Short-Term Load Forecasting With Deep Residual Networks

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 res...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2019-07, Vol.10 (4), p.3943-3952
Main Authors: Chen, Kunjin, Chen, Kunlong, Wang, Qin, He, Ziyu, Hu, Jun, He, Jinliang
Format: Article
Language:English
Subjects:
Computer simulation
deep learning
deep residual network
Forecasting
Load forecasting
Load modeling
Mathematical models
Model testing
Neural networks
Predictive models
probabilistic load forecasting
Probabilistic logic
Short-term load forecasting
Task analysis
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Summary: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.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2018.2844307