Abstract
Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified.
Highlights
Wind power has remarkable uncertainties and randomness
In order to better evaluate the overall performance of the probabilistic prediction, this paper proposes the new comprehensive index (NCI), which can adaptively adjust the assessment keypoints according to different situations
The detailed performance indices calculation results show that the average coverage error (ACE) is quite close to 0, and the scores of the interval score and the NCI
Summary
Wind power has remarkable uncertainties and randomness. Traditional research projects about wind power prediction mainly focused on deterministic point prediction [1,2]. The algorithms of point prediction mainly include convolutional neural network [3], long short-term memory neural network [4], and gated recurrent neural network [5]. The main focus of these articles is to reduce the prediction errors by combining or improving some algorithms, but the errors of point prediction are unavoidable and the results cannot describe the uncertainties of wind power generation quantitatively. A new prediction method that can quantitatively reflect the uncertainties of wind power generation is needed to overcome the defects of traditional point prediction, and probabilistic prediction is an effective way to solve this problem
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
