Price forecasting using computational intelligence techniques: A comparative analysis

Deregulation of Power market has initiated a multitude of reforms in the electricity sector aiming to make it more efficient, transparent and friendly to both the consumers and the suppliers. Accurate forecasting of the future electricity prices has become the most important management goal since it forms the basis of maximizing profits for the market participants. Electricity price forecasting however is a complex task due to non-linearity, non-stationarity and volatility of the price signal. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. An optimum selection amongst a large number of various input combinations and parameters is a real challenge for any modelers in using SVMs. This study applies SVM to predicting the hourly market prices of Ontario market. Optimal parameters of SVM are determined using computational intelligence techniques such as Genetic algorithm, Particle Swarm Optimization and Quantum inspired Particles Swarm Optimization (QPSO). A detailed analysis of these techniques has been performed to evaluate their robustness and ability to reach global solution in different scenarios and using different models.