Abstract
With increased competitiveness in energy generation industries, more resources are directed in optimizing plant operation in all aspects of the production, including fault detection and diagnosis, increase efficiency, forecasting the consumption and production. One of the most powerful tools in optimizing plant operation is artificial intelligence (AI). For the last few decades there has been major interest towards intelligent condition monitoring system (ICMS) application in power plant especially with AI development particularly in artificial neural network (ANN). It should be noted that the development of the energy industry is a step towards the development of other industries. That is why the transition to the digital industry is impossible without the digitalization and intellectualization of the energy industry. With massive possibility and room for improvement in AI, the inspiration for re-searching them are apparent, and literally, hundreds of pa-pers have been published, discussing the findings of hybrid AI for condition monitoring purposes. This paper attempts to discuss and review related work of AI and its applica-tion in energy industry. With regard to the energy indus-try, the integration of artificial intelligence in the industry will help optimize and improve efficiency in all aspects of the production, transmission and consumption of energy, fault detection and diagnosis, increase efficiency, forecast-ing the consumption and production. This note provides an overview of AI methods utilized for energy sector applica-tions, based on a systematic review of over 15 papers, 3 companies and commercial initiatives. The papers are clas-sified with regards to both the AI/ML algorithm(s) used and the application area in energy industry. We conclude the paper with a discussion of advantages and potential limitations of reviewed AI techniques for different tasks, and outlines directions for future research in this fast-growing area.