###### Abstract  
The accurate prediction of wind power generation, as well as the development of a digital twin of a wind turbine, require estimation of the power curve. Actual measurements of generated power, especially over short-term intervals, show that in many cases the power generated differs from the calculated power, which considers only the wind speed and the technical parameters of the wind turbine. Some of these measurements are erroneous, while others are influenced by additional factors affecting generation beyond wind speed alone. This study presents an investigation of the features influencing the accuracy of calculations of wind turbine power at short-term intervals. The open dataset of SCADA-system measurements from a real wind turbine is used. It is discovered that using ensemble machine learning models and additional features, including the actual power from the previous time step, enhances the accuracy of the wind power calculation. The root-mean-square error achieved is 113 kW, with the nominal capacity of the wind turbine under consideration being 3.6 MW. Consequently, the ratio of the root-mean-square error to the nominal capacity is 3%. wind turbine; wind power forecasting; power curve; ensemble models; machine learning; feature importance +
Footnote †: journal:  
_Applied system_  
_innovation_  
_Article_  
speed, wind direction, air temperature, and other factors to build a power generation forecasting model. For example, the paper [17] shows the use of convolutional neural networks to predict wind power output based on meteorological information. For all wind turbines, manufacturers provide the functional dependencies of the power generation on the wind speed. Wind turbines operate on the principle of converting wind energy into mechanical energy. The blades of the wind turbine rotate under the influence of the wind. This rotation is transmitted to the generator, which ultimately produces electricity. Therefore, the physics of the process of electricity generation by a wind turbine requires a more careful study of the possibility of calculating generation from wind speed, considering such factors as inertia, air density, and thermal processes [18]. Thus, wind speed forecasting and wind turbine generation forecasting are different tasks. Although wind speed can be used to calculate generation, accurately determining the wind turbine generation is a separate task [19]. As theoretical wind power curves cannot always characterize the actual state and performance of the wind turbine, it is necessary to use data from the real operating wind turbines and farms to obtain more accurate power curves. There is a wide variety of different approaches used for wind power prediction [20]: warping function [21], linearized segmented model, polynomial power curve, ideal power curve [22], probabilistic model [23], dynamical power curve [24], and logistic models [25]. A data synthesis-informed training U-net-based method of solving the wind turbine power curve modeling problem from the image processing perspective is presented in [26]. During recent years, machine learning algorithms have become one of the most popular approaches, replacing the mathematical models. Authors of the study [19] propose a new method to detect the outliers and assess the power curves with the use of clustering algorithms of vector quantization and density-based spatial clustering. The proposed method is tested on Korean wind farms. According to the research, this is one approach to increasing the efficiency in terms of eliminating noise from the data. Calculating short-term and ultra-short-term wind turbine power generation is important not only for wind power forecasting, but also for creating digital twins of wind turbines that can be used to assess their condition and for other purposes [27; 28; 29]. Paper [30] presents a deep-learning approach to predicting the power generation for the various wind turbines using a two-stage modeling strategy, where the neural network unites the spatiotemporal correlation.