We present in this paper a model for forecasting short-term power loads 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 is able to provide accurate load forecasting
results and has high generalization capability.