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：负荷预测对于发电容量调度、供需规划及能源交易成本最小化等多种能源管理任务至关重要。近年来，随着可再生能源、电动汽车和微电网的并网，其重要性愈发凸显。传统负荷预测技术通过利用历史负荷需求的消耗模式来获得单值负荷预测结果。然而，此类技术无法评估负荷需求的内在不确定性，也无法捕捉消耗模式的动态变化。为解决这些问题，本文提出一种基于隐马尔可夫模型自适应在线学习的概率负荷预测方法。我们提出了具有理论保证的学习与预测技术，并通过多场景实验评估其性能。具体而言，我们开发了递归更新模型参数的自适应在线学习技术，以及利用最新参数获得概率预测的序列预测技术。该方法在多个数据集上进行了性能评估，这些数据集对应不同规模且呈现多样化时变消耗模式的区域。结果表明，所提方法能在广泛场景下显著提升现有技术的预测性能。