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

Hui Hwang Goh, Hui Hwang Goh and Qinwen Luo, Qinwen Luo and Dongdong Zhang, Dongdong Zhang and Hui Liu, Hui Liu and Wei Dai, Wei Dai and Chee Shen Lim, Chee Shen Lim and Tonni Agustiono Kurniawan, Tonni Agustiono Kurniawan and Kai Chen Goh, Kai Chen Goh (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. 66-76.

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Abstract

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
Uncontrolled Keywords:	-
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Technology Management and Business > Department of Construction Management
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	21 Nov 2023 01:14
Last Modified:	21 Nov 2023 01:14
URI:	http://eprints.uthm.edu.my/id/eprint/10394

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