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. Index Terms-Short-term load forecasting, deep learning, deep residual network, probabilistic load forecasting.
Chen et al. seek to integrate domain knowledge in different neural network building blocks in REF .
Short-Term Load Forecasting With Deep Residual Networks
IEEE Transactions on Smart Grid
IEEE Transactions on Smart Grid
Mathematics Computer Science Engineering