TY - GEN A1 - Li, Yang A1 - Janik, Przemysław A1 - Schwarz, Harald T1 - Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation T2 - Electrical Engineering N2 - The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. KW - Artificial intelligence KW - Aggregated wind power characteristics KW - Regional wind power KW - Engineering Y1 - 2024 U6 - https://doi.org/10.1007/s00202-023-02005-z SN - 0948-7921 SN - 1432-0487 VL - 106 IS - 1 SP - 655 EP - 671 PB - Springer CY - Berlin Heidelberg ER -