One solution ( sol1) was based on achieving varying training data sets. In their work, Liu, Chen, Lv, Wu and Liu presented different ways of creating an ensemble. The concept of ensemble is to achieve data variability to compensate for disadvantages of component models, such as bias, and obtain a solution that is more robust and less susceptible to the errors of NWP models. In recent years, ensemble models have become popular to tackle the deficiencies of single prediction models. The proposed ensemble methods are also applicable to short-time generation forecasting for other renewable energy sources (RES), e.g., hydropower or photovoltaic (PV) systems. A new, original “Additional Expert Correction” additionally reduces errors of energy generation forecasts for both wind farms. Predictions by the original ensemble forecasting method, called “Ensemble Averaging Without Extremes” have the lowest normalized mean absolute error (nMAE) among all tested methods. Original ensemble methods, developed for researching specific implementations, have reduced errors of forecast energy generation for both wind farms as compared to single methods. Ensemble, hybrid and single methods are used for predictions, including machine learning (ML) solutions like Gradient-Boosted Trees (GBT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), K-Nearest Neighbours Regression (KNNR) and Support Vector Regression (SVR). Also, we prove that it is better to use forecasts from two NWP models as input data. We demonstrate that it is better to use Numerical Weather Prediction (NWP) point forecasts for hourly lags: −3, 2, −1, 0, 1, 2, 3 (original contribution) as input data than lags 0, 1 that are typically used. In the first part of our study, we have analysed the available data to identify potentially useful explanatory variables for forecasting models with additional development of new input data based on the basic data set. Using two wind farms for statistical analyses and forecasting considerably increases credibility of newly created effective prediction methods and formulated conclusions. Even a small improvement in the quality of these forecasts translates into more security of the system and savings for the economy. They are also used in energy market transactions.

Forecasts for the next 24 h play an important role in this process. The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation.

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