Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction

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

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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 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).

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Technology Management and Business > Department of Construction Management
Depositing User: Mr. Mohamad Zulkhibri Rahmad
Date Deposited: 16 May 2023 02:24
Last Modified: 16 May 2023 02:24
URI: http://eprints.uthm.edu.my/id/eprint/8699

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