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Since solar energy is intermittent, finding the best solutions is a challenge. This paper provides a thorough review of solar energy systems' generic optimization objectives. The intelligent optimization techniques for solar energy systems are discussed, including their functions, constraints, contributions, mathematical models, and analysis methods. Optimization studies using new and traditional generation techniques are analyzed, and a few optimization methods, including combined hybrid algorithms, are presented. New generation artificial intelligence algorithms have been most widely used during the last decade, needing less computational time. They have good convergence and better accuracy than traditional optimization methods. They can scan local and global optima and do robust calculations. Solar system optimization has demonstrated remarkable benefits in size, load demand, and electricity output. The improvements reduce operating expenditures, power losses, and peak output integration and controllability. With a 50% rise in power prices, the optimal number of solar collectors rises by approximately 25%. However, with adjustment as per optimization techniques, the solar absorption cooling system's maximum thermal efficiency can be increased up to 75%.The present study recommends using two or more algorithms to overcome the curbs of a single algorithm. The main aim of optimization strategies, according to this assessment, is to reduce capital expenditures, operation and maintenance expenses, and emissions while improving system reliability. The paper also briefly describes several solar energy optimization challenges and issues. Lastly, some practical future approaches for developing a reliable and efficient solar power system are proposed for developing the complex renewable energy-based hybrid system.

Since solar energy is intermittent, finding the best solutions is a challenge. This paper provides a thorough review of solar energy systems' generic optimization objectives. The intelligent optimization techniques for solar energy systems are discussed, including their functions, constraints, contributions, mathematical models, and analysis methods. Optimization studies using new and traditional generation techniques are analyzed, and a few optimization methods, including combined hybrid algorithms, are presented. New generation artificial intelligence algorithms have been most widely used during the last decade, needing less computational time. They have good convergence and better accuracy than traditional optimization methods. They can scan local and global optima and do robust calculations. Solar system optimization has demonstrated remarkable benefits in size, load demand, and electricity output. The improvements reduce operating expenditures, power losses, and peak output integration and controllability. With a 50% rise in power prices, the optimal number of solar collectors rises by approximately 25%. However, with adjustment as per optimization techniques, the solar absorption cooling system's maximum thermal efficiency can be increased up to 75%.The present study recommends using two or more algorithms to overcome the curbs of a single algorithm. The main aim of optimization strategies, according to this assessment, is to reduce capital expenditures, operation and maintenance expenses, and emissions while improving system reliability. The paper also briefly describes several solar energy optimization challenges and issues. Lastly, some practical future approaches for developing a reliable and efficient solar power system are proposed for developing the complex renewable energy-based hybrid system.

The ability of phase change materials (PCM) to store thermal energy has gained wide application area, like battery thermal management, solar water desalination and many other. The melting process of beeswax phase change material within the cube geometry with constant wall temperature (65 °C) boundary condition has been investigated using solidification and melting model. The fluid flow and heat transfer governing equations are solved using second order finite volume scheme. A PRESTO algorithm is applied for pressure-velocity coupling. The convergence criteria of 10−10 have been selected for energy equation, while 10−8 is selected for both momentum and continuity equations. The results like percentage variation along length-height and height-width plane for transient liquid fraction and temperature has been plotted, along with velocity streamlines within the cube geometry. From the obtained results it is concluded that the melting fraction and temperature of beeswax PCM is different in different planes and the major factors which affect the complete melting process is wall temperature, and the geometry. A difference of more than 0.1 °C in temperature has been recorded between mid-length-height and height-width plane while a difference of more than 2% in liquid fraction of PCM is observed. Even the uniformity of temperature and liquid fraction is notably influenced and vary along length, height, and width of cube geometry. Thus, it is concluded that melting process of PCM may affect the ability to store and release the heat energy which further affect the performance parameters of applied physical system.

The ability of phase change materials (PCM) to store thermal energy has gained wide application area, like battery thermal management, solar water desalination and many other. The melting process of beeswax phase change material within the cube geometry with constant wall temperature (65 °C) boundary condition has been investigated using solidification and melting model. The fluid flow and heat transfer governing equations are solved using second order finite volume scheme. A PRESTO algorithm is applied for pressure-velocity coupling. The convergence criteria of 10−10 have been selected for energy equation, while 10−8 is selected for both momentum and continuity equations. The results like percentage variation along length-height and height-width plane for transient liquid fraction and temperature has been plotted, along with velocity streamlines within the cube geometry. From the obtained results it is concluded that the melting fraction and temperature of beeswax PCM is different in different planes and the major factors which affect the complete melting process is wall temperature, and the geometry. A difference of more than 0.1 °C in temperature has been recorded between mid-length-height and height-width plane while a difference of more than 2% in liquid fraction of PCM is observed. Even the uniformity of temperature and liquid fraction is notably influenced and vary along length, height, and width of cube geometry. Thus, it is concluded that melting process of PCM may affect the ability to store and release the heat energy which further affect the performance parameters of applied physical system.

This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.

This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.