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Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization.

As the rising of electricity demand, electricity load profile characterization (ELPC) is the integral aspect in planning, operating system, and distribution network development. The approach in the existing ELPC is still relatively macro in nature and does not involve other aspects outside the elect...

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Main Authors: Senen, Adri, Putri, Tri Wahyu Oktaviana, Jamian, Jasrul Jamani, Supriyanto, Eko, Anggaini, Dwi
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/104976/1/JasrulJamaniJamian2023_FuzzyCMeansClusteringBasedonMicroSpatial.pdf
http://eprints.utm.my/104976/
http://dx.doi.org/10.11591/ijeecs.v30.i1.pp33-45
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spelling my.utm.1049762024-04-01T06:37:39Z http://eprints.utm.my/104976/ Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization. Senen, Adri Putri, Tri Wahyu Oktaviana Jamian, Jasrul Jamani Supriyanto, Eko Anggaini, Dwi TK Electrical engineering. Electronics Nuclear engineering As the rising of electricity demand, electricity load profile characterization (ELPC) is the integral aspect in planning, operating system, and distribution network development. The approach in the existing ELPC is still relatively macro in nature and does not involve other aspects outside the electricity variable, so the results tend to be biased for areas experiencing rapid land use changes. Therefore, this paper proposes an ELPC approach based on micro-spatial. Microspatial analysis is done by dividing area in the form of the smallest grids involving various electrical, demographic, geographic and socio-economic variables, which are then grouped using adaptive clustering based on fuzzy C-means (FCM). The adaptive clustering algorithm is proven to be able to determine the degree of membership of each grid data against each cluster with the ability to determine the number of clusters automatically according to the attribute data provided. The ELPC results which consist of 5 clusters are then analyzed using descriptive statistic, plotted, and mapped to obtain more accurate and realistic load characteristics in accordance with the pattern and geographical conditions of the region, so that the results can be used as a reference in load forecasting, network development, and distributed generation (DG) integration. Institute of Advanced Engineering and Science 2023-04 Article PeerReviewed application/pdf en http://eprints.utm.my/104976/1/JasrulJamaniJamian2023_FuzzyCMeansClusteringBasedonMicroSpatial.pdf Senen, Adri and Putri, Tri Wahyu Oktaviana and Jamian, Jasrul Jamani and Supriyanto, Eko and Anggaini, Dwi (2023) Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization. Indonesian Journal of Electrical Engineering and Computer Science, 30 (1). pp. 33-45. ISSN 2502-4752 http://dx.doi.org/10.11591/ijeecs.v30.i1.pp33-45 DOI: 10.11591/ijeecs.v30.i1.pp33-45
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Senen, Adri
Putri, Tri Wahyu Oktaviana
Jamian, Jasrul Jamani
Supriyanto, Eko
Anggaini, Dwi
Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization.
description As the rising of electricity demand, electricity load profile characterization (ELPC) is the integral aspect in planning, operating system, and distribution network development. The approach in the existing ELPC is still relatively macro in nature and does not involve other aspects outside the electricity variable, so the results tend to be biased for areas experiencing rapid land use changes. Therefore, this paper proposes an ELPC approach based on micro-spatial. Microspatial analysis is done by dividing area in the form of the smallest grids involving various electrical, demographic, geographic and socio-economic variables, which are then grouped using adaptive clustering based on fuzzy C-means (FCM). The adaptive clustering algorithm is proven to be able to determine the degree of membership of each grid data against each cluster with the ability to determine the number of clusters automatically according to the attribute data provided. The ELPC results which consist of 5 clusters are then analyzed using descriptive statistic, plotted, and mapped to obtain more accurate and realistic load characteristics in accordance with the pattern and geographical conditions of the region, so that the results can be used as a reference in load forecasting, network development, and distributed generation (DG) integration.
format Article
author Senen, Adri
Putri, Tri Wahyu Oktaviana
Jamian, Jasrul Jamani
Supriyanto, Eko
Anggaini, Dwi
author_facet Senen, Adri
Putri, Tri Wahyu Oktaviana
Jamian, Jasrul Jamani
Supriyanto, Eko
Anggaini, Dwi
author_sort Senen, Adri
title Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization.
title_short Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization.
title_full Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization.
title_fullStr Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization.
title_full_unstemmed Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization.
title_sort fuzzy c-means clustering based on micro-spatial analysis for electricity load profile characterization.
publisher Institute of Advanced Engineering and Science
publishDate 2023
url http://eprints.utm.my/104976/1/JasrulJamaniJamian2023_FuzzyCMeansClusteringBasedonMicroSpatial.pdf
http://eprints.utm.my/104976/
http://dx.doi.org/10.11591/ijeecs.v30.i1.pp33-45
_version_ 1797905679047983104
score 13.211869

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