Coupling the Empirical Wavelet and the Neural Network Methods in Order to Forecast Electricity Price

Heni Boubaker () and Nawres Bannour
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Heni Boubaker: Institute of High Commercial Studies (IHEC) of Sousse, LaREMFiQ, B.P. 40, Sousse 4054, Tunisia
Nawres Bannour: Faculté des Sciences Economique et de Gestion de Sousse, Cité Riadh, Sousse 4023, Tunisia

JRFM, 2023, vol. 16, issue 4, 1-22

Abstract: This paper aims to evaluate the forecast capability of electricity markets, categorized by numerous major characteristics such as non-stationarity, nonlinearity, highest volatility, high frequency, mean reversion and multiple seasonality, which give multifarious forecasts. To improve it, this investigation proposes a new hybrid approach that links a dual long-memory process (Gegenbauer autoregressive moving average (GARMA) and generalized long-memory GARCH (G-GARCH)) and the empirical wavelet transform (EWT) and local linear wavelet neural network (LLWNN) approaches, forming the k-factor GARMA-EWLLWNN model. The future hybrid model accomplished is assessed via data from the Polish electricity markets, and it is matched with the generalized long-memory k-factor GARMA-G-GARCH process and the hybrid EWLLWNN, to demonstrate the robustness of our approach. The obtained outcomes show that the suggested model presents important results to define the relevance of the modeling approach that offers a remarkable framework to reproduce the inherent characteristics of the electricity prices. Finally, it is presented that the adopted methodology is the most appropriate one for prediction as it realizes a better prediction performance and may be an answer for forecasting electricity prices.

Keywords: electricity cost; empirical wavelet; dual long memory; hybrid estimation; prediction performance (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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