Integration Of Logistic Regression And Multi-Layer Perceptron For Single And Dual Axis Solar Tracking Systems
Intelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. However, different solar tracking variables have been employed to build those intelligent solar tracking systems without considering the dominant and optimum ones. In...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://eprints.usm.my/56155/1/Integration%20Of%20Logistic%20Regression%20And%20Multi-Layer%20Perceptron%20For%20Single%20And%20Dual%20Axis%20Solar%20Tracking%20Systems_Nadia%20A.%20Al-Rousan.pdf |
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| Summary: | Intelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. However, different solar tracking variables have been employed to build those intelligent solar tracking systems without considering the dominant and optimum ones. In addition, several low performance intelligent solar tracking systems have been designed and implemented due to the inappropriate combination of solar tracking variables and intelligent classifiers to drive the solar trackers. Thus, this research aims to (i) investigate and evaluate the most effective and dominant variables on solar tracking systems, (ii) investigate the appropriate combination of solar variables and intelligent classifier for solar tracking systems, (iii) propose new solar tracking systems by integrating supervised and unsupervised intelligent classifiers. The results revealed that month, day, and time are the most effective variables for single and dual axis solar tracking systems. By using these variables, this study has successfully integrated between multi-layer perceptron (MLP) or cascade multi-layer perceptron (CMLP) and trained logistic regression (LR) models. The proposed MLP-LR system is able to increase the prediction rate of MLP network to 99.13% for single axis tracking systems (i.e. which is 2.35% of improvement). The system is also able to decrease the mean square error (MSE) rate to 0.010 × 10−2 as compared to value of MSE for the conventional MLP. In addition, the proposed CMLP-LR system is able to increase the prediction rate of CMLP network to 99.19% for dual axis tracking system (i.e. 1.23% of improvement), while the MSE rate is decreased to 6.250 × 10−5 as compared to value of MSE for the conventional CMLP. The new developed models achieved less number of overall connections (i.e. which are 77.58% and 86.16% of improvement for MLP and CMLP respectively), less number of neurons (i.e. 63.51% of improvement for both MLP and CMLP), and less time complexity (i.e. which are 70.40% and 99% of improvement for MLP and CMLP respectively). 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). |
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