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: | , , , , , , , |
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| Format: | Article |
| Language: | en |
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IEEE
2023
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| Online Access: | http://eprints.uthm.edu.my/8699/1/J14369_830d1175165a60a814f4f04bf869a007.pdf http://eprints.uthm.edu.my/8699/ https://doi.org/10.17775/CSEEJPES.2021.04560 |
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| author | Goh, Hui Hwang Luo, Qinwen Zhang, Dongdong Liu, Hui Dai, Wei Lim, Chee Shen Kurniawan, Tonni Agustiono Goh, Kai Chen |
| author_facet | Goh, Hui Hwang Luo, Qinwen Zhang, Dongdong Liu, Hui Dai, Wei Lim, Chee Shen Kurniawan, Tonni Agustiono Goh, Kai Chen |
| author_sort | Goh, Hui Hwang |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 incorporates 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. Simulation results indicate
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 |
| id | my.uthm.eprints-8699 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2023 |
| publisher | IEEE |
| record_format | eprints |
| spelling | my.uthm.eprints-86992023-05-16T02:24:55Z http://eprints.uthm.edu.my/8699/ Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction Goh, Hui Hwang Luo, Qinwen Zhang, Dongdong Liu, Hui Dai, Wei Lim, Chee Shen Kurniawan, Tonni Agustiono Goh, Kai Chen TK Electrical engineering. Electronics Nuclear engineering 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 incorporates 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. Simulation results indicate 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). IEEE 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/8699/1/J14369_830d1175165a60a814f4f04bf869a007.pdf Goh, Hui Hwang and Luo, Qinwen and Zhang, Dongdong and Liu, Hui and Dai, Wei and Lim, Chee Shen and Kurniawan, Tonni Agustiono and Goh, Kai Chen (2023) Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction. CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 9 (1). pp. 1-11. ISSN 2096-0042 https://doi.org/10.17775/CSEEJPES.2021.04560 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Goh, Hui Hwang Luo, Qinwen Zhang, Dongdong Liu, Hui Dai, Wei Lim, Chee Shen Kurniawan, Tonni Agustiono Goh, Kai Chen Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction |
| title | Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction |
| title_full | Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction |
| title_fullStr | Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction |
| title_full_unstemmed | Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction |
| title_short | 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 | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.uthm.edu.my/8699/1/J14369_830d1175165a60a814f4f04bf869a007.pdf http://eprints.uthm.edu.my/8699/ https://doi.org/10.17775/CSEEJPES.2021.04560 |
| url_provider | http://eprints.uthm.edu.my/ |