Yazar "Isa, Nor Ashidi Mat" seçeneğine göre listele

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    Correlation analysis and MLP/CMLP for optimum variables to predict orientation and tilt angles in intelligent solar tracking systems
    (WILEY-HINDAWI, ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON WIT 5HE, ENGLAND, 2021) Al-Rousan, Nadia; Isa, Nor Ashidi Mat; Desa, Mohd Khairunaz Mat
    Different solar tracking variables have been employed to build intelligent solar tracking systems without considering the dominant and optimum ones. Thus, several low performance intelligent solar tracking systems have been designed and implemented due to the inappropriate combination of solar tracking variables and intelligent predictors to drive the solar trackers. This research aims to investigate and evaluate the most effective and dominant variables on dualand single-axis solar trackers and to find the appropriate combination of solar variables and intelligent predictors. The optimum variables will be found by using correlation results between different variables and both orientation and tilt angles. Then, to use the selected variables to develop different intelligent solar trackers. The results revealed that month, day, and time are the most effective variables for horizontal single-axis and dual-axis solar tracking systems. Using these variables in cascade multilayer perceptron (CMLP) and multilayer perceptron (MLP) produced high performance. These predictors could predict both orientation and tilt angles efficiently. It is found that day variable is very effective to increase the performance of solar trackers although day variable is neither correlated nor significant with both orientation and tilt angles. Linear regression predicted less than 70% of the given data in most cases, whereas nonlinear models could predict the optimum orientation and tilt angles. In single-axis tracker, month, day, and time variables achieved prediction rates of 96.85% and 96.83% for three hidden layers of MLP and CMLP, respectively, whereas the MSE are 0.0025 and 0.0008, respectively. In dual-axis solar tracker, MLP and CMLP predicted 96.68% and 97.98% respectively, with MSE of 0.0007 for both.
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    Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems
    (WILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ, 2021) Al-Rousan, Nadia; Isa, Nor Ashidi Mat; Desa, Mohammad Khairunaz Mat; Al-Najjar, Hazem
    Intelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. Several low performance intelligent solar tracking systems have been designed and implemented. Multilayer perceptron (MLP) is one of the common controllers that used to drive solar tracking systems. However, when the input data are complex for neural network, neural network would not well explain the relationship between these data. Thus, it performed worse than when the input data are simple. Using a premapping of relationship between samples of data as input to neural network along with the original input data could probably a strong guide to help neural network to reach the desired goal and predict the output variables faster and more accurate. It is found that using the output of logistic regression as input to neural network would faster the process of finding the predicted output by neural network. Thus, this study aims to propose new efficient and low complexity single and dual axis solar tracking systems by integrating supervised logistic regression (LR) and supervised MLP or cascade multilayer perceptron (CMLP). LR models are trained by using one of unsupervised clustering techniques (k?means, fuzzy c?means, and hierarchical clustering algorithms). The proposed models were used to predict both tilt and orientation angles by two different data sets (month, day, and time variables data set) and (month, day, time, Isc, Voc, and power radiation variables data sets). The results revealed that the proposed MLP/CMLP?LR systems are able to increase the prediction rate and decrease the mean square error rate as compared to conventional models in both single and dual axis solar tracking systems. The new developed intelligent systems achieved less number of overall connections, less number of neurons, and less time complexity. The finding suggests that the proposed intelligent solar tracking systems has a great potential to be applied for real?world applications (i.e., solar heating systems, solar lightening systems, factories, and solar powered ventilation.