Roadmap

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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.

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• 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 

KTP

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

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2022/2023:

Input Alliander:

Forecasting topics at Alliander 2022:

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