Probabilistic load forecasting with Reservoir Computing

Michele Guerra, Simone Scardapane, Filippo Maria Bianchi
8/24/2023
cs.LG

No official code found. Best implementation: eloMG/Probabilistic-load-forecasting-with-Reservoir-Computing (10 stars, MIT, last commit 2023-12-30).

Abstract

Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric power grid is one of these cases: to avoid risky scenarios, decision-makers need both precise and reliable forecasts of, for example, power loads. For this reason, point forecasts are not enough hence it is necessary to adopt methods that provide an uncertainty quantification. This work focuses on reservoir computing as the core time series forecasting method, due to its computational efficiency and effectiveness in predicting time series. While the RC literature mostly focused on point forecasting, this work explores the compatibility of some popular uncertainty quantification methods with the reservoir setting. Both Bayesian and deterministic approaches to uncertainty assessment are evaluated and compared in terms of their prediction accuracy, computational resource efficiency and reliability of the estimated uncertainty, based on a set of carefully chosen performance metrics.

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Code Implementations(1)

eloMG/Probabilistic-load-forecasting-with-Reservoir-ComputingCommunity73%
MIT

Probabilistic load forecasting with Reservoir Computing

103Python, Jupyter NotebookSep 23, 20222 years agoMIT
probabilistic-forecastingprobabilistic-programmingreservoir-computinguncertainty-neural-networksuncertainty-quantification

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Cite this paper

@article{guerra2023probabilistic,
  title  = {Probabilistic load forecasting with Reservoir Computing},
  author = {Michele Guerra and Simone Scardapane and Filippo Maria Bianchi},
  year   = {2023},
  eprint = {2308.12844},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.12844v1}
}

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