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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
https://ir.uitm.edu.my/id/eprint/91415/
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spelling my.uitm.ir.914152024-08-14T03:27:04Z https://ir.uitm.edu.my/id/eprint/91415/ A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin 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 NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/91415/1/91415.pdf A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin. (2019) Masters thesis, thesis, Universiti Teknologi MARA (UiTM). <http://terminalib.uitm.edu.my/91415.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
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
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
publishDate 2019
url https://ir.uitm.edu.my/id/eprint/91415/1/91415.pdf
https://ir.uitm.edu.my/id/eprint/91415/
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score 13.211869

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