A hybrid swarm-machine intelligence approach for day ahead price forecasting. (3rd January 2012)

• Record Type: Journal Article
• Authors: Shrivastava, Nitin Anand; Panigrahi, Bijay Ketan
• Is Part Of: International journal of innovative computing and applications. Volume 3:Number 4(2011)
• Page Start: 177
• Page End: 187
• Publication Date: 2012-01-03
• Subjects: deregulation -- day ahead market -- hourly Ontario energy price -- HOEP -- price forecasting -- machine intelligence -- swarm intelligence -- genetic algorithm -- GA -- particle swarm optimisation -- PSO -- support vector machine -- SVM; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Accurate forecasting of the future electricity prices in deregulated markets has become the most important management goal since it forms the basis of maximising 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 machine intelligence technique that has 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 modeller in using SVMs. This study applies SVM to predict the hourly electricity prices of Ontario market. Optimal parameters of SVM are determined using swarm intelligence techniques. Some strategies are also developed specifically for day ahead market price forecasting considering data availability, the dynamics of price movement and forecasting horizon. A detailed analysis of a hybrid technique clubbing together the machine and swarm intelligence technique has been performed with different scenarios and strategies.