mlsanityData to 7 Oct 2026

Short-term Load Forecasting with Deep Residual Networks

arXiv 1805.11956 ↗First posted 30 May 2018stat.ML cs.LG stat.APcited by 22

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Abstract (arXiv)

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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2018.

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Citing papers per year: 2021: 4; 2022: 8; 2023: 4; 2024: 1; 2025: 0; 2026: 5.

22 papers.

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  1. 2021ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecastingcited by 11
  2. 2022ES-dRNN with Dynamic Attention for Short-Term Load Forecastingcited by 6
  3. 2022Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flowscited by 6
  4. 2024Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Surveycited by 6
  5. 2023Deep Learning for Energy Time-Series Analysis and Forecastingcited by 5
  6. 2022Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecastingcited by 4
  7. 2021Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approachcited by 3
  8. 2022An Optimal Time Variable Learning Framework for Deep Neural Networkscited by 3
  9. 2023A Survey of Historical Learning: Learning Models with Learning Historycited by 3
  10. 2023Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clientscited by 3
  11. 2022Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecastingcited by 2
  12. 2021Appliance Level Short-term Load Forecasting via Recurrent Neural Networkcited by 1
10 more
  1. 2022A Global Modeling Approach for Load Forecasting in Distribution Networkscited by 1
  2. 2021Prediction of Energy Consumption for Variable Customer Portfolios Including Aleatoric Uncertainty Estimationcited by 0
  3. 2022An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Developmentcited by 0
  4. 2022FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Buildingscited by 0
  5. 2023A Unifying Framework of Attention-based Neural Load Forecastingcited by 0
  6. 2026Reliable Grid Forecasting: State Space Models for Safety-Critical Energy Systemscited by 0
  7. 2026Short-term electricity load forecasting with multi-frequency reconstruction diffusioncited by 0
  8. 2026Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecastingcited by 0
  9. 2026Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australiacited by 0
  10. 2026Modeling spatio-temporal locality in multi-step forecasting of geo-referenced time seriescited by 0