Particle Swarm Optimization for Least Square Support Vector Machine in MediumTerm Electricity Price Prediction

Abstract

Predicting electricity price has now become an important task for planning and maintenance of power system. In medium-term forecast, electricity price can be predicted for several weeks ahead, up to a few months or a year ahead. It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario. However, the research on medium-term price forecast have also exhibited low forecast accuracy due to the limited historical data for training and testing purposes. Therefore, an optimization technique using Particle Swarm Optimization (PSO) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimized LSSVM parameters. After thorough database mining in English language, no literature has been found on parameter optimization using LSSVM-PSO for medium-term price prediction. The model was examined on the Ontario power market which was reported as among the most volatile market worldwide. Monthly average of Hourly Ontario Electricity Price (HOEP) for the past 12 months and month index were selected as the input. The developed LSSVM-PSO showed higher forecast accuracy with lower complexity than the existing models.