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Accurate electricity price forecasting is the main management goal for marketparticipants since it represents the fundamental basis to maximize the profitsfor market players. However, electricity is a non-storable commodity and theelectricity prices are affected by some social and natural factors that makethe price forecasting a challenging task. This study investigates thepredictive performance of a new hybrid model based on the Generalized longmemory autoregressive model (k-factor GARMA), the Gegenbauer GeneralizedAutoregressive Conditional Heteroscedasticity(G-GARCH) process, Waveletdecomposition, and Local Linear Wavelet Neural Network (LLWNN) optimized usingtwo different learning algorithms; the Backpropagation algorithm (BP) and theParticle Swarm optimization algorithm (PSO). The performance of the proposedmodel is evaluated using data from Nord Pool Electricity markets. Moreover, itis compared with some other parametric and non-parametric models in order toprove its robustness. The empirical results prove that the proposed methodperforms well than other competing techniques.

Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting