English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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