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