• A K Srivastava
  • D Singh
  • A S Pandey
  • Sanjay Kumar IIT(BHU), Varanasi
Keywords: Price forecasting, Data mining toolbox, weka, J48, Random forest, Bagging


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.


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Author Biography

Sanjay Kumar, IIT(BHU), Varanasi
Sanjay Kumar received his B. E. Degree in Electrical Engineering from Government Engineering College, Rewa(M.P.) India in 1997; M.Tech. Degree with specialization in Electrical Machines & Drives from Indian Institute of Technology(B.H.U.) Varanasi (U.P.) India in 2010. Recently he is a research scholar in the Department of Electrical Engineering, Indian Institute of Technology(B.H.U.) Varanasi (U.P.)India. His research area includes Electrical Machines & Drives  and Power System Protection.


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How to Cite
Srivastava, A. K., Singh, D., Pandey, A. S., & Kumar, S. (2019). SHORT TERM PRICE FORECASTING USING TREE BASED METHODS. COMPUSOFT: An International Journal of Advanced Computer Technology, 8(1). Retrieved from