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    Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting

    Ming Zhang, Hongbo Li, Xing Deng*

    Energy Engineering, Vol.119, No.1, pp. 237-252, 2022, DOI:10.32604/EE.2022.017916

    Abstract The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To verify the final performance of… More >

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