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

• Main Authors: Kishore, D. J. Krishna, Mohamed, M. R., Sudhakar, K., Jewaliddin, S. K., Peddakapu, K., Srinivasarao, P.
• Format: Conference or Workshop Item
• Language: English; English
• Published: IEEE 2021
• Subjects: TA Engineering (General). Civil engineering (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/37280/1/Ultra-short-term%20PV%20power%20forecasting%20based%20on%20a%20support%20vector%20machine%20.pdf; http://umpir.ump.edu.my/id/eprint/37280/2/Ultra-short-term%20PV%20power%20forecasting.pdf; http://umpir.ump.edu.my/id/eprint/37280/; https://doi.org/ 10.1109/ETI4.051663.2021.9619323

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 vecto...