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Published October 1, 2021 | Version v0.2.2
Software Open

ProLoaF: v0.2.2

  • 1. RWTH Aachen University
  • 1. RWTH Aachen University

Description

A Probabilistic Load Forecasting Project

v0.1.1

  • Updated User Guide
  • Added Functionalities to explain importance of features in training (Draft version)
  • Fixed smaller issues, such as links and consistent naming
  • Updated Benchmark Models
  • Added new Plot Functionalities, such as error distribution plots
  • Updated Plotting and Evaluation to compare multiple models with each other
  • Updates to Code Documentation now available at: https://sogno-platform.github.io/proloaf/
  • New example data added

v0.2.2

  • fixed bugs validation_ds, quantile prediction failing, hparams updating
  • updated interpreter functionalities
  • included structure for transformer, informer models
  • added smoothed pinball loss
  • updated example notebooks (includes more benchmarks)
  • created torch dataloader, tensorloader, which assembles all data prep steps and postpones jobs to torch functions rather than requiring numpy or pandas routines up front

Files

proloaf-master 2.zip

Files (5.0 MB)

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md5:2b6028457207e226259b8c9eccad3d92
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Additional details

Funding

CoordiNet – Large scale campaigns to demonstrate how TSO-DSO shall act in a coordinated manner to procure grid services in the most reliable and efficient way 824414
European Commission

References

  • G. Gürses-Tran, H. Flamme and A. Monti, "Probabilistic Load Forecasting for Day-Ahead Congestion Mitigation," 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2020, pp. 1-6, doi: 10.1109/PMAPS47429.2020.9183670.