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.

Join this session to get hands-on experience with energy forecasting and learn how easy it is to get started with openSTEF's latest release: 4.0. We will use pre built notebooks to look into training, forecasting and benchmarking (bring your own laptop with Python installed). We will explore the various model types that openSTEF has to offer, and we will understand how you can add your own model to the mix.

We will use the Liander 2024 open source data set so everyone can get started, but you are also welcome to bring your own energy data you would like to forecast.

Wether you're just starting out with energy forecasting, or whether you are already experienced with forecasting (potentially through using openSTEF 3) and want to see what's new in 4.0 this session is for you!