Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent years due to the integration of renewable energies, electric cars, and microgrids. Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand. However, such techniques cannot assess intrinsic uncertainties in load demand, and cannot capture dynamic changes in consumption patterns. To address these problems, this paper presents a method for probabilistic load forecasting based on the adaptive online learning of hidden Markov models. We propose learning and forecasting techniques with theoretical guarantees, and experimentally assess their performance in multiple scenarios. In particular, we develop adaptive online learning techniques that update model parameters recursively, and sequential prediction techniques that obtain probabilistic forecasts using the most recent parameters. The performance of the method is evaluated using multiple datasets corresponding with regions that have different sizes and display assorted time-varying consumption patterns. The results show that the proposed method can significantly improve the performance of existing techniques for a wide range of scenarios.

翻译：由于可再生能源、电动汽车和微电网的一体化,近年来这种相关性甚至有所增加。常规负载预报技术通过利用过去负载需求的消费模式获得单值负载预测。然而,这些技术无法评估负载需求的内在不确定性,也无法捕捉消费模式的动态变化。为解决这些问题,本文件根据对隐蔽的Markov模型的适应性在线学习,提出了一种概率负载预测方法。我们提出了具有理论保证的学习和预测技术,并实验性地评估了这些技术在多种情景中的性能。特别是,我们开发了适应性在线学习技术,对模型参数进行循环更新,并使用最新参数连续预测,以获得概率性预测。对这种方法的性能进行了评估,使用与不同大小和显示不同时间变化的消费模式的区域相对应的多套数据集。结果显示,拟议方法可以大大改进各种情景现有技术的性能。