Short-Term Load Forecasting With Deep Residual Networks

By Kunjin Chen, Kunlong Chen, Qin Wang, Ziyu He, Jun Hu, Jinliang He. IEEE Transactions on Smart Grid, 2018

542 citations recorded by the source corpus.

We present in this paper a model for forecasting short-term electric load based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers’ understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated to improve the forecast results. Further, a two-stage ensemble strategy is used to enhance the generalization capability of the proposed model. We also apply the proposed model to probabilistic load forecasting using Monte Carlo dropout. Three public datasets are used to prove the effectiveness of the proposed model. Multiple test cases and comparison with existing models show that the proposed model provides accurate load forecasting results and has high generalization capability.

Subfields: Building and Construction, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6aae2606ec782a2da4268618c9301881f9cdc44d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Cesare Alippi
2. Slobodan Lukovic
3. Alberto Gasparin

In Computer Vision and Pattern Recognition

1. Cesare Alippi
2. Slobodan Lukovic
3. Hao Dong
4. Shihao Tang
5. Shaobo Li
6. Jing Yang
7. Jun Wang
8. Alberto Gasparin

In Electrical and Electronic Engineering

1. Haitham Abu-Rub
2. Cesare Alippi
3. Slobodan Lukovic
4. Alberto Gasparin

Citations in the corpus

• Deep Learning for Time Series Forecasting: The Electric Load Case (2019, methodology): “More recently, architectural variants of feed forward neural networks have been used; for example, in [14] a ResNet [42] inspired model is used to provide day ahead forecast by leveraging on a very deep architecture.”
• A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting (2021, methodology): “For the time horizon, most of the LF employed techniques can be divided into four major classes; long-term LF valid for years, medium-term LF valid from months to years, Short-Term Load Forecasting (STLF) used from minutes up to one week, and very short-term LF valid from seconds to minutes [6].”
• Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, background): “The residual network is characterized by easy optimization and can improve the accuracy by increasing the network depth [88,89].”