A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity

Heni Boubaker (1) , Souhir Ben Amor (2) , Hichem Rezgui (3)

1 UIR - Université Internationale de Rabat
2 IHEC Sousse
3 ESC Rennes School of Business

This study investigates the performance of a novel neural network technique in the problem of price forecasting. To improve the prediction accuracy using each model’s unique features, this research proposes a hybrid approach that combines the -factor GARMA process, empirical wavelet transform and the local linear wavelet neural network (LLWNN) methods, to form the GARMA-WLLWNN process. In order to verify the validity of the model and the algorithm, the performance of the proposed model is evaluated using data from Polish electricity markets, and it is compared with the dual generalized long memory -factor GARMA-G-GARCH model and the individual WLLWNN. The empirical results demonstrated the proposed hybrid model can achieve a better predicting performance and prove that is the most suitable electricity market forecasting technique.

Domaines: Ingénierie de l'environnement

Heni Boubaker, Souhir Ben Amor, Hichem Rezgui. A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity. Energy Studies Review, 2020, 24 (1), ⟨10.15173/esr.v24i1.4135⟩. ⟨hal-04082570⟩