The extensive deployment of smart meters in millions of households provides a huge amount of individual electricity consumption data for demand side analysis at a fine granularity. Different from traditional aggregated system-level data, smart meter data is more irregular and unpredictable. As a result, probabilistic load forecasting (PLF), which can provide a better understanding of the uncertainty and volatility in future demand, is critical to constructing energy-efficient and reliable smart grids. In this article, a recently developed technique called Bayesian deep learning is employed to solve this challenging problem. In particular, a novel multitask PLF framework based on Bayesian deep learning is proposed to quantify the shared uncertainties across distinct customer groups while accounting for their differences. Further, a clustering-based pooling method is designed to increase the data diversity and volume for the framework. This not only addresses the problem of overfitting but also improves the predictive performance. Numerical results are presented which demonstrate that the proposed framework provides superior probabilistic forecasting accuracy over conventional methods.

中文翻译：

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智能电网中基于贝叶斯深度学习的概率负载预测

智能电表在数百万个家庭中的广泛部署提供了大量的个人用电量数据，可以以精细的粒度进行需求侧分析。与传统的聚合系统级数据不同，智能电表数据更加不规则且不可预测。因此，概率负载预测（PLF）可以更好地了解未来需求的不确定性和波动性，对于构建节能高效且可靠的智能电网至关重要。在本文中，采用了一种最新发展的技术，称为贝叶斯深度学习，以解决这一难题。特别是，提出了一种基于贝叶斯深度学习的新颖的多任务PLF框架，以量化不同客户群体之间的共享不确定性，同时考虑他们之间的差异。进一步，设计了一种基于群集的池化方法，以增加框架的数据多样性和数量。这不仅解决了过度拟合的问题，而且改善了预测性能。数值结果表明，提出的框架提供了优于传统方法的较高概率预测精度。