Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability - Le, Thanh Tuan; Priya, Jayabal Chandra; Le, Huu Cuong; Le, Nguyen Viet Linh; Duong, Minh Thai; Cao, Dao Nam (Center of Biomass & Renewable Energy (CBIORE), 2024)
The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.