Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

AL-Rousan N., Mat Isa N. A., Mat Desa M. K., AL-Najjar H.

International Journal of Intelligent Systems, vol.36, no.10, pp.5605-5669, 2021 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 10
  • Publication Date: 2021
  • Doi Number: 10.1002/int.22525
  • Journal Name: International Journal of Intelligent Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.5605-5669
  • Keywords: complexity, intelligent systems, logistic regression, multilayer perceptron, neural network, single and dual axis
  • Istanbul Gelisim University Affiliated: Yes


© 2021 Wiley Periodicals LLCIntelligent 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.