A Review of Machine Learning Applications in Electricity Market Studies

Abstract

Liberalized electricity markets have been studied for the past few decades with different mathematical techniques. Operating these markets under the growing uncertainties has been challenging in many jurisdictions. Given the recent advances in machine learning techniques and big-data analysis, applications of such techniques have been growing in recent years. In this paper we review state-of-the-art developments of machine learning techniques and their applications in electricity market studies. We briefly provide current market challenges around the world. Then we show how these challenges are addressed using different machine learning approaches. Later, we provide a comparative table where all relevant papers are compared. This table can guide future studies on the machine learning applications in electricity markets by highlighting the promising potential areas. Then, we suggest directions for future researches to pursue machine learning applications in electricity market. Consequently, these approaches can be employed to resolve the uncertainties.