Research on Fluctuation Recognition and Short-term Forecasting of New Energy Power Generation Based on ICEEMD-VMD and Bi-LSTM

This research focuses on the fluctuation recognition and short-term forecasting of new energy power generation, specifically targeting wind and solar farms. We propose a novel two-layer decomposition model based on ICEEMD-VMD to preprocess the non-stationary and volatile output power data, effectively reducing noise and improving data quality. The study explores the fluctuation patterns of wind and solar power generation, establishing a dynamic thresholdbased fluctuation recognition model to detect significant power changes. For short-term forecasting, a Bi-LSTM model is employed to predict the output power components obtained from the ICEEMD-VMD decomposition. The model's performance is evaluated using RMSE, MAE, and goodness of fit (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>), demonstrating high accuracy and reliability. Our findings indicate that the proposed methods can effectively capture and predict power generation fluctuations, providing valuable insights for grid operation and energy management.