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OpenSynth is a new, global open community designed to democratize synthetic data, to accelerate the decarbonization of global energy systems.

Access to smart meter data is essential to rapid and successful energy transitions. Researchers, modelers and policymakers need to understand how energy demand profiles are changing, in a system that requires greater real time optimization of demand and supply on the grid. Yet current global energy modeling and policymaking is still largely based on static and highly aggregated data from the past – when energy flowed in one direction, consumer profiles were relatively predictable, and power generation was highly controllable.

Yet access to demand data is highly restrictive, as a result of privacy protections. Rather than joining industry calls to unlock raw smart meter data through existing mechanisms, by challenging current data regulations and smart meter legislation, OpenSynth believes generating synthetic data is the fastest way to achieve widespread, global access to smart meter datasets.

OpenSynth is building a community for holders of raw smart meter (i.e. demand) data to generate and share synthetic data and models that can be used by researchers, industry innovators and policy-makers.

The smart meter data that will be shared through OpenSynth won’t only be synthetic, but will contain important metadata such as property type, EPC rating and low carbon technology (LCT) ownership, including heat pumps, electric vehicles and batteries. This will enable better understanding of behind-the-meter changes and inform the ongoing development of future demand profiles for different demographics. 

The initial focus for the community includes:

  • Defining what comprises ‘good’ synthetic data (to include how synthetic data in energy might be evaluated for common concepts such as privacy, fidelity and utility)
  • Developing an open repository for synthetic smart meter data and algorithms
  • Encouraging community members to contribute data by using our initial algorithms

OpenSynth Videos

This video was recorded at the Open Sustainability Policy Summit in Washington, DC. It was presented by Gareth Jones and Gus Chadney from the Centre for Net Zero.

Abstract: Access to raw smart meter data is essential to a rapid and successful energy transition. Yet little of this data is available for research and modelling purposes, largely because of consumer privacy protections. Synthetic data can solve this problem.

We want to liberate global access to smart meter data for research and innovation purposes, by creating an open community of synthetic smart meter data contributors and users.

Centre for Net Zero is already engaged in work that advances these ambitions. Our generative AI model, Faraday, outputs synthetic half-hourly consumption data for specific household archetypes. These are adapted for user-specified inputs, such as low carbon technology, property type and season, allowing users to simulate the entire distribution of load profiles of a bespoke population. The model is designed to help grid operators, innovators and researchers understand the impacts of electrification, and optimise the design of the future energy system.

In this session, we will be exploring what “good” looks like for synthetic smart meter data, as well as some interesting real-life and proposed use cases for the data.

The Open Sustainability Policy Summit was hosted May 2-3, 2024 by the Johns Hopkins University Whiting School of Engineering. 22:22

This video was recorded at the Open Sustainability Policy Summit in Washington, DC. It was presented by Gareth Jones and Gus Chadney from the Centre for Net Zero.

Abstract: Access to raw smart meter data is essential to a rapid and successful energy transition. Yet little of this data is available for research and modelling purposes, largely because of consumer privacy protections. Synthetic data can solve this problem.

We want to liberate global access to smart meter data for research and innovation purposes, by creating an open community of synthetic smart meter data contributors and users.

Centre for Net Zero is already engaged in work that advances these ambitions. Our generative AI model, Faraday, outputs synthetic half-hourly consumption data for specific household archetypes. These are adapted for user-specified inputs, such as low carbon technology, property type and season, allowing users to simulate the entire distribution of load profiles of a bespoke population. The model is designed to help grid operators, innovators and researchers understand the impacts of electrification, and optimise the design of the future energy system.

In this session, we will be exploring what “good” looks like for synthetic smart meter data, as well as some interesting real-life and proposed use cases for the data.

The Open Sustainability Policy Summit was hosted May 2-3, 2024 by the Johns Hopkins University Whiting School of Engineering.

YouTube Video UExLeUZmMUo5WGtwc2lBb2kxNE5tZEVSb0ZwT2k3SXJsaC4yODlGNEE0NkRGMEEzMEQy

OSPS 2024 - OpenSynth - An open source community for synthetic energy data

May 29, 2024 4:04 pm

Recent OpenSynth News

Project Special Interest Group: Data Standards & Tooling

Project Lifecycle Stage: Sandbox

OpenSynth was contributed to LF Energy in 2024 by the Centre for Net Zero.