E3S Web Conf.
Volume 256, 20212021 International Conference on Power System and Energy Internet (PoSEI2021)
|Number of page(s)||5|
|Section||Energy Internet R&D and Smart Energy Application|
|Published online||10 May 2021|
Short-term wind power prediction based on GPR-BSO model
1 College of Electrical and Information Engineering, Hunan University, Changsha, Hunan Province, 410082, China
2 Yiyang Power Supply Company, State Grid Hunan Electric Power Company, Yiyang, Hunan Province, 413000, China
3 College of Machinery and Electrical Engineering, Hunan City University, Yiyang, Hunan Province, 413049, China
* Corresponding author’s e-mail: email@example.com
Wind power forecasting is a crucial part for the safe and stable operation of wind power integration, which is under the influence of different factors such as wind speed, wind direction, atmospheric pressure. These factors bring randomness and volatility to wind power which makes it less predictable. While, there are very limited studies on describing the uncertainty of wind power. Therefore, to providing additional information on the uncertainty and volatility, a kernel-based on Gaussian Process Regression (GPR) incorporating the hyper-parameters intelligent optimization method is proposed in this paper. Firstly, the hyper-parameters solution of GPR is formulated as a nonlinear optimization with constraints. Then, an intelligent algorithm named Brain-storming optimization (BSO) is adopted to obtain the optimal hyper-parameters of GPR. Furthermore, the performance is examined on short-term wind power data. Most importantly, the GPR incorporating BSO can avoid the hyper-parameters at local optimum.
© The Authors, published by EDP Sciences, 2021
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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