The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.

The objective of this study was to investigate the power generation, efficiency, and thermal stress of a thermoelectric module with leg geometry, material, segmentation, and two-stage arrangement. The effects of leg geometry, material, segmentation, and two-stage arrangement on maximum power, maximum efficiency, and maximum stress under various temperature differences and voltage load conditions were investigated. The performance parameters of the thermoelectric module were evaluated based on a numerical approach using ANSYS 19.1 commercial software. An analytical approach based on theoretical equations of the thermoelectric module was used to verify the accuracy and reliability of the numerical approach. The numerically predicted values for maximum power and maximum efficiency of the thermoelectric module were validated as ±5% and those for the maximum thermal stress of the thermoelectric module as ±7% with the corresponding calculated theoretical values. In addition, the predicted values of maximum power and maximum stress of the thermoelectric module were validated as ±2% and ±5%, respectively, with studies reported by Ma et al. and Al-Merbati et al. Of all the combinations, the single stage segmented arrangement with cylindrical leg geometry and SiGe+Bi2Te3 material was suggested as the best combination with maximum power of 0.73 W, maximum efficiency of 13.2%, and maximum thermal stress of 0.694 GPa.

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