Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity
Wei Guo, Xiang Zha, Kun Qian, Tao Chen · 2019
In the past decade, a plethora of efforts were given to the field of facilitating a better smart grid system by leveraging the power of artificial intelligence. Undoubtedly, machine learning is currently playing an increasingly important role in almost every aspect of power systems. However, in real practice, there is a much larger amount of unlabelled data than the one labelled by human experts. In this work, we make a perspective study on overcoming the data scarcity in smart grid. The active learning strategy will be proposed to provide a feasible solution for addressing the data scarcity challenge. In addition, we will give a discussion on current state-of-the-art and the limitations in previous work. We hope this work can be a good guide for researchers to further the relevant study in the near future.