Simultaneous load forecasting across multiple entities (e.g., regions, buildings) is crucial for the efficient, reliable, and cost-effective operation of power systems. Accurate load forecasting is a challenging problem due to the inherent uncertainties in load demand, dynamic changes in consumption patterns, and correlations among entities. Multi-task learning has emerged as a powerful machine learning approach that enables the simultaneous learning across multiple related problems. However, its application to load forecasting remains underexplored and is limited to offline learning methods, which cannot capture changes in consumption patterns. This paper presents an adaptive multi-task learning method for probabilistic load forecasting. The proposed method can dynamically adapt to changes in consumption patterns and correlations among entities. In addition, the techniques presented provide reliable probabilistic predictions for loads of multiple entities and assess load uncertainties. Specifically, the method is based on vectorvalued hidden Markov models and uses a recursive process to update the model parameters and provide predictions with the most recent parameters. The performance of the proposed method is evaluated using datasets that contain the load demand of multiple entities and exhibit diverse and dynamic consumption patterns. The experimental results show that the presented techniques outperform existing methods both in terms of forecasting performance and uncertainty assessment.

翻译：对多个实体（如区域、建筑物）进行同步负荷预测，对于电力系统的高效、可靠和经济运行至关重要。由于负荷需求固有的不确定性、用电模式的动态变化以及实体间的相关性，准确的负荷预测是一个具有挑战性的问题。多任务学习已成为一种强大的机器学习方法，能够同时学习多个相关问题。然而，其在负荷预测中的应用仍待深入探索，且目前仅限于离线学习方法，无法捕捉用电模式的变化。本文提出了一种用于概率负荷预测的自适应多任务学习方法。所提方法能够动态适应用电模式的变化以及实体间的相关性。此外，所提出的技术可为多个实体的负荷提供可靠的概率预测，并评估负荷的不确定性。具体而言，该方法基于向量值隐马尔可夫模型，并采用递归过程来更新模型参数，并使用最新参数提供预测。使用包含多个实体负荷需求且呈现多样化和动态用电模式的数据集对所提方法的性能进行了评估。实验结果表明，所提出的技术在预测性能和不确定性评估方面均优于现有方法。