Image credit: Unsplash
Abstract
In this paper, we consider the load forecasting for a new user in the system by observing only few shots (data points) of its energy consumption. We propose to utilize clustering to mitigate the challenges brought by the limited samples. Specifically, we first design a feature extraction clustering method for categorizing the historical data. Then, the load forecast for new users is conducted through a two-phase Long Short Term Memory (LSTM) model, which inherits prior knowledge from the clustering results.
Type
Publication
In IEEE Southern Power Electronics Conference
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Create your slides in Markdown - click the Slides button to check out the example.
Supplementary notes can be added here, including code, math, and images.
Zhihui Chen
Undergraduate Student Major in Statistics
My research interests include robotics, computer vision and few-shot learning.