Renewable energy sources are taking on an increasingly important role in the power system, which raises the need for reliable forecasting of their production. This thesis focuses on the development of a model for forecasting electricity generation from a wind farm. Reliable forecasts are crucial for effective power plant operation planning, optimization of production capacities, and balancing the power system. This work presents the development of a model capable of forecasting future wind power generation based on historical electricity production data and weather conditions. Special attention is given to the preparation and processing of the dataset, as the quality of input data significantly affects the performance of the forecasting model. The training dataset was based on publicly available archives of weather data and the measured electrical power output delivered by the wind farm to the grid. The models were developed in the Python programming language, using the open-source TensorFlow library, which enables the construction and training of complex neural networks.Based on the findings of the thesis, it is evident that, due to the complexity of weather patterns, the most suitable forecasting model is based on a neural network.
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