cocco Mariani and Leandro dos Santos Coelho
Additional contact information
Matheus Henrique Dal Molin Ribeiro: Department of Mathematics (DAMAT), Federal Technological University of Parana (UTFPR), Pato Branco (PR) 85503-390, Brazil
Stéfano Frizzo Stefenon: Electrical Engineering Graduate Program, Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinvile (SC) 80215-901, Brazil
José Donizetti de Lima: Department of Mathematics (DAMAT), Federal Technological University of Parana (UTFPR), Pato Branco (PR) 85503-390, Brazil
Ademir Nied: Electrical Engineering Graduate Program, Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinvile (SC) 80215-901, Brazil
Viviana Cocco Mariani: Department of Electrical Engineering, Federal University of Parana (UFPR), Curitiba (PR) 80060-000, Brazil
Leandro dos Santos Coelho: Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), Curitiba (PR) 80215-901, Brazil

Energies, 2020, vol. 13, issue 19, 1-22

Abstract: Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

Keywords: complementary ensemble empirical mode decomposition; electricity price forecasting; ensemble learning models; exogenous variables; short-term forecasting (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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