LF Energy Summit Europe 2026

Detecting low-carbon technologies (LCTs) from smart meter data, and disaggregating their individual consumption profiles, is becoming increasingly important as electrification reshapes electricity systems. This interactive workshop will explore emerging approaches for technologies such as EVs, heat pumps, solar PV, and batteries from Advanced Metering Infrastructure (AMI), or smart meter, data using open and reproducible methods.

Hosted by researchers from the Centre for Net Zero, the session will combine presentations with collaborative, hands-on experimentation using open datasets and starter notebooks. Participants will explore machine learning and demand disaggregation techniques, discuss benchmarking and evaluation challenges, and consider the operational value of LCT detection for forecasting, flexibility, and network planning.

The workshop is designed to foster collaboration between researchers, industry practitioners, and open-source contributors working at the intersection of energy systems and data science. Whilst content is evolving, the focus will remain on practical experimentation, transparent methodologies, and community learning using open tools and datasets.

This hands-on tutorial introduces our end-to-end framework released through the OpenGridFM project, enabling the development and deployment of GridFM models for steady-state grid analysis.
Participants will work with notebooks to generate synthetic but realistic power-flow and optimal-power-flow datasets using gridfm-datakit, including load variation, generator-dispatch variation, topology perturbations, and configurable grid cases. They will then use gridfm-graphkit to train and evaluate GENCO, a unified graph neural solver, in a low-code environment and compare its performance against classical solvers. For advanced participants, the tutorial will also explain how to implement, integrate, and benchmark custom model architectures using the framework and the provided benchmarking datasets.
The tutorial is designed for power-system practitioners, researchers, and developers who want to contribute to OpenGridFM or apply its tools in their own workflows. No prior experience with the project is required; familiarity with Python and basic power-system concepts is helpful.

OpenSTEF 4 introduces a redesigned, modular architecture that makes it easier to build and adapt energy forecasting solutions to your specific context. Rather than prescribing a single way of to integrate OpenSTEF in your setup, version 4 gives users the flexibility to create forecasting pipelines that fit their data, workflows, and IT environment.

In this hands-on tutorial, participants will explore the new architecture and learn how to assemble forecasting workflows using OpenSTEF's modular building blocks. You'll discover how to customize data preparation and feature engineering, integrate your preferred tools, experiment with different modeling approaches, and adapt forecasting setups to your own use case.

The tutorial provides a practical introduction to the newly developed capabilities of OpenSTEF 4, including support for ensemble forecasting, benchmarking and backtesting through STEF BEAM, and integration of deep learning and foundation models. Participants will work through real examples and gain insight into how

OpenSTEF can support everything from experimentation to production-scale forecasting.

Who should attend?
Whether you are a researcher exploring new forecasting methods, a data scientist building operational models, an organization looking to scale forecasting capabilities, or whether you are already experienced with forecasting (potentially through using OpenSTEF 3), this session is for you.