Due to the strong volatility of the electrical load and the defect of a time-consuming problem, in addition to overfitting existing in published forecasting methods, short-term electrical demand is difficult to forecast accurately and robustly. Given the excellent capability of weight sharing and feature extraction for convolution, a novel hybrid method based on ensemble GoogLeNet and modified deep residual networks for short-term load forecasting (STLF) is proposed to address these issues. Specifically, an ensemble GoogLeNet with dense block structure is used to strengthen feature extraction ability and generalization capability. Meanwhile, a group normalization technique is used to normalize outputs of the previous layer. Furthermore, a modified deep residual network is introduced to alleviate a vanishing gradient problem in order to improve the forecasting results. The proposed model is also adopted to conduct probabilistic load forecasting with Monte Carlo Dropout. Two acknowledged public datasets are used to evaluate the performance of the proposed methodology. Multiple experiments and comparisons with existing state-of-the-art models show that this method achieves accurate prediction results, strong generalization capability, and satisfactory coverages for different prediction intervals, along with reducing operation times.
Short-term scenario-based probabilistic load forecasting: A data-driven approach