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

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.

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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).

Item Type: Article
Uncontrolled Keywords: Photovoltaic power forecasting; similar day selection; wavelet packet transform; dendritic neural model; improved biogeography-based optimization
Subjects: T Technology > T Technology (General)
Depositing User: Mr. Abdul Rahim Mat Radzuan
Date Deposited: 21 Jul 2022 07:21
Last Modified: 21 Jul 2022 07:21
URI: http://eprints.uthm.edu.my/id/eprint/7420

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