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A K Srivastava, D Singh, A S Pandey, Sanjay Kumar


In this paper, electricity price forecasting using J48, Random forest and Bagging are used to effectively forecast the electricity price. These models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. The effectiveness of the proposed methods has been validated through comprehensive tests using real price data from Australian electricity market. The comparison of these methods shows that the bagging is having an edge as for as accuracy is concerned.


Price forecasting; Data mining toolbox; weka; J48; Random forest; Bagging

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D. W. Bunn, “Forecasting loads and prices in competitive power markets,” in Proceedings of the IEEE, vol. 88, no. 2, pp. 163-169, Feb. 2000.

Singh, Nitin & R. Mohanty, S. (2015). A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets. Journal of Power and Energy Engineering, Vol. 03, pp 1-19, 2015

Breimann, L., Friedman, J.H., Olshem, J.H., Stone, C.J.: ‘Classification and regression trees’ (Chapman and Hall/CRC, 1984)

A. Khotanzad, E. Zhou and H. Elragal, “A Neuro-Fuzzy Approach to Short-Term Load Forecasting in a Price-Sensitive Environment”, IEEE Transactions on Power Systems, Vol. 17, No. 4, pp. 1273-1282, November 2002,

M. K. Kim, "Short-term price forecasting of Nordic power market by combination Levenberg–Marquardt and Cuckoo search algorithms," in IET Generation, Transmission & Distribution, vol. 9, no. 13, pp. 1553-1563, 1 10 2015.

P. Sarikprueck, W. Lee, A. Kulvanitchaiyanunt, V. C. P. Chen and J. Rosenberger, "Novel Hybrid Market Price Forecasting Method With Data Clustering Techniques for EV Charging Station Application," in IEEE Transactions on Industry Applications, vol. 51, no. 3, pp. 1987-1996, May-June 2015.

E. E. Elattar, "Day-ahead price forecasting of electricity markets based on local informative vector machine," in IET Generation, Transmission & Distribution, vol. 7, no. 10, pp. 1063-1071, October 2013.

C. González, J. Mira-McWilliams and I. Juárez, "Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests," in IET Generation, Transmission & Distribution, vol. 9, no. 11, pp. 1120-1128, 6 8 2015.

Hong-Tzer Yang and Chao-Ming Huang, “A New Short-Term Load Forecasting Approach Using Self-organizing Fuzzy ARMAX Models”, IEEE Transactions on Power Systems, Vol. 13, No. 1, February 1998, pp. 217-225.



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