A multi-step day-ahead wind power forecasting based on VMD-LSTM-EFG-ABC technique

Abstract

Accurate and robust wind power prediction for wind farms could significantly decrease the substantial effect on grid operating safety caused by integrating high-permeability intermittent power supplies into the power grid. The article introduces a new wind power multistep prediction model combining Variational Mode De-composition (VMD) with the Long Short-Term Enhanced Forget Gate (LSTM_EFG) network. The VMD is occupied to break down the initial wind power and speed data into various sub-layers. The LSTM_EFG network predicts the low-frequency sub-layers extracted from the VMD. In contrast, the Artificial Bee Colony optimization algorithm fine-tunes the network for the high-frequency sub-layers acquired from the VMD-LSTM-EFG model. The high performance of projected methods in multistep prediction was evaluated by comparing them with eight different models. Results from four experiments show that: (a) the projected model exhibits the most superior multistep prediction performance out of all models tested; (b) in comparison to other models, the proposed model proves to be more efficient and resilient in capturing trend information. The implementation of accurate wind power prediction models continues to pose challenges due to the unpredictable, sudden, and seasonal changes in wind patterns.