This repository contains codes for the paper Short-term Load Forecasting with Deep Residual Networks.
We opensource in this repository the model used for the ISO-NE test case. Code for ResNetPlus model can be found in /ISO-NE/ResNetPlus_ISONE.py
The dataset contains load and temperature data from 2003 to 2014.
The code for the North American test case is added. Learning rate decay is added to produce more stable results.
(2021-05-11) Competition: DAY-AHEAD ELECTRICITY DEMAND FORECASTING: POST-COVID PARADIGM
The implementation of the proposed model in Pytorch ranked 5# (team 19) in this competition.
If you find the codes useful in your research, please consider citing:
@article{chen2018short,
title={Short-term load forecasting with deep residual networks},
author={Chen, Kunjin and Chen, Kunlong and Wang, Qin and He, Ziyu and Hu, Jun and He, Jinliang},
journal={IEEE Transactions on Smart Grid},
year={2018},
publisher={IEEE}
}