# Short-term Load Forecasting with Deep Residual Networks

arXiv: https://arxiv.org/abs/1805.11956 · HTML: https://mlsanity.com/paper/1805.11956/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2018.

## Built on by (most cited first, 22 of 22)

- [ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting](https://mlsanity.com/paper/2112.02663/) (2021)
- [ES-dRNN with Dynamic Attention for Short-Term Load Forecasting](https://mlsanity.com/paper/2203.00937/) (2022)
- [Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows](https://mlsanity.com/paper/2204.13939/) (2022)
- [Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2408.16202/) (2024)
- [Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting](https://mlsanity.com/paper/2212.09030/) (2022)
- [Deep Learning for Energy Time-Series Analysis and Forecasting](https://mlsanity.com/paper/2306.09129/) (2023)
- [Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach](https://mlsanity.com/paper/2108.11763/) (2021)
- [An Optimal Time Variable Learning Framework for Deep Neural Networks](https://mlsanity.com/paper/2204.08528/) (2022)
- [A Survey of Historical Learning: Learning Models with Learning History](https://mlsanity.com/paper/2303.12992/) (2023)
- [Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients](https://mlsanity.com/paper/2309.13194/) (2023)
- [Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting](https://mlsanity.com/paper/2202.03264/) (2022)
- [Appliance Level Short-term Load Forecasting via Recurrent Neural Network](https://mlsanity.com/paper/2111.11998/) (2021)
- [A Global Modeling Approach for Load Forecasting in Distribution Networks](https://mlsanity.com/paper/2204.00493/) (2022)
- [Prediction of Energy Consumption for Variable Customer Portfolios Including Aleatoric Uncertainty Estimation](https://mlsanity.com/paper/2110.02166/) (2021)
- [An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development](https://mlsanity.com/paper/2203.13563/) (2022)
- [FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Buildings](https://mlsanity.com/paper/2209.01338/) (2022)
- [A Unifying Framework of Attention-based Neural Load Forecasting](https://mlsanity.com/paper/2305.05082/) (2023)
- [Reliable Grid Forecasting: State Space Models for Safety-Critical Energy Systems](https://mlsanity.com/paper/2601.01410/) (2026)
- [Short-term electricity load forecasting with multi-frequency reconstruction diffusion](https://mlsanity.com/paper/2601.06533/) (2026)
- [Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting](https://mlsanity.com/paper/2602.21415/) (2026)
- [Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia](https://mlsanity.com/paper/2604.12304/) (2026)
- [Modeling spatio-temporal locality in multi-step forecasting of geo-referenced time series](https://mlsanity.com/paper/2608.25698/) (2026)

## 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.