Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction

The role of renewable energy sources in the Polish power system is growing. The highest share of installed capacity goes to wind and solar energy. Both sources are characterized by high variability of their power output and very low dispatchability. Taking into account the nature of the power system, it is, therefore, imperative to predict their future energy generation to economically schedule the use of conventional generators. Considering the above, this paper examines the possibility to predict day-ahead wind power based on different machine learning methods not for a specific wind farm but at national level. A numerical weather prediction model used operationally in the Institute of Meteorology and Water Management-National Research Institute in Poland and hourly data of recorded wind power generation in Poland were used for forecasting models creation and testing. With the best method, the Extreme Gradient Boosting, and two years of training (2018-2019), the day-ahead, hourly wind power generation in Poland in 2020 was predicted with 26.7% mean absolute percentage error and 4.5% root mean square error accuracy. Seasonal and daily differences in predicted error were found, showing high mean absolute percentage error in summer and during daytime.

Automated summary

In the Polish power system, renewable energy sources like wind and solar energy play a growing role with high variability and low dispatchability. This study explores the prediction of day-ahead wind power at the national level in Poland using machine learning methods, achieving accuracy with a mean absolute percentage error of 26.7% and root mean square error of 4.5% for 2020. Seasonal and daily variations in prediction errors were observed, with higher errors in summer and daytime.