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.

翻译：向完全可再生能源电网转型需要更好地预测低压层面的需求，以提高效率并确保可靠控制。然而，高波动性和日益增长的电气化导致预测变异性巨大，传统的点估计无法反映这一点。概率负荷预测考虑了未来的不确定性，从而为低碳能源系统的规划与运行提供更具信息量的决策支持。我们提出了一种基于伯恩斯坦多项式归一化流的灵活条件密度短期负荷预测方法，其中神经网络控制流的参数。在一项包含363个智能电表用户的实证研究中，我们的密度预测优于高斯密度和高斯混合密度。此外，对于两种不同的神经网络架构，我们的方法在24小时提前负荷预测中，性能优于基于分位数损失的非参数方法。