MEALTWIN-H: smart data-driven digital twin for hospitality energy management

Extended abstract 1-057-26

This presentation addresses the challenge of reducing the environmental footprint of meal preparation (e.g., breakfast, lunch, or dinner) through the development of a Digital Twin (DT) for the University of Reading’s Park Eat restaurant.  Park Eat has partnered with Measurable.Energy Ltd to deploy smart plug sockets with traffic-light carbon intensity indicators, based on the UK National Grid’s carbon intensity API. These provide real-time and forecasted emissions data; however, they reflect grid-level intensity and do not capture appliance-level or meal-specific carbon impacts.

To estimate the carbon footprint of meals, appliance-level electricity data must be integrated with ingredient composition, preparation methods, portion sizes, sales, and food waste data. A key challenge is translating this complex data into meaningful, actionable insights for kitchen staff to support sustained behavioural change.

The proposed DT integrates these data streams into a unified, interactive platform, enabling real-time simulation of kitchen operations and visualisation of energy use and carbon emissions at the meal level. It supports scenario analysis, allowing staff to explore alternative preparation methods, ingredient choices, and operational strategies with immediate feedback.

The presentation objectives are to: (1) identify user needs for DT adoption; (2) present a brief way to quantify energy use per meal and (3) link energy consumption with carbon. emissions.

This work bridges research and practice, promoting sustainable catering and supporting knowledge exchange with industry stakeholders to advance low-carbon foodservice systems.

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