Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm

Kishore, D. J. Krishna and Mohamed, M. R. and Sudhakar, K. and Jewaliddin, S. K. and Peddakapu, K. and Srinivasarao, P. (2021) Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm. In: 1st IEEE International Conference on Emerging Trends in Industry 4.0, ETI 4.0 2021 , 19 - 21 May 2021 , Raigarh, India. pp. 1-5. (175124). ISBN 978-166542237-6

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DOI/Official URL: https://doi.org/ 10.1109/ETI4.051663.2021.9619323

Abstract

Photo-voltaic (PV) is one of the most abundant sources on the earth for the generation of electricity. Although, due to the stochastic nature of PV characteristics to sustain constant power, an accurate PV power prediction is needed for a grid-connected PV system. The proposed model of support vector machine (SVM) with improved dragonfly algorithm(IDA) is used to forecast the PV power. Previously, Theexecution can be done by dragonfly algorithm (DA) through adaptive learning factor along with the differential evolution technique. The IDA is used to select the best support vector machine parameters. Eventually, the suggested model provides better performance as compared to the other algorithm such as SVM with dragonfly algorithm(SVM-DA). It is suitable for forecasting ultra-short-term PV power.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Dragonfly Algorithm (DA); Improved Dragonfly Algorithm (IDA); PV power prediction; Support Vector Machine (SVM)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Institute of Postgraduate Studies College of Engineering Faculty of Electrical and Electronic Engineering Technology Faculty of Mechanical and Automotive Engineering Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	14 Mar 2023 05:39
Last Modified:	14 Mar 2023 05:39
URI:	http://umpir.ump.edu.my/id/eprint/37280
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