论文标题
低压网络的概率负载预测：预测融合和每日峰
Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks
论文作者
论文摘要
短期能源消耗的预测对于能源系统的运行，包括低压电力网络是无价的。但是，网络负载挑战是预测何时将高度种类划分为少数客户，这可能由个人行为而不是与总消费相关的平滑概况主导。此外，分销网络几乎完全受到峰值负载的挑战，并且可以通过预测的峰值需求来驱动安排存储和/或需求灵活性之类的任务，该功能通常以通用预测方法的特征较差。在这里，我们提出了一种预测每日峰值需求的时间和水平的方法，以及结合常规和峰值预测的数据融合程序，以产生通用概率预测，并在峰期间提高性能。使用真实的智能电表数据和一个假设的低压网络层次结构，包括馈线，次要和主要变电站，证明了所提出的方法。发现与峰值预测的最新概率负载预测可以提高整体性能，尤其是在智能计和进料器水平上以及在高峰时段，在CRP方面的改善超过10％。
Short-term forecasts of energy consumption are invaluable for the operation of energy systems, including low voltage electricity networks. However, network loads are challenging to predict when highly desegregated to small numbers of customers, which may be dominated by individual behaviours rather than the smooth profiles associated with aggregate consumption. Furthermore, distribution networks are challenged almost entirely by peak loads, and tasks such as scheduling storage and/or demand flexibility maybe be driven by predicted peak demand, a feature that is often poorly characterised by general-purpose forecasting methods. Here we propose an approach to predict the timing and level of daily peak demand, and a data fusion procedure for combining conventional and peak forecasts to produce a general-purpose probabilistic forecast with improved performance during peaks. The proposed approach is demonstrated using real smart meter data and a hypothetical low voltage network hierarchy comprising feeders, secondary and primary substations. Fusing state-of-the-art probabilistic load forecasts with peak forecasts is found to improve performance overall, particularly at smart-meter and feeder levels and during peak hours, where improvement in terms of CRPS exceeds 10%.
