Application of artificial intelligence for prediction, optimization, and control of thermal energy storage systems

• Published in: Thermal science and engineering progress Vol. 39; p. 101730
• Main Authors: Olabi, A.G., Abdelghafar, Aasim Ahmed, Maghrabie, Hussein M., Sayed, Enas Taha, Rezk, Hegazy, Radi, Muaz Al, Obaideen, Khaled, Abdelkareem, Mohammad Ali
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2023
• Subjects: Artificial intelligence; Artificial neural networks; Energy efficiency; Energy storage; Optimization; MPC; FFC; FLFFC; GHI; CHP; DP; PVT; HVAC; RNN; ANFIS; SNEG; NARX; R2; FFNN; AC; Artificial neural networks; AI; MAPE; PSO; ES; MAE; RH; TESS; Energy efficiency; HTF; PCM; SAH; PTC; Qcool; Energy storage; CFD; 5GDHC; ANN; MRE; GEHP; GMDH; SVM; BP; Optimization; RDC; DHW; ANNi; VDF; GA; SP; MSE; CSP; LSTM; ESS; COP; SOC; Nu; CL; RMSE; CCHP; Artificial intelligence
• ISSN: 2451-9049; 2451-9049
• DOI: 10.1016/j.tsep.2023.101730

•Different types of Artificial Intelligence Techniques are presented.•Artificial Intelligence Techniques for ESS are presented.•Analysis, design, operation, optimization, and control of ESS are studied.•Multiple independent parameters affecting the performance of ESS are reviewed. 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.