论文标题
转移学习是可再生能源系统中数字双胞胎的重要工具
Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems
论文作者
论文摘要
转移学习（TL）是机器学习（ML）的下一个前沿（ML），近年来由于ML面临的各种挑战，例如需要大量培训数据，昂贵且耗时的数据样本标签过程，以及模型的长期培训时间。 TL可用于解决这些问题，因为它专注于将知识从以前解决的任务转移到新任务。数字双胞胎和其他智能系统需要利用TL来使用以前获得的知识并以更自力更生的方式解决新任务，并逐步增加其知识库。因此，在本文中，在可再生能源系统的背景下，在权力预测和异常检测中面临着关键的挑战，并提出了应对这些挑战的潜在TL框架。本文还提出了一种处理缺失传感器数据的功能嵌入方法。提出的TL方法有助于使系统在有机计算的背景下更加自主。
Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.
