Artificial intelligence and machine learning in energy systems: A bibliographic perspective

Economic development and the comfort-loving nature of human beings in recent years have resulted in increased energy demand. Since energy resources are scarce and should be preserved for future generations, optimizing energy systems is ideal. Still, due to the complexity of integrated energy systems, such a feat is by no means easy. Here is where computer-aided decision-making can be very game-changing in determining the optimum point for supply and demand. The concept of artificial intelligence (AI) and machine learning (ML) was born in the twentieth century to enable computers to simulate humans' learning and decision-making capabilities. Since then, data mining and artificial intelligence have become increasingly essential areas in many different research fields. Naturally, the energy section is one area where artificial intelligence and machine learning can be very beneficial. This paper uses the VOSviewer software to investigate and review the usage of artificial intelligence and machine learning in the energy field and proposes promising yet neglected or unexplored areas in which these concepts can be used. To achieve this, the 2000 most recent papers in addition to the 2000 most cited ones in different energy-related keywords were studied and their relationship to AI-and ML-related keywords was visualized. The results revealed different research trends in recent years from the basic to more cutting-edge topics and revealed many promising areas that are yet to be explored. Results also showed that from the com-mercial aspect, patents submitted for artificial intelligence and machine learning in energy-related areas had a sharp increase.

Automated summary

Economic development and human beings' desire for comfort have led to increased energy demand. Optimizing energy systems is crucial due to scarce resources. Artificial intelligence and machine learning have the potential to be game-changers in determining the optimal point for supply and demand. This paper investigates and reviews the usage of AI and ML in the energy field and proposes promising yet unexplored areas where these concepts can be applied.