论文标题
使用Bernstein-Polynomial标准化流对低压负载的短期密度预测
Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
论文作者
论文摘要
向完全可再生能源网格的过渡需要在低压水平上更好地预测需求，以提高效率并确保可靠的控制。但是，高波动和增加电气化会导致巨大的预测可变性，而不会反映在传统点估计中。概率负载预测考虑了未来的不确定性，因此可以为低碳能源系统的计划和运行提供更多明智的决策。我们提出了一种基于Bernstein多项式归一化流的柔性条件密度预测的方法，其中神经网络控制流动的参数。在与363个智能电表客户的实证研究中，我们的密度预测与高斯和高斯混合物密度相比有利。此外，根据两种不同的神经网络体系结构的24小时载荷预测的弹球损失，他们的表现优于非参数方法。
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizing flows, where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities. Also, they outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.
