Classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance

This paper presents a classification of solar tilt irradiance using the k-means clustering method, and an evaluation of the impact of different solar variabilities on monocrystalline and thin-film photovoltaic (PV) systems. The variability index and clearness index were implemented to quantify five...

Full description

Saved in:
Bibliographic Details
Main Authors: Mahmoud Farghaly, Sara Ragab, Gan, Chin Kim
Format: Article
Language:English
Published: Gazi Universitesi 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26235/2/2022%20-%20CLASSIFICATION%20OF%20SOLAR%20VARIABILITY%20-%20IJRER.PDF
http://eprints.utem.edu.my/id/eprint/26235/
http://www.ijrer.org/ijrer/index.php/ijrer/article/view/12929/pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utem.eprints.26235
record_format eprints
spelling my.utem.eprints.262352023-03-02T16:22:13Z http://eprints.utem.edu.my/id/eprint/26235/ Classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance Mahmoud Farghaly, Sara Ragab Gan, Chin Kim This paper presents a classification of solar tilt irradiance using the k-means clustering method, and an evaluation of the impact of different solar variabilities on monocrystalline and thin-film photovoltaic (PV) systems. The variability index and clearness index were implemented to quantify five years of solar datasets to assist in clustering solar variabilities. The elbow method was used to validate the k-clustering for solar variabilities. Due to the compact solar datasets, the Silhouette Coefficient and Gap Statistic were utilized to validate the k-cluster numbers. The PV performance was evaluated using the generated power, energy, and performance ratio for solar datasets from 2015 and 2019. Equal number of samples was taken from each PV system to analyse the average calculated values. The results showed that the elbow method was inaccurate for clustering solar variabilities, although it showed a weak elbow at K2 that was inaccurate for grouping solar variabilities. However, the k-means validation methods detected K3, K4, and K5 as the best k-cluster numbers. Among them, K4 was compatible for separating four types of solar variabilities, namely, overcast, moderate, mixed (clear/mild), and high variability. Based on the average performance values of the monocrystalline and thin-film PV systems for 2015 compared to 2019, similar degradation values were detected, especially for the performance ratio (0.77) under overcast. The thin-film showed degraded generated power and energy under the moderate type. The degraded generated power and performance ratio for the monocrystalline were due to the high passing clouds under the mixed and high variability types. Gazi Universitesi 2022-06 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26235/2/2022%20-%20CLASSIFICATION%20OF%20SOLAR%20VARIABILITY%20-%20IJRER.PDF Mahmoud Farghaly, Sara Ragab and Gan, Chin Kim (2022) Classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance. International Journal of Renewable Energy Research, 12 (2). pp. 692-702. ISSN 1309-0127 http://www.ijrer.org/ijrer/index.php/ijrer/article/view/12929/pdf 10.20508/ijrer.v12i2.12929.g8457
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description This paper presents a classification of solar tilt irradiance using the k-means clustering method, and an evaluation of the impact of different solar variabilities on monocrystalline and thin-film photovoltaic (PV) systems. The variability index and clearness index were implemented to quantify five years of solar datasets to assist in clustering solar variabilities. The elbow method was used to validate the k-clustering for solar variabilities. Due to the compact solar datasets, the Silhouette Coefficient and Gap Statistic were utilized to validate the k-cluster numbers. The PV performance was evaluated using the generated power, energy, and performance ratio for solar datasets from 2015 and 2019. Equal number of samples was taken from each PV system to analyse the average calculated values. The results showed that the elbow method was inaccurate for clustering solar variabilities, although it showed a weak elbow at K2 that was inaccurate for grouping solar variabilities. However, the k-means validation methods detected K3, K4, and K5 as the best k-cluster numbers. Among them, K4 was compatible for separating four types of solar variabilities, namely, overcast, moderate, mixed (clear/mild), and high variability. Based on the average performance values of the monocrystalline and thin-film PV systems for 2015 compared to 2019, similar degradation values were detected, especially for the performance ratio (0.77) under overcast. The thin-film showed degraded generated power and energy under the moderate type. The degraded generated power and performance ratio for the monocrystalline were due to the high passing clouds under the mixed and high variability types.
format Article
author Mahmoud Farghaly, Sara Ragab
Gan, Chin Kim
spellingShingle Mahmoud Farghaly, Sara Ragab
Gan, Chin Kim
Classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance
author_facet Mahmoud Farghaly, Sara Ragab
Gan, Chin Kim
author_sort Mahmoud Farghaly, Sara Ragab
title Classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance
title_short Classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance
title_full Classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance
title_fullStr Classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance
title_full_unstemmed Classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance
title_sort classification of solar variability using k-means method for the evaluation of solar photovoltaic systems performance
publisher Gazi Universitesi
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26235/2/2022%20-%20CLASSIFICATION%20OF%20SOLAR%20VARIABILITY%20-%20IJRER.PDF
http://eprints.utem.edu.my/id/eprint/26235/
http://www.ijrer.org/ijrer/index.php/ijrer/article/view/12929/pdf
_version_ 1759693058604007424
score 13.159267