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The unpredictability and instability of wind have hindered the development and utilization of wind power. To harness wind energy and ensure a secure and stable power grid after wind power integration, precise predictions of wind power generation are imperative. Here, we apply one-year data from a coastal wind farm in Zhejiang to train a Random Forest (RF) model for predicting wind power generation. The results indicate that the RF model (mean bias (MB) 1.33) outperforms traditional linear models (MB 87.70). An evaluation of prediction-measurement bias shows a strong agreement between the RF model's predictions and actual measurements (MB 1.33), especially when wind speeds exceed 5.7 m/s. While the Weather Research and Forecasting (WRF) model yields less accurate results (R2 0.65) compared to using measured wind velocity data (R2 0.97) for wind power prediction, it remains acceptable as it can capture and forecast peak generating power, meeting daily management requirements. Therefore, our machine learning-based approach offers practical guidance for reducing wind power generation uncertainties. Enhancing wind velocity forecasting through the WRF model could further improve the RF model's accuracy in predicting wind power generation. Our study also verified the future application of RF model in coastal areas of China. [ABSTRACT FROM AUTHOR]

A method was developed for rapid quantitative determination of cellulose in tobacco by utilizing 13 C cross polarization magic angle spinning NMR spectroscopy ( 13 C CP/MAS NMR). Sample powder was loaded into NMR rotor, which was customized rotor containing a matched silicon tube as an intensity reference. 13 C CP/MAS NMR spectra of tobacco samples were processed with spectral deconvolution to obtain the area of the C-1 resonance at 105.5 ppm and the internal standard at 0 ppm. The ratio between the area of 105.5 ppm and 0 ppm of a set of standard cellulose samples was used to construct a calibration curve. The cellulose content of a tobacco sample was determined by comparison of the ratio between the area of 105.5 ppm and 0 ppm to the calibration curve. Results of this developed method showed good agreement with those obtained from chemical analysis. The proposed method has such advantages of accuracy, quickness and efficiency, and could be an alternative to chemical analyses of cellulose. [ABSTRACT FROM AUTHOR]