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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7420/1/J14369_894efed23977592207c4d59d1d4b01f9.pdf |
| _version_ | 1825710376954626048 |
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| 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 |
| collection | UTHM |
| 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). |
| first_indexed | 2024-03-05T21:56:40Z |
| format | Article |
| id | uthm.eprints-7420 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:56:40Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | 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 |
| 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 |
| title | 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_short | 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 |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/7420/1/J14369_894efed23977592207c4d59d1d4b01f9.pdf |
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