A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin

This thesis presents a hybrid technique for predicting the AC power output from a Grid-Connected Photovoltaic (GCPV) system. Initially, the prediction was conducted using six classical Multi-Layer Feedforward Neural Network (MLFNN) models. These models were developed based on different sets of input...

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Main Author:	Nordin, Norfarizani
Format:	Thesis
Language:	English
Published:	2019
Online Access:	https://ir.uitm.edu.my/id/eprint/91415/1/91415.pdf
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id	my-uitm-ir.91415
record_format	uketd_dc
spelling	my-uitm-ir.914152024-04-16T02:00:46Z A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin 2019 Nordin, Norfarizani This thesis presents a hybrid technique for predicting the AC power output from a Grid-Connected Photovoltaic (GCPV) system. Initially, the prediction was conducted using six classical Multi-Layer Feedforward Neural Network (MLFNN) models. These models were developed based on different sets of inputs. A key feature for developing these models is the inclusion of time-series inputs. The inclusion of time-series inputs to the network is important as the solar irradiance, ambient temperature and module temperature have different time-constant; i.e. they have different rate of change as the climate changes. 2019 Thesis https://ir.uitm.edu.my/id/eprint/91415/ https://ir.uitm.edu.my/id/eprint/91415/1/91415.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Sulaiman, Shahril Irwan
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Sulaiman, Shahril Irwan
description	This thesis presents a hybrid technique for predicting the AC power output from a Grid-Connected Photovoltaic (GCPV) system. Initially, the prediction was conducted using six classical Multi-Layer Feedforward Neural Network (MLFNN) models. These models were developed based on different sets of inputs. A key feature for developing these models is the inclusion of time-series inputs. The inclusion of time-series inputs to the network is important as the solar irradiance, ambient temperature and module temperature have different time-constant; i.e. they have different rate of change as the climate changes.
format	Thesis
qualification_level	Master's degree
author	Nordin, Norfarizani
spellingShingle	Nordin, Norfarizani A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin
author_facet	Nordin, Norfarizani
author_sort	Nordin, Norfarizani
title	A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin
title_short	A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin
title_full	A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin
title_fullStr	A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin
title_full_unstemmed	A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin
title_sort	mutated hybrid cuckoo search artificial neural network for grid-connected photovoltaic system output prediction / norfarizani nordin
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/91415/1/91415.pdf
_version_	1804889835509383168

A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin

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