Short term price forecasting using tree based methods

Abstract

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 the accuracy is concerned. © 2019 An international journal of advanced computer technology.