Pujić, Dea Janev, Valentina Jelić, Marko Stanković, Katarina application/pdf 1325099 bytes https://phaidrabg.bg.ac.rs/o:34084 info:eu-repo/semantics/other Естиматор производње електричне енергије ветрогенератора заснован на машинском учењу естиматор производње, обновљиви извори, ветрогенератор, неуралне мреже To decrease reliance on fossil fuels and address the pressing issue of climate change, renewable energy sources like solar panels and wind turbines have been implemented in recent years. These sources, however, come with their own set of challenges. The stochastic nature of renewable energy, which is the result of its high dependency on meteorological conditions, makes it difficult to plan for their usage and this in turn affects the stability of the electrical grid. The mismatch between energy production and demand can lead to power outages and other disruptions in the grid. As renewable energy becomes more prevalent in the energy market, accounting for a larger share of the overall energy mix, it is crucial to have accurate predictions for accessible energy in order to maintain a stable grid. In this regard, the need for accurate Renewable Energy Sources (RES) production forecaster is obvious, and it has been considered as a crucial aspect of any technical solution aimed at improving the integration of renewable energy into the grid. Amongst the various forms of renewable energy, wind energy has been considered as one of the most promising options due to its large potential and relatively low cost. Therefore, the forecast of wind turbine production has become a critical part of ensuring a stable grid. As a part of the research within this solutions, wind turbine production forecasting model has been developed based on the forecasted meteorological conditions. Moreover, it was integrated with the data storage platform, for both obtaining the relevant inputs and storing back the provided outputs. eng All rights reserved 2022