MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04618ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20261005145103.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
261005t20262026my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0010810 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FTKEE .A45 2026 r Thesis |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Amirah Nurhafizah Binti Abu Bakar, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Artificial neural network-based solar irradiance measurement system applied for photovoltaic modules / |
| Statement of responsibility, etc. |
Amirah Nurhafizah Binti Abu Bakar |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2026 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2026 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 99 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-COM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Electrical and Electronics Engineering Technology |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2026 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
In 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 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Electrical and Electronics Engineering Technology |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Thesis |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |