期刊
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
卷 45, 期 43, 页码 23791-23808出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2020.06.209
关键词
Probabilistic prediction; Renewable energy; Improved kernel density estimation; Uncertainty; Wind Farm
资金
- Natural Science Foundation of Fujian Province, China [2017J01782]
- Science and Technology Guiding Project of Xiamen city [3502Z20179020]
- Science and Technology Planning Project of Longyan city [2017LY90]
- High-level Talent Programs of Xiamen University of technology [YKJ18003R]
- National Nature Science Foundation [61703356]
The evolution of renewable energy especially wind energy over the past decade has sur-passed all expectations. Short-term probabilistic wind power forecasting is a good option to increase the reliability of power system. But short-term forecasting of power generated by wind turbine has high error due to uncertainty parameter such as wind speed so, it is very important to find a way to increase the accuracy of forecasting. Therefore, in this paper, a novel forecasting method that has high reliability compared to other methods is presented. In this paper, in order to benefit the superiority of various prediction models, a new Improved Kernel Density Estimation (IKDE) method is exploited to estimate the wind en-ergy possibility. The combination of various prediction models and the suggested method might develop the function of probabilistic prediction by supplying divergent types of compactness performance. KDE method is a powerful method to analyze background and foreground characteristic. In order to increase the efficiency of KDE method some of pre-dicting model are combined and a detection algorithm based on an Improved Kernel Density Estimation (IKDE) model is defined. The appropriate bandwidths, comparative threshold, comparative background sample learning array, and an enhanced sample updating model for sample learning array are proposed as the basics of the IKDE model. Two levels of optimization are used to simplify the IKDE model parameters. Finally, in order to prove the superiority of the proposed method over other methods, this method and 4 other methods have been implemented on 10 wind farms. The simulation results show that the prediction accuracy of the proposed method is about 3.8% higher than other methods due to the improved structure. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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