2017/08/22 by Zhiwen Wang, Chen Shen, Wang, Zhiwen +3
Engineering · #Data Analysis #Electric Power System Optimization #Energy Load and Power Forecasting #FOS: Mathematics #FOS: Physical sciences #Power System Reliability and Maintenance #Probability (math.PR) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1708.06759
openalex publication_date 2017/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to different forecast values. Therefore, appropriate probabilistic models that can provide accurate information for conditional forecast error distributions are of great need. On the basis of Gaussian mixture model, this paper constructs analytical conditional distributions of forecast errors for multiple wind farms with respect to forecast values. The accuracy of the proposed probabilistic models is verified by using historical data. Thereafter, a fast sampling method is proposed to generate scenarios from the conditional distributions which are non-Gaussian and interdependent. The efficiency of the proposed sampling method is verified.