Short-Term Solar Power Forecasting Based on CEEMDAN and Kernel Extreme Learning Machine
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Published:2023-04-24
Issue:2
Volume:29
Page:28-34
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ISSN:2029-5731
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Container-title:Elektronika ir Elektrotechnika
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language:
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Short-container-title:ELEKTRON ELEKTROTECH
Author:
Gun Ali Riza,Dokur Emrah,Yuzgec Ugur,Kurban Mehmet
Abstract
The use of renewable energy sources contributes to environmental awareness and sustainable development policy. The inexhaustible and nonpolluting nature of solar energy has attracted worldwide attention. Accurate forecasting of solar power is vital for the reliability and stability of power systems. However, the effect of the intermittency nature of solar radiation makes the development of accurate prediction models challenging. This paper presents a hybrid model based on Kernel Extreme Learning Machine (Kernel-ELM) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for short-term solar power forecasting. The decomposition technique increases the number of stable, stationary, and regular patterns of the original signals. Each decomposed signal is fed into Kernel-ELM. To validate the performance of the hybrid model, solar power data from the BSEU Renewable Energy Laboratory, measured at 5-minute intervals, are used. To validate the proposed model, its performance is compared to some state-of-the-art forecasting models with seasonal data. The results highlight the good performance of the proposed hybrid model compared to other classical algorithms according to the metrics.
Publisher
Kaunas University of Technology (KTU)
Subject
Electrical and Electronic Engineering
Cited by
1 articles.
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