{"id":6142,"date":"2026-05-10T06:19:50","date_gmt":"2026-05-09T22:19:50","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6142"},"modified":"2026-07-02T22:05:34","modified_gmt":"2026-07-02T14:05:34","slug":"short-term-photovoltaic-power-prediction-based-on-fvs-kelm-method","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=short-term-photovoltaic-power-prediction-based-on-fvs-kelm-method","title":{"rendered":"Short-term photovoltaic power prediction based on FVS-KELM method"},"content":{"rendered":"\n
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<\/i> 2020<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 23, Issue 2<\/a><\/p>\n<\/div>\n\n\n\n

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Jun Li1,2,3<\/sup><\/i><\/a> and Meng Li1<\/sup><\/p>\n\n\n\n

1<\/sup>School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, P.R. China <\/p>\n\n\n\n

2<\/sup>Gansu Provincial Engineering Technology Center for Informatization of Logistics Transport Equipment, Lanzhou 730070, P.R.China <\/p>\n\n\n\n

3<\/sup>Gansu Provincial Industry Technology Center of Logistics Transport Equipment. Lanzhou 730070, P.R. China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0August 29, 2019
Accepted:\u00a0February 17, 2020
Publication Date:\u00a0May 10, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Architecture of FVS-KELM method<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202006_23(2).0013<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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For the highly nonlinear short-term photovoltaic power problems affected by a variety of factors, a novel modeling method FVS-KELM based on feature vectors selection (FVS) algorithm and kernel extreme learning machine (KELM) is proposed. Firstly, FVS algorithm maps the input data to the high-dimensional feature space through kernel trick, considering the geometric features, selecting the relevant data subset to form the base vector of the subspace. Secondly, the input data is projected onto the feature subspace, and the final prediction model is established by the KELM method, which does not need set the number of the hidden layer nodes, and uses the kernel function representing the unknown nonlinear feature mapping of the hidden layer, so it has good generalization ability. In order to verify the effectiveness of the FVS-KELM modeling method, it is applied to the benchmark photovoltaic power prediction example provided by Global Energy Forecasting Competition 2014 (GEFCOM2014). Under the same condition, the FVS-KELM method is compared with extreme learning machine (ELM), support vector machine (SVM) method etc. The experimental results show that the FVS-KELM method can not only reduce the computational complexity, but also has good prediction accuracy and robustness, and the model has good generalization.<\/p>\n\n\n\n

Keywords:\u00a0Photovoltaic Power; Prediction; Feature Vectors Selection; Extreme Learning Machine; Kernel Method<\/em><\/p>\n\n\n\n

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