Review of machine learning applications to power systems studies

The complexity of electric power networks from generation, transmission and distribution stations in modern times has resulted to generation of big and more complex data that requires more technical and mathematical analysis because it deals with monitoring, supervisory control and data acquisition all in real time. This has necessitated the need for more accurate analysis and predictions in power systems studies especially under transient, uncertainty or emergency conditions without interference of humans. This is necessary so as to minimize errors with the aim targeted towards improving the overall performance and the need to use more technical but very intelligent predictive tools has become very relevant. Machine learning (ML) is a powerful tool which can be utilized to make accurate predictions about the future nature of data based on past experiences. ML algorithms operate by building a model (mathematical or pictorial) from input examples to make data driven predictions or decisions for the future. ML can be used in conjunction with big data to build effective predictive systems or to solve complex data analytic problems. Electricity generation forecasting systems that could predict the amount of power required at a rate close to the electricity consumption have been proposed in several works. This study seeks to review machine learning applications to power system studies. This paper reviewed applications of ML tools in power systems studies.