Abstract: A beamforming method includes obtaining feature data. The feature data includes at least one of feature data of a terminal device or feature data of a cell. The feature data of the terminal device represents azimuth information of the terminal device, and the feature data of the cell represents load information of the cell and distribution information of the terminal device. The beamforming method further includes obtaining a beamforming parameter based on the feature data and a machine learning model. The beamforming parameter includes at least one of a beam direction, a beam width, or a quantity of beams. The beamforming method further includes performing beamforming on a first signal based on the beamforming parameter.

Abstract: This application describes examples of a communication processing method, a communication apparatus, and a communication system. In one example, a communication apparatus obtains a plurality of candidate cell sets used by a terminal device for handover. Based on at least first quality of service and an energy consumption estimation value of each candidate cell set, the communication apparatus selects, from the plurality of candidate cell sets, a target cell set of which the first quality of service satisfies a target value and of which the energy consumption estimation value satisfies an energy consumption condition.

Abstract: Embodiments of this application provide a beamforming method and apparatus, a radio access network device, and a readable storage medium. In this method, feature data is first obtained, and the feature data includes at least one of feature data of a terminal device and feature data of a cell, where the feature data of the terminal device is used to represent azimuth information of the terminal device, and the feature data of the cell is used to represent load information of the cell and distribution information of the terminal device. After the feature data is obtained, a beamforming parameter is obtained based on the feature data and a machine learning model, where the beamforming parameter includes at least one of a beam direction, a beam width, and a quantity of beams. After the beamforming parameter is obtained, beamforming is performed on a to-be-sent signal based on the beamforming parameter.

Abstract: A correction method and a correction apparatus for a predicted wind speed of a wind farm is provided according to the present disclosure. The correction method includes: establishing wind speed deviation matrixes of a plurality of existing wind farms respectively; establishing a wind speed deviation correction model library based on the plurality of wind speed deviation matrixes of the existing wind farms; determining relevant parameters of the target wind farm, determining a matched wind speed deviation correction model in the wind speed deviation correction model library based on the relevant parameters, and correcting the predicted wind speed of the target wind farm based on the determined wind speed deviation correction model.

Abstract: A method and a device for calculating a power generation of a wind farm is provided. The method includes: determining whether a terrain complexity of a wind farm field exceeds a predetermined complexity; determining a representativeness of anemometer tower data in the wind farm field in a case where the terrain complexity exceeds the predetermined complexity; performing a mesoscale numerical simulation of a meteorological variable in the wind farm field in a case where the anemometer tower data is unrepresentative; extracting mesoscale numerical simulation data as virtual anemometer tower data; and calculating the power generation of the wind farm by using the virtual anemometer tower data.

Abstract: Provided are a method and an apparatus for icing prediction, and a method and an apparatus for generating an icing prediction model. The method for icing prediction includes: extracting a valid meteorological prediction data feature of a target wind turbine based on geographic information of the target wind turbine; inputting the valid meteorological prediction data feature into an icing prediction model for predict icing information; and outputting an icing prediction result by the icing prediction model in response to the input.

Abstract: A correction method and a correction apparatus for a predicted wind speed of a wind farm is provided according to the present disclosure. The correction method includes: establishing wind speed deviation matrixes of a plurality of existing wind farms respectively; establishing a wind speed deviation correction model library based on the plurality of wind speed deviation matrixes of the existing wind farms; determining relevant parameters of the target wind farm, determining a matched wind speed deviation correction model in the wind speed deviation correction model library based on the relevant parameters, and correcting the predicted wind speed of the target wind farm based on the determined wind speed deviation correction model.

Abstract: A method and a device for calculating a power generation of a wind farm is provided. The method includes: determining whether a terrain complexity of a wind farm field exceeds a predetermined complexity; determining a representativeness of anemometer tower data in the wind farm field in a case where the terrain complexity exceeds the predetermined complexity; performing a mesoscale numerical simulation of a meteorological variable in the wind farm field in a case where the anemometer tower data is unrepresentative; extracting mesoscale numerical simulation data as virtual anemometer tower data; and calculating the power generation of the wind farm by using the virtual anemometer tower data.