Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features

Accurate prediction of regional wind power generation intervals is an effective support tool for the economic and stable operation of provincial power grid. However, it involves a large amount of high-dimensional meteorological and historical power generation information related to massive wind power stations in a province. In this paper, a lightweight model is developed to directly obtain probabilistic predictions in the form of intervals. Firstly, the input features are formed through a fused image generation method of geographic and meteorological information as well as a power aggregation strategy, which avoids the extensive and tedious data processing process prior to modeling in the traditional approach. Then, in order to effectively consider the spatial meteorological distribution characteristics of regional power stations and the temporal characteristics of historical power, a parallel prediction network architecture of a convolutional neural network (CNN) and long short-term memory (LSTM) is designed. Meanwhile, an efficient channel attention (ECA) mechanism and an improved quantile regression-based loss function are introduced in the training to directly generate prediction intervals. The case study shows that the model proposed in this paper improves the interval prediction performance by at least 12.3% and reduces the deterministic prediction root mean square error (RMSE) by at least 19.4% relative to the benchmark model.