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Comparison of Various Machine Learning Algorithms for Predicting Energy Price in Open Electricity Market
Chaudhury P., Tyagi A.,
Published in Institute of Electrical and Electronics Engineers Inc.
This paper focus on exploring the possibilities for prediction of energy prices using various machine learning (ML) algorithms. Earlier, this problem has been tackled using various numerical methods like quadratic and cubic spline interpolation, etc. However, the accuracy of prediction has always been sub-par due to the limited capacity of such models. In this work, the focus is on exploring the possibilities of predicting the energy price in the open electricity market using four different algorithms namely: Simple Linear Regression, Support Vector Machines (SVM), K nearest neighbor, and Long Short-Term Memory. The main contribution of this work is to develop an ML system that can predict future prices. Realtime data are obtained from the Indian Energy Exchange (IEX) which handles around 30% of energy transactions through online within India under open access. The results are validated from the same which ensures the proper validation of the proposed model. The four models on the Indian Energy Exchange dataset are trained and the results are compared to find the best algorithm with the highest accuracy. © 2020 IEEE.