Short-Term Electricity-Load Forecasting by deep learning: A comprehensive survey

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Abstract

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. Over the past decade, deep learning, as a key component of implemented artificial intelligence, has been widely applied to STELF, enabling accurate modeling and prediction of electricity demand. This paper provides a comprehensive survey on deep-learning-based STELF over the past ten years. It examines the…

Citation impact

46
total citations
FWCI
26.26
Percentile
100%
References
282
Citations per year

Authors

7

Topics & keywords

Keywords
  • Computer science
  • Term (time)
  • Electricity
  • Artificial intelligence
  • Machine learning
  • Operations research
  • Industrial engineering
  • Electrical engineering
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