E3S Web Conf.
Volume 185, 20202020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|Number of page(s)||10|
|Section||Energy Engineering and Power System|
|Published online||01 September 2020|
Ultra-short-time prediction technology of wind power station output based on variational mode decomposition and particle swarm optimization least squares vector machine
1 College of Electronic and Information Engineering, Tongji University, Shanghai 201800, China
2 Electric Power Research Institute , State Grid Gansu Electric Power Company, Lanzhou, Gansu 730000, China
* Corresponding author: email@example.com
Wind power is developing rapidly in the context of sustainable development, and a series of problems such as wind curtailment and power curtailment have gradually emerged. The forecast of power generation output has become one of the hotspots of current research. This paper proposes a wind power plant output ultra-short-time prediction technology based on variational modal decomposition and particle swarm optimization least squares vector machine. Variational Modal Decomposition (VMD) method decomposes the historical output data of wind power plants at multiple levels. At the same time, it explores the impact of various decomposition methods such as EMD decomposition on the prediction accuracy, and uses the least squares support vector machine based on particle swarm optimization algorithm. Predictive summation is performed on each level of data separately to obtain a more accurate prediction effect, which has a certain improvement in prediction accuracy compared with traditional prediction algorithms.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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