Short-Term Electricity Load Forecasting: A Benchmark Study of Gradient Boosting Against Deep Sequence Models

Authors

  • Berit Rappenglueck Department of Statistics, Spessart Institute of Applied Artificial Intelligence, Aschaffenburg, Germany Author
  • Regula Roethlisberg Department of Energy Systems, Surselva Institute of Applied Artificial Intelligence, Ilanz, Switzerland Author
  • Ziga Ravnikar Department of Data Science, Notranjska Institute of Applied Artificial Intelligence, Postojna, Slovenia Author

DOI:

https://doi.org/10.5281/zenodo.22657217

Keywords:

Short-Term Electricity Load Forecasting; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Short-Term Electricity Load Forecasting is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for short-term electricity load
forecasting, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has
converged and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We
conclude that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.

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Published

2022-04-12

Issue

Vol. 1 No. 1 (2022): Vol 1 Issue 1 2022

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Articles

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