A method for computer-implemented prediction of power production of one or more wind turbines in a wind farm, the prediction of power production being used for generating control commands provided to at least one of the wind turbines of the wind farm, wherein at each time point of one or more time points during the operation of the wind farm the following steps are performed: i) obtaining first weather forecast data for a given first time period (WIN_H), the first weather forecast data being historical weather forecast data for the area of the wind farm being generated in the past by a weather forecast provider, the first weather forecast data comprising at least one weather information, selected from a wind speed from different heights, a wind direction, a temperature, and an air density; ii) obtaining first power production data of the wind farm for the given first time period (WIN_H), the first power production data being historical power production data of the wind warm resulting from then actual environmental conditions in the area of the wind farm, where the first power production data comprise at least one power output information, comprising electric generated power; iii) obtaining second weather forecast data for a given second time period (WIN_P), the second weather forecast data being future weather forecast data for the area of the wind farm being generated in the past by the weather forecast provider, the second weather forecast data comprise at least one weather information, selected from a wind speed from different heights, a wind direction, a temperature, and an air density; iv) determining second power production data of the wind farm for the second time period (WIN_P) by processing the first weather forecast data, the first power production data and the second weather forecast data by a trained data driven model, where the first weather forecast data, the first power production data and the second weather forecast data are fed as a digital input to the trained data driven model and the trained data driven model provides the second power production data as a digital output, the second power production data being a prediction of power production of the wind farm for the second time period (WIN_P) and comprising at least one power output information comprising electric generated power, characterized in that the first and second power production data comprise an operation parameter of the one or more wind turbines, and the trained data driven model is a recurrent neural network. i) obtaining first weather forecast data for a given first time period (WIN_H), the first weather forecast data being historical weather forecast data for the area of the wind farm being generated in the past by a weather forecast provider, the first weather forecast data comprising at least one weather information, selected from a wind speed from different heights, a wind direction, a temperature, and an air density; ii) obtaining first power production data of the wind farm for the given first time period (WIN_H), the first power production data being historical power production data of the wind warm resulting from then actual environmental conditions in the area of the wind farm, where the first power production data comprise at least one power output information, comprising electric generated power; iii) obtaining second weather forecast data for a given second time period (WIN_P), the second weather forecast data being future weather forecast data for the area of the wind farm being generated in the past by the weather forecast provider, the second weather forecast data comprise at least one weather information, selected from a wind speed from different heights, a wind direction, a temperature, and an air density; iv) determining second power production data of the wind farm for the second time period (WIN_P) by processing the first weather forecast data, the first power production data and the second weather forecast data by a trained data driven model, where the first weather forecast data, the first power production data and the second weather forecast data are fed as a digital input to the trained data driven model and the trained data driven model provides the second power production data as a digital output, the second power production data being a prediction of power production of the wind farm for the second time period (WIN_P) and comprising at least one power output information comprising electric generated power,