Probabilistic Load Forecasting via Neural Basis Expansion Model Based Prediction Intervals

To narrow the width of prediction interval while guaranteeing coverage for probabilistic short term load forecasting, we propose a deep-learning forecasting model based on neural basis expansion analysis (N-BEATS). It takes load data as input, and feed the load sequence into three stacks. Each stack projects the load sequence on a set of basis vectors. Both the basis vectors and the corresponding coefficients are learned by the neural networks. A novel doubly residual stacking strategy is adopted, which decomposes forecasting task into three sub-problems, i.e., pattern characterization tasks corresponding to the stacks, under the assumption that load series can generally be represented by three patterns in subspaces with lower dimensions respectively. It removes redundant information in each stack, which guides the stack to concentrate on learning of one pattern. We further apply conformal quantile regression, which uses the residuals in a held-out validation, to calibrate constructed prediction interval for better theoretical coverage guarantee. Experiments based on load dataset provided by UT Dallas demonstrate improved performance of the proposed model in capturing the characteristics of load and providing narrow prediction intervals with nearly nominal coverage.