Paper
13 May 2024 Research on medium and short term prediction of regional photovoltaic power generation based on fuzzy support vector machine
Peng Wang, Sheyu Zhou, Jiaxi Chen, Kai Yang, Qing Shi, Meng Tao
Author Affiliations +
Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 1315911 (2024) https://doi.org/10.1117/12.3024669
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China
Abstract
In the power market, accurate prediction of regional photovoltaic power generation has an important impact on power trading and operational decision-making. It can make reasonable arrangements for energy dispatch, energy storage management, power grid planning, and other aspects, improving the efficiency and reliability of energy systems. To this end, a short-term and medium-term prediction method for regional photovoltaic power generation based on fuzzy support vector machine is proposed. Detect abnormal data, replace abnormal data, and normalize the collected regional photovoltaic power generation data. Based on fuzzy support vector machine, calculate the short-term membership function of the pre processed the data. On this basis, the regression function of the optimal classification function of the fuzzy support vector machine model is established to achieve short-term and medium-term prediction. The experimental results show that the proposed method has high accuracy and can effectively shorten the time for predicting regional photovoltaic power generation in the medium and short term.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Peng Wang, Sheyu Zhou, Jiaxi Chen, Kai Yang, Qing Shi, and Meng Tao "Research on medium and short term prediction of regional photovoltaic power generation based on fuzzy support vector machine", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 1315911 (13 May 2024); https://doi.org/10.1117/12.3024669
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KEYWORDS
Photovoltaics

Support vector machines

Fuzzy logic

Solar energy

Data modeling

Education and training

Solar cells

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