Document Type
Conference Proceeding
Publication Date
5-4-2022
Department
Department of Computer Science
Abstract
In an electricity system, a coincident peak (CP) is defined as the highest daily power demand in a year, which plays an important role in keeping the balance between power supply and its demand. Advanced information about the time of coincident peaks would be helpful for both utility companies and their customers. This work addresses the prediction of the five coincident peak days (5CP) in a year. We present a few-shot learning model to classify a day as a 5CP day or a non-5CP day 24-hours ahead. A triplet network is implemented for the 2-way-5-shot classifications on six different historical datasets. The prediction results have an average (across the six datasets) mean recall of 0.933, mean precision of 0.603, and mean F1 score of 0.733.
Publication Title
Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS
Recommended Citation
Liu, J.,
&
Brown, L.
(2022).
A Few-shot Learning Model based on a Triplet Network for the Prediction of Energy Coincident Peak Days.
Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS,
35.
http://doi.org/10.32473/flairs.v35i.130733
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16164
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Version
Publisher's PDF
Publisher's Statement
Copyright (c) 2022 Jinxiang Liu, Laura E. Brown. Publisher’s version of record: https://doi.org/10.32473/flairs.v35i.130733