An Analysis Of Time-Of-Use Period for Residential Customer In Peninsular Malaysia Using K-Means Clustering Technique

FYP 2 SEM 2 2019/2020

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Main Author: Muhammad Syazwan Bin Zawawi
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Language:English
Published: 2023
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spelling my.uniten.dspace-216432023-05-05T08:04:17Z An Analysis Of Time-Of-Use Period for Residential Customer In Peninsular Malaysia Using K-Means Clustering Technique Muhammad Syazwan Bin Zawawi Time-Of-Use Period FYP 2 SEM 2 2019/2020 Time-of-Use (TOU) is one of the programs or the initiatives from Demand Response Program. It is the program to encourage the customers either industrial, commercial or residential to control the electricity usage throughout their daily routine. One of it’s advantage is the customers should be able to reduce the electricity monthly bill by reducing the energy usage during Peak Period. This is due to the rate of electricity during Peak Period is expensive compared to Off-Peak Period. In this research, an analysis of TOU Period were done. The analysis were done only for residential customers in Peninsular Malaysia. Industrial and Commercial customers in Peninsular Malaysia were already implemented this TOU scheme. Main objective in this research is to segregate the TOU Period into two parts which are Peak Period and Off-Peak Period using K-Means Clustering technique. Other than that, Jenks Natural Breaks were also used to compare the segregation results between these two clustering technique. Both method were implemented in MATLAB. In this research, six (6) actual residential customers load profile were used to segregate the TOU Period. Other than that, 100 and 1000 randomly generated residential customers load profile also were used in determining the impact of Time-Of-Use scheme towards residential customers. There are total of eight (8) case studies that were discussed regarding the performance of TOU compared to conventional Inclining Block Tariff (IBT) that already implemented in Peninsular Malaysia. Among all case studies, four (4) of them are using K-Means Clustering in determining TOU Period and the other four (4) are using Jenks Natural Breaks. For K-Means Clustering, Peak Period start from 7.30am until 7.00pm for weekdays while for weekends start from 3.30am until 8.00pm. For Jenks Natural Breaks, Peak Period for weekdays start from 7.00am until 6.00pm and for weekends start from 4.00am until 7.30pm. From the case studies, it shows that the TOU Period segregation using Jenks Natural Breaks could provide more benefits towards residential customers compared to K-Means Clustering technique. 2023-05-03T17:31:36Z 2023-05-03T17:31:36Z 2020-02 https://irepository.uniten.edu.my/handle/123456789/21643 en application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
topic Time-Of-Use Period
spellingShingle Time-Of-Use Period
Muhammad Syazwan Bin Zawawi
An Analysis Of Time-Of-Use Period for Residential Customer In Peninsular Malaysia Using K-Means Clustering Technique
description FYP 2 SEM 2 2019/2020
format
author Muhammad Syazwan Bin Zawawi
author_facet Muhammad Syazwan Bin Zawawi
author_sort Muhammad Syazwan Bin Zawawi
title An Analysis Of Time-Of-Use Period for Residential Customer In Peninsular Malaysia Using K-Means Clustering Technique
title_short An Analysis Of Time-Of-Use Period for Residential Customer In Peninsular Malaysia Using K-Means Clustering Technique
title_full An Analysis Of Time-Of-Use Period for Residential Customer In Peninsular Malaysia Using K-Means Clustering Technique
title_fullStr An Analysis Of Time-Of-Use Period for Residential Customer In Peninsular Malaysia Using K-Means Clustering Technique
title_full_unstemmed An Analysis Of Time-Of-Use Period for Residential Customer In Peninsular Malaysia Using K-Means Clustering Technique
title_sort analysis of time-of-use period for residential customer in peninsular malaysia using k-means clustering technique
publishDate 2023
_version_ 1806427832949145600
score 13.214268