VMD-based iterative Boruta feature extraction and CNNA-BiLSTM for short-term load forecasting.

Short-term load forecasting is vital for efficient power system planning. To better extract features expanded by the signal decomposition method and enhance the performance of load prediction models, this paper proposes an iterative Boruta feature extraction method based on variational modal decomposition (VMD-IB) and a bi-directional long short-term memory network assisted by convolutional neural network and self-attention mechanism(CNNA-BiLSTM). Firstly, VMD decomposes the load data to capture different patterns and scales. An iterative Boruta method is proposed to identify the most valuable features for load prediction through an adaptive iterative strategy during feature selection. VMD-IB shows an advantage in extracting correlated features at multiple scales, enabling the model to better capture complex load fluctuations. Secondly, CNNA-BiLSTM enhances the ability of BiLSTM to fit the nonlinear data using CNN for temporal feature extraction and SAM for attention and weight allocation, improving the feature expressiveness and prediction performance. Lastly, extensive experiments on New York Independent System Operator(NYISO) data validate the framework. Compared to the state-of-the-art method, the proposed method achieves a 12% improvement in mean absolute percentage error and an 8% improvement in the Theil inequality coefficient, showcasing the superiority of the proposed framework in accurately predicting short-term load demand. • Capturing multi-scale patterns and features using VMD. • Adaptive selection of key features through Boruta iteration. • CNNA-BiLSTM enhances nonlinear data fitting and prediction. • Proposed model effectively improves the prediction accuracy. [ABSTRACT FROM AUTHOR]