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008			261005t20262026my a|||fr|||| 000 0 eng d
020			_aTHE0010810 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKEE .A45 2026 r Thesis
100			_aAmirah Nurhafizah Binti Abu Bakar, _eauthor.
245	1	0	_aArtificial neural network-based solar irradiance measurement system applied for photovoltaic modules / _cAmirah Nurhafizah Binti Abu Bakar
264		1	_aKuantan, Pahang : _bUMPSA, _c2026
264		4	_c© 2026
300			_axiv, 99 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-COM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Electrical and Electronics Engineering Technology
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2026
504			_aIncludes bibliographical references
520	3		_aIn recent years, solar photovoltaic power generation has been widely adopted across many countries. To ensure the security and reliability of solar power systems, energy monitoring facilities have been established to promptly address any issues that arise. Solar irradiance is typically measured using conventional equipment such as pyranometers and solarimeters, which are commercially available. However, these traditional sensors and devices often fail to account for real-world factors like dirt accumulation, dust, micro-cracks, and module degradation. This leads to discrepancies between measured and actual irradiance values. Up to recent, numerous studies have been published on solar irradiance prediction. However, it is rare to find research that extensively integrates the development of solar radiation sensors with predictive modeling. A reliable solution that aligns theoretical irradiance measurements with real-world field conditions has yet to be realized. To address this challenge, this study introduces an Artificial Neural Network (ANN)-based solar irradiance measurement system to accurately measure the actual solar irradiance reaching the top surface of solar modules. This system captures current and voltage values, which serve as inputs to the ANN using the nntool in MATLAB. The ANN is designed with four hidden neurons, and the dataset is divided into 80% for training, 10% for testing, and 10% for validation. Comparisons between the ANN method and the Adaptive Neuro-Fuzzy Inference System (ANFIS) method were conducted to validate the ANN-based system's performance using metrics such as correlation coefficient (R), coefficient of determination (R²), root mean square error (RMSE), and mean bias error (MBE). The model with the lowest RMSE and MBE values, along with the highest R and R² values, was selected as the best measurement system for predicting actual solar irradiance. The results from 660mW, 10W and 100W solar module demonstrate that the ANN-based solar irradiance measurement system outperforms ANFIS, achieving R, R², RMSE, and MBE values of 0.9699, 0.9406, 62.2163 W/m2, and -1.5611 W/m2, respectively. The results indicate that the ANN model demonstrated better performance than the ANFIS model, achieving a 6.19% lower RMSE, a 0.87% higher R², a 0.21% higher R, and a 90.7% reduction in bias (MBE). These improvements indicate higher prediction accuracy, reliability, and robustness of the ANN model. The system was further validated using a 50W solar module, proving its compatibility with various solar module specifications and orientations. The proposed ANN-based solar irradiance measurements by demonstrating robust predictive accuracy and generalizability. The system achieves a high correlation coefficient and coefficient of determination, indicating strong agreement between predicted and actual values. Additionally, the RMSE of 62.2163 W/m2 and MBE of -1.5611 W/m2 highlight the model’s ability to maintain both precision and minimal bias in its predictions. The model’s validation across solar modules with varying power ratings (660mW, 10W, 50W and 100W) further emphasizes its adaptability and effectiveness in accommodating diverse specifications and orientations, thereby demonstrating its potential for practical application in dynamic real-world conditions.
610	2	0	_aFaculty of Electrical and Electronics Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aThesis _xDissertations
942			_2lcc _cTHESIS
999			_c105667 _d105673