Electricity load forecasting is a necessary capability for power system operators and electricity market participants. The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modelling and forecasting. We address this challenge in two ways. First, our setting is adaptive; our models take into account the most recent observations available, yielding a forecasting strategy able to automatically respond to changes in the underlying process. Second, we consider probabilistic rather than point forecasting; indeed, uncertainty quantification is required to operate electricity systems efficiently and reliably. Our methodology relies on the Kalman filter, previously used successfully for adaptive point load forecasting. The probabilistic forecasts are obtained by quantile regressions on the residuals of the point forecasting model. We achieve adaptive quantile regressions using the online gradient descent; we avoid the choice of the gradient step size considering multiple learning rates and aggregation of experts. We apply the method to two data sets: the regional net-load in Great Britain and the demand of seven large cities in the United States. Adaptive procedures improve forecast performance substantially in both use cases for both point and probabilistic forecasting.

翻译：电力负荷预测是电力系统运营者和电力市场参与者所需的关键能力。分布式发电、需求响应以及热力和交通电气化的普及正在改变电力负荷的基本驱动因素，并增加了负荷建模与预测的复杂性。我们通过两种方式应对这一挑战。首先，我们的设定具有自适应性；模型考虑最新的可用观测数据，形成一种能够自动响应潜在过程变化的预测策略。其次，我们采用概率预测而非点预测；实际上，量化不确定性对于高效可靠地运行电力系统至关重要。我们的方法依赖于卡尔曼滤波器，该滤波器此前已成功用于自适应点负荷预测。概率预测通过对点预测模型残差进行分位数回归来实现。我们利用在线梯度下降实现自适应分位数回归；通过考虑多种学习率并进行专家聚合，避免了梯度步长选择的问题。我们将该方法应用于两个数据集：英国的区域净负荷和美国七个大型城市的用电需求。在点预测和概率预测两种场景中，自适应程序显著提升了这两个应用案例的预测性能。