Short-term probabilistic load forecasting method based on uncertainty estimation and deep learning model considering meteorological factors.

Probabilistic load forecasting (PLF) are of critical importance to reliable and economical power system operations. To further improve the PLF performance, this paper extends the load forecasting model with high accuracy to PLF by using Monte Carlo (MC) Dropout and soft thresholding (ST) for uncertainty modeling and noise reduction, respectively. Specifically, the method first builds a Dual-stage Attention-based Long and Short Time Pattern Network (DA-LSTPNet) by weighting the temporal information extracted from Gate Recurrent Unit (GRU) and convolutional neural networks (CNN) through the time attention mechanism(TAM). Then, an adaptive ST structure is used to reduce the impact of meteorological data noise on model prediction performance. The term ST-DA-LSTPNet refers to DA-LSTPNet with the addition of the ST structure. Finally, an appropriate Dropout setting is inserted in the neural network layers during the model's testing phase to achieve the model parameters' stochastic process. After multiple sampling and calculations, the final PLF result is obtained. To demonstrate the effectiveness of the proposed method, this work construct a case study using the actual data from a prefecture-level city in southern China. Compared with several benchmark methods, the proposed method significantly improves the performance of PLF and has good anti-noise generalization ability. [Display omitted] • The high-accuracy load forecasting model extends to probabilistic load forecasting. • The adaptive soft thresholding can mitigate noise impact on prediction. • The model performs well on actual grid data, with over 92% prediction coverage. • This work provides effective support for economic scheduling and management. [ABSTRACT FROM AUTHOR]