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【学术论文】IEEE Trans. Power Systems:Direct quantile regression for nonparametric probabilistic forecasting of wind power generation
作者:sgool    发布于:2016-12-09 12:01:53    文字:【】【】【

C. Wan, J. Lin, J. Wang, Y. Song, Z.Y. Dong

Direct quantile regression for nonparametric probabilistic forecasting of wind power generation”

IEEE Transactions on Power Systems, In press

AbstractThe fluctuation and uncertainty of wind power generation bring severe challenges to secure and economic operation of power systems. Because wind power forecasting error is unavoidable, probabilistic forecasting becomes critical to accurately quantifying the uncertainty involved in traditional point forecasts of wind power and to providing meaningful information to conduct risk management in power system operation. This paper proposes a novel direct quantile regression (DQR) approach to efficiently generate nonparametric probabilistic forecasting of wind power generation combining extreme learning machine (ELM) and quantile regression. Quantiles with different proportions can be directly produced via an innovatively formulated linear programming optimization model, without dependency on point forecasts. Multi-step probabilistic forecasting of 10-min wind power is newly carried out based on real wind farm data from Bornholm Island in Denmark. The superiority of the proposed approach is verified through comparisons with other well-established benchmarks. The proposed approach forms a new artificial neural network-based nonparametric forecasting framework for wind power with high efficiency, reliability, and flexibility, which can be beneficial to various decision-making activities in power systems.

KeywordsForecasting, Wind power generation, Probabilistic logic, Uncertainty, Wind forecasting, Predictive models, Reliability

ISSN0885-8950

DOI10.1109/TPWRS.2016.2625101

 

 

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