# Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2408.16202 · HTML: https://mlsanity.com/paper/2408.16202/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 263 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [Short-term Load Forecasting with Deep Residual Networks](https://mlsanity.com/paper/1805.11956/) (2018)
- [Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations](https://mlsanity.com/paper/2106.00006/) (2021)
- [Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources](https://mlsanity.com/paper/2305.10559/) (2023)
- [DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks](https://mlsanity.com/paper/2305.08767/) (2023)
- [TimeGPT in Load Forecasting: A Large Time Series Model Perspective](https://mlsanity.com/paper/2404.04885/) (2024)
- [Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data](https://mlsanity.com/paper/2301.10663/) (2023)
- [A Survey on Adaptive Random Testing](https://mlsanity.com/paper/2007.03885/) (2020)
- [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)

## Built on by (most cited first, 6 of 6)

- [VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting](https://mlsanity.com/paper/2509.15394/) (2025)
- [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)
- [Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series](https://mlsanity.com/paper/2605.04957/) (2026)
- [Short-Term Electricity Demand Forecasting for New England: A Comprehensive Machine Learning Benchmark with Weather, Calendar, and COVID-19 Indicators](https://mlsanity.com/paper/2606.20918/) (2026)

## Abstract (arXiv)

Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate demand (in the next few hours to several days) for the power system. Various external factors, such as weather changes and the emergence of new electricity consumption scenarios, can impact electricity demand, causing load data to fluctuate and become non-linear, which increases the complexity and difficulty of STELF. In the past decade, deep learning has been applied to STELF, modeling and predicting electricity demand with high accuracy, and contributing significantly to the development of STELF. This paper provides a comprehensive survey on deep-learning-based STELF over the past ten years. It examines the entire forecasting process, including data pre-processing, feature extraction, deep-learning modeling and optimization, and results evaluation. This paper also identifies some research challenges and potential research directions to be further investigated in future work.