OpenSTEF Roadmap

Roadmap 2025

Vision

Our open-source initiative aims to develop a cutting-edge machine learning pipelines for short-term energy load forecasting on the grid. These pipelines are designed for seamless adoption, boasting exceptional quality and harnessing built-in expert knowledge. The aim is to make OpenSTEF the industry standard for short term energy forecasting.

Fostering an active community, our project encourages collaborative efforts, fostering continuous improvement of the code base through contributions from multiple stakeholders.

Unique selling points

  • Easy adoptability
    • Automated pipelines make is very easy to train a model and make a forecast
    • The example notebooks provide an easy way to get familiar with the model
  • Simple interface
    • Extendable
    • Explainable
  • Proven/benchmarked high forecasting quality
    • Easily benchmarked by backtest
    • Implemented at companies such as Alliander and RTE
  • Build-in expert knowledge on energy systems
    • Close connection to academic knowledge (latest scientific innovation)
    • Feature engineering
    • Energy splitting
  • Fully open-source
    • Community of experts
  • Flexibility to be able to implement in any enterprise environment
  • Scalability
  • Data standard on how to handle forecast (standardization)

Plans community 

TO update before TSC of sept 2nd 

Alliander - KTP team

RTE

RTE-i

Sigholm


Previous roadmaps 

2024:

Alliander - KTP team

  • Improve forecast quality
  • Dagster
    • MLOPs architecture
      • May influence the reference implementation
      • Add maybe second reference implementation
        • Larger scale implementation
        • Add to description
  • Improved DAZLs model (Q1)
  • Update example notebooks

Firan

  • Improve forecasting quality of peaks, with a very short time horizon

RTE

  • Predict wind power on OpenSTEF.
    • Predict every hour for 72 hours.
    • 2500 windfarms.
  • Implement new machine learning model

RTE-i

  • Create a complete POC for demonstration purposes (Q1/Q2)
  • Present a webinar on OpenSTEF (22nd of March, Q1)

Sigholm

  • Deploy OpenSTEF on Sigholm cloud environment
  • If requirements are met, switch to OpenSTEF as main forecasting tool.
  • Demo to Sigholm customers
  • Integrate with other Sigholm products.

Shell

  • Implementation OpenSTEF-dbc realtime


Planned milestones

Year

Q

Milestone

Company

Kind

2024

1

Promote OpenSTEF at FOSDEM

Alliander

Outreach

2024

1

OpenSTEF workshop

Alliander

Outreach

2024

1

Present webinar on OpenSTEF

RTE-i

Outreach

2024

1/2

Complete POC for demonstration purposes

RTE-i

New

2024

1/2

Improve forecasting quality

Alliander

Improvement

2024

2

Provide benchmark of OpenSTEF on relevant usecase compared to other forecasting providers


New

2024

2

Promote OpenSTEF at CIRED (incl. Methodology paper)

Alliander

Outreach

2024

3

LF energy summit


Outreach



2022/2023:

Input Alliander:

Forecasting topics at Alliander 2022:

  • Scale up ‘Contingencies’; locations with active capacity management

  • T-prognoses customers; Including those in our load/generation forecasts

  • Forecasting load at non-measured secondary substations

  • Forecasting Reactive power (with few measurements)

Milestones related directly to OpenSTEF: (version 2022-02-21):

Year

Q

Milestone

Kind

2022

1

Finish OpenSTEF LFE Intake

Outreach

2022

1

Remove Openstef-dbc from Openstef

Improvement

2022

2

Forecasting API - together with SOGNO

FastAPI wrapper around OpenSTEF

New

2022

2/3

Promote OpenSTEF @ LFE

  • project interview video

  • host webinar

  • promote at conference

Outreach

2022

3

Backtest / Predictability Analyses

New

2022

3

Ensemble forecasts - Automated optimized combination of independent forecasts / forecasting algorithms

Improvement




Input RTE: