## 4 Conclusions  
In this study, an analysis was conducted to examine the influence of various characteristics beyond wind speed on the accuracy of estimating wind turbine output power. The analysis utilized one year of real turbine data with a 10 min measurement interval. The necessity of incorporating additional features beyond wind speed and the estimated theoretical power derived from the power curve is substantiated. It is discovered that incorporating power values from the previous time step can enhance accuracy by accounting for the inertial properties of the wind turbine.  
Using categorical boosting, the root-mean-square error (RMSE) achieved was 113 kW, with the nominal capacity of the wind turbine under consideration being 3.6 MW. Consequently, the ratio of the RMSE to the nominal capacity is 3%. In comparison, the RMSE of the theoretical power curve was 262 kW. It is important to note that the data used in this study may contain measurement errors, which could potentially result in an under  
Figure 12: Comparison of real, theoretical, and predicted power on different dataset fragments: (**a**) 12–14 January 2018, (**b**) 9–11 February 2018, (**c**) 10–18 July 2018.  
estimation of the power curve error in real-world scenarios. However, during the data preprocessing stage, all obvious measurement errors were identified and removed.  
It should be noted that the accuracy of wind power forecasting over short periods of time will be higher when using a more accurate model of the power curve compared with the accuracy achieved using the widely employed theoretical power curve. This is because wind turbine power is influenced by various factors that are not accounted for in the traditional dependence of power on wind speed alone.  
The limitations of this study include the use of data from only one year and one wind turbine. However, publicly available real data with a measurement interval of less than an hour could not be identified. We plan to acquire additional data in the future. Another direction for improving the research is the development of machine learning models for forecasting wind power based on forecasted wind speed and other meteorological parameters, rather than actual wind speed. These models will consider different forecasting horizons, including 10 min, 1 h, and 24 h. In addition, we will conduct a more in-depth analysis of the significance of the features in further studies.  
Conceptualization P.V.M. and A.I.K.; methodology P.V.M. and D.A.H.; software, P.V.M. and M.V.M.; validation, M.V.M.; formal analysis, A.I.K.; investigation, P.V.M.; resources, A.I.K. and P.V.M.; data curation, M.V.M.; writing--original draft preparation, D.A.H. and M.V.M.; writing--review and editing, P.V.M.; visualization, A.I.K. and P.V.M.; supervision, A.I.K.; project administration, A.I.K.; funding acquisition A.I.K. All authors have read and agreed to the published version of the manuscript.  
This research was carried out within the state assignment with the financial support of the Ministry of Science and Higher Education of the Russian Federation (subject No. FEUZ-202-0030, development of an intelligent multi-agent system for modeling deeply integrated technological systems in the power industry).  
Not applicable.  
The original data presented in the study are openly available in Kaggle at [[https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset](https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset)]([https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset](https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset)), accessed on 22 August 2024.  
The authors declare no conflicts of interest.