3results about How to "Improve forecasting performance" patented technology

The application discloses a power meteorological data fusion method and system based on multi-source meteorological prediction and a storage medium, and combines power meteorological monitoring and multi-source numerical weather prediction data. Through error analysis on the multi-source numerical weather prediction data, the numerical weather prediction is changed from single deterministic prediction to multi-source fusion prediction, which is beneficial to the real application of meteorological information to actual business work of the power grid. The application can provide precision evaluation of different prediction data sources, provide multi-source prediction fusion data, improve prediction precision, and provide technical support for power meteorological fine prediction.

The present application relates to the field of meteorological monitoring, and discloses a method for constructing an ozone pollution meteorological condition index, comprising: obtaining historical ozone and meteorological data of multiple cities, and dividing the data into a modeling set and a verification set after preprocessing; constructing a random forest regression model with ozone as a label, optimizing parameters through grid search cross-validation, and calculating weight coefficients of each meteorological factor through SHAP analysis; comparing multi-strategy binning through four strategies of equal width, equal frequency, clustering and decision tree optimal binning, introducing a physical trend consistency penalty term into the objective function, and optimizing the optimal binning interval that meets the physical monotonicity law; calculating the interval division index, and obtaining the ozone pollution meteorological condition index OPMI through weighted summation; and S5, dividing the potential level and verifying the reliability. The present application solves the problems of poor pertinence, subjective weight and non-fine interval division of the existing index, and improves the precision and business applicability of ozone pollution meteorological potential evaluation.

The application provides an ECMWF mode element bias correction method based on AttUnet, and belongs to the meteorological prediction field. The method uses a deep convolutional neural network in 4 elements of ECMWF mode output surface, 2m temperature, surface pressure, 2m specific humidity and 10m wind, and performs bias correction on the 0.125 degree resolution ECMWF mode output result. The method first constructs a feature library according to the ECMWF mode output elements, and then uses the XgBoost tool to analyze the sample library and sort the importance, and further screens the factors combined with artificial experience and considers the terrain information. Then, the deep convolutional neural network is used for prediction and bias correction to obtain more accurate 0.125 degree grid products. At the same time, in the model debugging stage, by optimizing the learning rate and the loss function, the model has good prediction performance for extreme disastrous weather.