Wind power forecasting based on meteorological data using neural networks

The growing share of renewable energy sources in the structure of energy systems causes many problems related to the correct operation of the grid. This impact is most evident in low-voltage grids to which many low-power prosumer solar and wind installations are connected. For the correct management and, consequently, the economic operation of power systems, the most accurate forecast of electricity consumption and generation in grids with different voltage levels is needed. Conventional generation devices have stable production values and can be regulated within wide limits, while the production of electricity from renewable sources, by wind farms in particular, depends on external weather conditions and requires a more careful approach to its forecasting. The aim of the article is to present a method of forecasting the power generated by wind turbines based on publicly available meteorological data. The presented forecasting method uses the theory of neural networks.