Prediction of operating photovoltaic module temperature using hybrid Cuckoo Search algorithm: artificial neural network / Nur Zahidah Zainol
This project presents a hybrid Cuckoo Search-Artificial Neural Network (CSANN) for predicting the module operating temperature of a Grid-Connected Photovoltaic (GCPV) system. In this project, the ANN used ambient temperature (AT) and solar irradiance (SI) as the inputs and module temperature (MT) as...
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| Format: | Thesis |
| Language: | en |
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2014
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| Online Access: | https://ir.uitm.edu.my/id/eprint/85281/1/85281.pdf https://ir.uitm.edu.my/id/eprint/85281/ |
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| _version_ | 1833078688031178752 |
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| author | Zainol, Nur Zahidah |
| author_facet | Zainol, Nur Zahidah |
| author_sort | Zainol, Nur Zahidah |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This project presents a hybrid Cuckoo Search-Artificial Neural Network (CSANN) for predicting the module operating temperature of a Grid-Connected Photovoltaic (GCPV) system. In this project, the ANN used ambient temperature (AT) and solar irradiance (SI) as the inputs and module temperature (MT) as the main output. Furthermore, Cuckoo Search (CS) was utilized to determine the optimal number of neurons, learning rate and momentum rate in the hidden layer throughout training process of Cuckoo Search such that Mean Absolute Percentage Error (MAPE) of the prediction was minimized. After the training process, testing was performed to validate the ANN training. The results indicated that the proposed hybrid CS-ANN had outperformed a hybrid Artificial Bee Colony-Artificial Neural Network (ABC-ANN) in producing lower MAPE. In addition, the coefficient of determination was discovered to be very close to unity such that a high prediction performance could be guaranteed. |
| format | Thesis |
| id | my.uitm.ir-85281 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2014 |
| record_format | eprints |
| spelling | my.uitm.ir-852812024-02-14T02:47:36Z https://ir.uitm.edu.my/id/eprint/85281/ Prediction of operating photovoltaic module temperature using hybrid Cuckoo Search algorithm: artificial neural network / Nur Zahidah Zainol Zainol, Nur Zahidah This project presents a hybrid Cuckoo Search-Artificial Neural Network (CSANN) for predicting the module operating temperature of a Grid-Connected Photovoltaic (GCPV) system. In this project, the ANN used ambient temperature (AT) and solar irradiance (SI) as the inputs and module temperature (MT) as the main output. Furthermore, Cuckoo Search (CS) was utilized to determine the optimal number of neurons, learning rate and momentum rate in the hidden layer throughout training process of Cuckoo Search such that Mean Absolute Percentage Error (MAPE) of the prediction was minimized. After the training process, testing was performed to validate the ANN training. The results indicated that the proposed hybrid CS-ANN had outperformed a hybrid Artificial Bee Colony-Artificial Neural Network (ABC-ANN) in producing lower MAPE. In addition, the coefficient of determination was discovered to be very close to unity such that a high prediction performance could be guaranteed. 2014 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/85281/1/85281.pdf Prediction of operating photovoltaic module temperature using hybrid Cuckoo Search algorithm: artificial neural network / Nur Zahidah Zainol. (2014) Degree thesis, thesis, Universiti Teknologi MARA (UiTM). |
| spellingShingle | Zainol, Nur Zahidah Prediction of operating photovoltaic module temperature using hybrid Cuckoo Search algorithm: artificial neural network / Nur Zahidah Zainol |
| title | Prediction of operating photovoltaic module temperature using hybrid Cuckoo Search algorithm: artificial neural network / Nur Zahidah Zainol |
| title_full | Prediction of operating photovoltaic module temperature using hybrid Cuckoo Search algorithm: artificial neural network / Nur Zahidah Zainol |
| title_fullStr | Prediction of operating photovoltaic module temperature using hybrid Cuckoo Search algorithm: artificial neural network / Nur Zahidah Zainol |
| title_full_unstemmed | Prediction of operating photovoltaic module temperature using hybrid Cuckoo Search algorithm: artificial neural network / Nur Zahidah Zainol |
| title_short | Prediction of operating photovoltaic module temperature using hybrid Cuckoo Search algorithm: artificial neural network / Nur Zahidah Zainol |
| title_sort | prediction of operating photovoltaic module temperature using hybrid cuckoo search algorithm: artificial neural network / nur zahidah zainol |
| url | https://ir.uitm.edu.my/id/eprint/85281/1/85281.pdf https://ir.uitm.edu.my/id/eprint/85281/ |
| url_provider | http://ir.uitm.edu.my/ |