Written response to the Department for Energy Security and Net Zero call for evidence: Data for AI in the energy system

Abstract

This evidence is submitted on behalf of a multi-disciplinary University of Southampton team from the EPSRC-funded Programme Grant on Future Electric Vehicle Energy networks supporting Renewables (FEVER, EP/W005883/1) project, which is developing grid-independent EV charging stations, and the Citizen-Centric AI Systems Turing AI Acceleration Fellowship, which investigates building artificial intelligence (AI) systems that can be trusted by all users. The evidence highlights the lack of appliance level energy data as a key barrier to AI enabled applications across residential electricity networks and proposes a privacy preserving, standardised approach for sharing appliance or device level data via smart meters.

Artificial Intelligence (AI) has significant potential to improve the efficiency, affordability, and resilience of the UK energy system. However, realising this potential, particularly in residential and low voltage electricity networks, is currently constrained by the lack of high quality, appliance level energy data. While smart meters provide half hourly aggregate household consumption, they do not offer visibility into the behaviour of individual high impact devices such as Electric Vehicle (EV) chargers, heat pumps, batteries, and electric heating systems.

In the absence of standardised appliance level data, stakeholders rely on either indirect inference, modelling, and estimation approaches - or - on fragmented, proprietary datasets controlled by individual device manufacturers. These approaches introduce uncertainty and increase computational and integration costs, whilst limiting the scalability of AI applications. This, in turn, limits the effectiveness of demand side flexibility, local energy system optimisation and energy management, in addition to low voltage network management, whilst increasing reliance on centralised data collection that raises privacy and security concerns.

This submission proposes an open, standardised protocol that allows smart devices to share appliance-level energy data directly with the smart meter. The smart meter would act as a trusted local data hub - generating, storing, and processing data locally by default, with sharing only happening when the user consents. This approach offers several key benefits: it avoids vendor lock-in at both the data and platform level, improves data quality at source, and supports privacy preserving AI by keeping data local and minimising unnecessary export. It also complements existing government initiatives to improve visibility of distributed energy assets.

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Identifiers

ORCID for Fariba Dehghan: orcid.org/0009-0002-0319-7905

ORCID for Jan Buermann: orcid.org/0000-0002-4981-6137

ORCID for Sebastian Stein: orcid.org/0000-0003-2858-8857

ORCID for Andrew Cruden: orcid.org/0000-0003-3236-2535

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