Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection

Malaysia has introduced Feed-in Tariff (FiT) mechanism in 2011. This is accordance with Renewable Energy Act 2011 and Sustainable Energy Development Authority Act 2011. This mechanism is to promote the development and encouragement of renewable energy sector in Malaysia such as solar photovoltaic (P...

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Main Author:	Abu Bakar, Akmal Akram
Format:	Thesis
Language:	English
Published:	2016
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/77602/1/AkmalAkramAbuMFKE2016.pdf
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spelling	my-utm-ep.776022018-06-25T08:55:41Z Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection 2016-06 Abu Bakar, Akmal Akram TK Electrical engineering. Electronics Nuclear engineering Malaysia has introduced Feed-in Tariff (FiT) mechanism in 2011. This is accordance with Renewable Energy Act 2011 and Sustainable Energy Development Authority Act 2011. This mechanism is to promote the development and encouragement of renewable energy sector in Malaysia such as solar photovoltaic (PV), biomass, biogas, small hydro and geothermal. After 5 years of implementation in Malaysia, FiT mechanism has been know as an effective solution to make a monthly income from the energy produced from renewable sources. Hence, the residential area has started to install solar PV after the FiT was introduced. However, without taking any consideration the possibility of increament in electricity tariff, bank loan commitment, solar irradiation, increase in energy consumed and weather conditions, the existing FiT will not give an advantages to the customer. Therefore, the purpose of this project is to evaluate the FiT scheme for long term condition to residential area by using solar PV system as a renewable sources. This can be achieved by study the historical data of the electricity tariff, FiT rates, solar irradiation and energy consumption for residential house for 21 years. From the linear regression projection, it was projected that the electricity tariff will be increased around 140% from year 2015 to year 2035 for block tariff above 300kWh. In order to validate the data projection and analysis of the electricity tariff, an Artificial Neural Network (ANN) projection is also been used. The ANN analysis shown the FiT for PV system in residential area can give negative impact in long term condition. 2016-06 Thesis http://eprints.utm.my/id/eprint/77602/ http://eprints.utm.my/id/eprint/77602/1/AkmalAkramAbuMFKE2016.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:94127 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Abu Bakar, Akmal Akram Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection
description	Malaysia has introduced Feed-in Tariff (FiT) mechanism in 2011. This is accordance with Renewable Energy Act 2011 and Sustainable Energy Development Authority Act 2011. This mechanism is to promote the development and encouragement of renewable energy sector in Malaysia such as solar photovoltaic (PV), biomass, biogas, small hydro and geothermal. After 5 years of implementation in Malaysia, FiT mechanism has been know as an effective solution to make a monthly income from the energy produced from renewable sources. Hence, the residential area has started to install solar PV after the FiT was introduced. However, without taking any consideration the possibility of increament in electricity tariff, bank loan commitment, solar irradiation, increase in energy consumed and weather conditions, the existing FiT will not give an advantages to the customer. Therefore, the purpose of this project is to evaluate the FiT scheme for long term condition to residential area by using solar PV system as a renewable sources. This can be achieved by study the historical data of the electricity tariff, FiT rates, solar irradiation and energy consumption for residential house for 21 years. From the linear regression projection, it was projected that the electricity tariff will be increased around 140% from year 2015 to year 2035 for block tariff above 300kWh. In order to validate the data projection and analysis of the electricity tariff, an Artificial Neural Network (ANN) projection is also been used. The ANN analysis shown the FiT for PV system in residential area can give negative impact in long term condition.
format	Thesis
qualification_level	Master's degree
author	Abu Bakar, Akmal Akram
author_facet	Abu Bakar, Akmal Akram
author_sort	Abu Bakar, Akmal Akram
title	Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection
title_short	Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection
title_full	Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection
title_fullStr	Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection
title_full_unstemmed	Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection
title_sort	evaluation on feed-in tariff scheme for residential area based on artificial neural network projection
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2016
url	http://eprints.utm.my/id/eprint/77602/1/AkmalAkramAbuMFKE2016.pdf
_version_	1747817787376533504

Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection

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