A comprehensive review on the development of data-driven methods for wind power prediction and AGC performance evaluation in wind–thermal bundled power systems

Wang, Shuai; Li, Bin; Li, Guanzheng; Li, Botong; Li, Hongbo; Jiao, Kui; Wang, Chengshan

doi:10.1016/j.egyai.2024.100336

Cited by 6 publications

(1 citation statement)

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“…Practically, the dataset is divided into ten parts, and trials are performed by rotating nine of them as training data and one as validation data. Each trial yields the corresponding model performance evaluation parameters [35]. The average of the evaluation parameters including bias, mean absolute error (MAE), root mean square error (RMSE), correlation (R), standard deviation (SD, for observations and predictions, respectively), and index of agreement (IA) [36] from the 10 trails was used as an estimate of the algorithm's accuracy.…”

Section: Model Development and Forecasting Stepsmentioning

confidence: 99%

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Fang,

Cheung,

Yang

2024

Remote Sensing

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In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations during 1960–2018, typhoon information (position and intensity), station information (position and altitude), and properties of the western Pacific subtropical high. Model evaluation shows that besides the distance between a station and cyclone, the subtropical high properties are ranked very high in the model’s feature importance, especially the subtropical ridgeline, and intensity. These aspects of the subtropical high influence the location and timing of typhoon landfall. The forecast model has a correlation coefficient of about 0.73, an Index of Agreement of nearly 0.8, and a mean bias of 1.28 mm based on the training dataset. Biases are consistently low, with both positive and negative signs, for target stations in the outer rainband (up to 1000 km, beyond which the model does not forecast) of typhoons. The range of biases is much larger for target stations in the inner-core (0–200 km) region. In this region, the model mostly overestimates (underestimates) the small (large) rain rates. Cases study of Typhoon Doksuri and Talim in 2023, as independent cases, shows the high performance of the model in forecasting the peak rain rates and timing of their occurrence of the two impactful typhoons.

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Section: Model Development and Forecasting Stepsmentioning

confidence: 99%