Abstract
Modern power systems should provide a reliable electric energy supply while preserving the environment and maximum profit in the conditions of a liberal electricity market and a constant increase in electricity production from renewable sources. In order to make best business decisions, it is necessary to analyze archive and real-time data. Implementation of IoT in the industrial sector (Industrial Internet of Things - IIoT) and integration of numerous intelligent measurement systems and sensors, cause the significant increase in the amount of data that needs to be analyzed and processed and often exceeds existing professional resources. Expert systems, intelligent monitor and control systems, artificial intelligence and machine learning (ML) algorithms play a crucial role in the process of analyzing and processing large amounts of data and potential automatic decision-making. The paper first gives an overview of ML methods, and then attention is focused on the practical application of ML algorithms in the power systems. Notable examples of the application of ML algorithms are: renewable sources’ production forecasting, load forecasting, forecasting the price of electrical energy, planning and schedule of production capacities, intelligent predictive maintenance, prediction of failure and aging of equipment, as well as the application of ML algorithms in the field of cybersecurity and risk management.
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