Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz

The electrical system photovoltaic (PV) modules for special design considerations due to unpredictable and sudden changes in weather conditions such as the solar irradiation level as well as the cell operating temperature. This thesis presents a practical and reliable approach for the prediction of...

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Main Author:	Aziz, Muhammad Aidil Adha
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Photovoltaic power systems
Online Access:	https://ir.uitm.edu.my/id/eprint/84302/1/84302.pdf
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id	my-uitm-ir.84302
record_format	uketd_dc
spelling	my-uitm-ir.843022024-07-30T01:57:27Z Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz 2019 Aziz, Muhammad Aidil Adha Photovoltaic power systems The electrical system photovoltaic (PV) modules for special design considerations due to unpredictable and sudden changes in weather conditions such as the solar irradiation level as well as the cell operating temperature. This thesis presents a practical and reliable approach for the prediction of PV power output using an intelligent-based technique namely Cuckoo Search Algorithm - Least Square Support Vector Machine (CS-LSSVM). Available historical output power data are analyzed and appropriate features are selected for the model. There are two input vectors to the model consist of solar irradiation and ambient temperature. Therefore, Cuckoo Search Algorithm (CS) is hybrid with LS-SVM in order to optimize the RBF parameters for a better prediction performance. The CS algorithm is inspired by the life of a bird family, called Cuckoo. This algorithm imitated from the effort of the cuckoos to survive. The performance of CS-LSSVM is compared with those obtained from LS-SVM using cross-validation technique in terms of accuracy. In this thesis, Mean Absolute Percentage Error (MAPE) is used to quantify the performance of the prediction. Besides that, evaluation also carried out by calculating the correlation of determination. The historical PV data is utilized to validate the workability of the proposed technique. The results showed that CS-LSSVM provides better performance in predicting photovoltaic system power output as compared to conventional LS-SVM using cross-validation technique. 2019 Thesis https://ir.uitm.edu.my/id/eprint/84302/ https://ir.uitm.edu.my/id/eprint/84302/1/84302.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Mat Yasin, Zuhaila Zakaria, Zuhaina
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mat Yasin, Zuhaila Zakaria, Zuhaina
topic	Photovoltaic power systems
spellingShingle	Photovoltaic power systems Aziz, Muhammad Aidil Adha Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz
description	The electrical system photovoltaic (PV) modules for special design considerations due to unpredictable and sudden changes in weather conditions such as the solar irradiation level as well as the cell operating temperature. This thesis presents a practical and reliable approach for the prediction of PV power output using an intelligent-based technique namely Cuckoo Search Algorithm - Least Square Support Vector Machine (CS-LSSVM). Available historical output power data are analyzed and appropriate features are selected for the model. There are two input vectors to the model consist of solar irradiation and ambient temperature. Therefore, Cuckoo Search Algorithm (CS) is hybrid with LS-SVM in order to optimize the RBF parameters for a better prediction performance. The CS algorithm is inspired by the life of a bird family, called Cuckoo. This algorithm imitated from the effort of the cuckoos to survive. The performance of CS-LSSVM is compared with those obtained from LS-SVM using cross-validation technique in terms of accuracy. In this thesis, Mean Absolute Percentage Error (MAPE) is used to quantify the performance of the prediction. Besides that, evaluation also carried out by calculating the correlation of determination. The historical PV data is utilized to validate the workability of the proposed technique. The results showed that CS-LSSVM provides better performance in predicting photovoltaic system power output as compared to conventional LS-SVM using cross-validation technique.
format	Thesis
qualification_level	Master's degree
author	Aziz, Muhammad Aidil Adha
author_facet	Aziz, Muhammad Aidil Adha
author_sort	Aziz, Muhammad Aidil Adha
title	Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz
title_short	Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz
title_full	Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz
title_fullStr	Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz
title_full_unstemmed	Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz
title_sort	prediction of photovoltaic system output using hybrid cuckoo search least square support vector machine / muhammad aidil adha aziz
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/84302/1/84302.pdf
_version_	1811768696374296576

Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz

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