TY - JOUR
T1 - Application of artificial intelligence for prediction, optimization, and control of thermal energy storage systems
AU - Olabi, A. G.
AU - Abdelghafar, Aasim Ahmed
AU - Maghrabie, Hussein M.
AU - Sayed, Enas Taha
AU - Rezk, Hegazy
AU - Radi, Muaz Al
AU - Obaideen, Khaled
AU - Abdelkareem, Mohammad Ali
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/3/1
Y1 - 2023/3/1
N2 - Energy storage is one of the core concepts demonstrated incredibly remarkable effectiveness in various energy systems. Energy storage systems are vital for maximizing the available energy sources, thus lowering energy consumption and costs, reducing environmental impacts, and enhancing the power grids' flexibility and reliability. Artificial intelligence (AI) progressively plays a pivotal role in designing and optimizing thermal energy storage systems (TESS). Recently, plenty of studies have been conducted to examine the feasibility of applying artificial intelligence techniques, such as particle swarm optimization (PSO), artificial neural networks (ANN), square vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), in the energy storage sector. This study introduces the classifications, roles, and efficient design optimization of energy systems in various applications using different artificial intelligence approaches. This study discusses the progress made regarding implementing artificial intelligence and its sub-categories for optimizing, predicting, and controlling the performance of energy systems that contain thermal energy storage facilities. In addition, the performance of these technologies is thoroughly analyzed, highlighting their noticeable accuracy while carrying out different objectives. Recommendations and future research points are introduced to offer new concepts and inspiration for the application of AI in TESS.
AB - Energy storage is one of the core concepts demonstrated incredibly remarkable effectiveness in various energy systems. Energy storage systems are vital for maximizing the available energy sources, thus lowering energy consumption and costs, reducing environmental impacts, and enhancing the power grids' flexibility and reliability. Artificial intelligence (AI) progressively plays a pivotal role in designing and optimizing thermal energy storage systems (TESS). Recently, plenty of studies have been conducted to examine the feasibility of applying artificial intelligence techniques, such as particle swarm optimization (PSO), artificial neural networks (ANN), square vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), in the energy storage sector. This study introduces the classifications, roles, and efficient design optimization of energy systems in various applications using different artificial intelligence approaches. This study discusses the progress made regarding implementing artificial intelligence and its sub-categories for optimizing, predicting, and controlling the performance of energy systems that contain thermal energy storage facilities. In addition, the performance of these technologies is thoroughly analyzed, highlighting their noticeable accuracy while carrying out different objectives. Recommendations and future research points are introduced to offer new concepts and inspiration for the application of AI in TESS.
KW - Artificial intelligence
KW - Artificial neural networks
KW - Energy efficiency
KW - Energy storage
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85150824861&partnerID=8YFLogxK
U2 - 10.1016/j.tsep.2023.101730
DO - 10.1016/j.tsep.2023.101730
M3 - Review article
AN - SCOPUS:85150824861
SN - 2451-9049
VL - 39
JO - Thermal Science and Engineering Progress
JF - Thermal Science and Engineering Progress
M1 - 101730
ER -
