Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in National electricity market

The parameter selection is very important for successful modelling of input–output relationship in a function approximation model. In this study, support vector machine (SVM) has been used as a function approximation tool for a price series and genetic algorithm (GA) has been utilised for optimisation of the parameters of the SVM model. Instead of using single time series, separate time series for each trading interval has been employed to model each day's price profile, and SVM parameters of these separate series have been optimised using GA. The developed model has been applied to two large power systems from National electricity market (NEM) of Australia. The forecasting performance of the proposed model has been compared with a heuristic technique, a linear regression model and the other reported works in the literature. Effect of price volatility on the performance of the models has also been analysed. Testing results show that the proposed GA–SVM model has better forecasting ability than the other forecasting techniques.