mlsanityData to 7 Oct 2026

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

arXiv 2408.16202 ↗First posted 29 Aug 2024cs.LG cs.AIcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 263 references that are in the map. First posted on arXiv 29 Aug 2024.

Builds on

14 of 263 references.

References · 14

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2 more
  1. 2020A Survey on Adaptive Random Testingcited by 2
  2. 2022Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecastingcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecastingcited by 0
  2. 2026Reliable Grid Forecasting: State Space Models for Safety-Critical Energy Systemscited by 0
  3. 2026Short-term electricity load forecasting with multi-frequency reconstruction diffusioncited by 0
  4. 2026Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecastingcited by 0
  5. 2026Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Seriescited by 0
  6. 2026Short-Term Electricity Demand Forecasting for New England: A Comprehensive Machine Learning Benchmark with Weather, Calendar, and COVID-19 Indicatorscited by 0