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