Abstract In short-term power load forecasting, changes in external multi-dimensional factors will have a certain impact on the accuracy of forecasting. response to this problem, paper proposes prediction model GWO-BILSTM that combines Grey Wolf Optimizer (GWO) and bi-directional long memory (BILSTM) network. Taking real data as set, high correlation parameters are selected input through Pearson...

This paper introduces an advanced Short-term Nodal Load Forecasting (STNLF) method that forecasts nodal load profiles for the next day in power systems, based on combined use of three machine learning techniques. Least Absolute Shrinkage and Selection Operator (LASSO) is employed to reduce number features a single forecasting. Principal Component Analysis (PCA) used capture historical loads low...

Accurate power load forecasting plays an integral role in systems. To achieve high prediction accuracy, models need to extract effective features from raw data, and the training of needs a large amount data. However, data sharing will require disclosure private participants. address this issue, we combined variational mode decomposition (VMD), federated k-means clustering algorithm (FK), Secure...