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.

中文翻译：

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基于神经基础扩展模型的预测区间的概率负荷预测

为了缩小预测区间的宽度，同时保证概率短期负荷预测的覆盖范围，我们提出了一种基于神经基础扩展分析（N-BEATS）的深度学习预测模型。它将加载数据作为输入，并将加载序列送入三个堆栈。每个堆栈将加载序列投影到一组基向量上。基向量和相应的系数都由神经网络学习。采用了一种新的双残差堆叠策略，假设载荷序列通常可以分别由较低维子空间中的三种模式表示，将预测任务分解为三个子问题，即对应于堆叠的模式表征任务。它删除了每个堆栈中的冗余信息，这引导堆栈专注于学习一种模式。我们进一步应用保形分位数回归，它使用保留验证中的残差来校准构建的预测区间以获得更好的理论覆盖保证。基于 UT Dallas 提供的负载数据集的实验证明了所提出的模型在捕获负载特征和提供接近标称覆盖的窄预测间隔方面的改进性能。