COMPARATIVEANALYSISOFRECURRENTAND TRANSFORMERMACHINE LEARNINGMODELS FORWIND TURBINEACTIVE POWERGENERATION FORECASTING

Wind turbines are inherently intermittent energy sources that predominantly
depend on meteorological conditions. Accurate prediction of wind turbine active power
generation is crucial for optimizing the operation of modern power systems rich in renewable
energy sources, improving system stability, enhancing the efficiency of production and
consumption balancing, optimizing the electricity market, and numerous other aspects. The
development and application of advanced artificial intelligence and machine learning (ML)
methods can further increase prediction accuracy, enabling the system to better adapt to the
variable nature of wind energy. This paper investigates the efficiency of two machine learning
approaches for predicting wind turbine active power, focusing on ML models based on
recurrent neural networks (LSTM and GRU) and Transformer models adapted for processing
and predicting time series data. Transformer models have recently gained prominence in time
series analysis due to their ability to effectively recognize long-term dependencies in data
through the self-attention mechanism, enabling parallel sequence processing and more
accurate identification of relevant patterns in the data. The choice of transformer model
architecture and parameter tuning is conditioned by two iterative processes. The validation of
the developed ML model is performed using a practical open-source dataset that contains a
large number of input features related to meteorological and operational system parameters.
The selection of the most relevant features is based on their correlation with the target
variable (in this case, active power), reducing the dimensionality of the problem and
improving model efficiency. The experimental evaluation includes an analysis of model
performance using standard metrics (RMSE and MAEI), while simultaneously examining the
transformer architecture and its parameter set. The obtained results provide insight into the
advantages and limitations of both approaches in various scenarios of short-term and long-
term prediction, with the aim of improving broader prediction strategies and optimizing wind
farm operations.