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A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction

Accurate photovoltaic (PV) power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency. This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural mod...

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Main Authors: Hui, Hwang Goh, Luo, Qinwen, Zhang, Dongdong, Dai, Wei, Chee, Shen Lim, Kurniawan, Tonni Agustiono, Kai, Chen Goh
Format: Article
Language:English
Published: 2021
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/7420/1/J14369_894efed23977592207c4d59d1d4b01f9.pdf
http://eprints.uthm.edu.my/7420/
https://doi.org/10.17775/CSEEJPES.2021.04560
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spelling my.uthm.eprints.74202022-07-21T07:21:04Z http://eprints.uthm.edu.my/7420/ A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction Hui, Hwang Goh Luo, Qinwen Zhang, Dongdong Dai, Wei Chee, Shen Lim Kurniawan, Tonni Agustiono Kai, Chen Goh T Technology (General) Accurate photovoltaic (PV) power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency. This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural model (DNM) in this paper. This model is trained using improved biogeography-based optimization (IBBO), a technique that incor�porates a domestication operation to increase the performance of classical biogeography-based optimization (BBO). To be more precise, a similar day selection (SDS) technique is presented for selecting the training set, and wavelet packet transform (WPT) is used to divide the input data into many components. IBBO is then used to train DNM weights and thresholds for each component prediction. Finally, each component’s prediction results are stacked and reassembled. The suggested hybrid model is used to forecast PV power under various weather conditions using data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs. The simulation results indicate that the proposed hybrid SDS and WPT-IBBO-DNM model has the lowest error of any of the benchmark models and hence has the potential to considerably enhance the accuracy of solar power forecasting (PVPF). 2021 Article PeerReviewed text en http://eprints.uthm.edu.my/7420/1/J14369_894efed23977592207c4d59d1d4b01f9.pdf Hui, Hwang Goh and Luo, Qinwen and Zhang, Dongdong and Dai, Wei and Chee, Shen Lim and Kurniawan, Tonni Agustiono and Kai, Chen Goh (2021) A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction. CSEE Journal of Power and Energy Systems. pp. 1-12. https://doi.org/10.17775/CSEEJPES.2021.04560
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Hui, Hwang Goh
Luo, Qinwen
Zhang, Dongdong
Dai, Wei
Chee, Shen Lim
Kurniawan, Tonni Agustiono
Kai, Chen Goh
A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction
description Accurate photovoltaic (PV) power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency. This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural model (DNM) in this paper. This model is trained using improved biogeography-based optimization (IBBO), a technique that incor�porates a domestication operation to increase the performance of classical biogeography-based optimization (BBO). To be more precise, a similar day selection (SDS) technique is presented for selecting the training set, and wavelet packet transform (WPT) is used to divide the input data into many components. IBBO is then used to train DNM weights and thresholds for each component prediction. Finally, each component’s prediction results are stacked and reassembled. The suggested hybrid model is used to forecast PV power under various weather conditions using data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs. The simulation results indicate that the proposed hybrid SDS and WPT-IBBO-DNM model has the lowest error of any of the benchmark models and hence has the potential to considerably enhance the accuracy of solar power forecasting (PVPF).
format Article
author Hui, Hwang Goh
Luo, Qinwen
Zhang, Dongdong
Dai, Wei
Chee, Shen Lim
Kurniawan, Tonni Agustiono
Kai, Chen Goh
author_facet Hui, Hwang Goh
Luo, Qinwen
Zhang, Dongdong
Dai, Wei
Chee, Shen Lim
Kurniawan, Tonni Agustiono
Kai, Chen Goh
author_sort Hui, Hwang Goh
title A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction
title_short A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction
title_full A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction
title_fullStr A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction
title_full_unstemmed A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction
title_sort hybrid sds and wpt-ibbo-dnm based model for ultra-short term photovoltaic prediction
publishDate 2021
url http://eprints.uthm.edu.my/7420/1/J14369_894efed23977592207c4d59d1d4b01f9.pdf
http://eprints.uthm.edu.my/7420/
https://doi.org/10.17775/CSEEJPES.2021.04560
_version_ 1739830456513200128
score 13.211869

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