Imagine having a SimCity-style digital map of a real neighborhood that shows exactly where energy is being used and wasted in real-time. It lets city planners test 'what-if' scenarios, like adding solar panels or changing traffic flow, to see the result before spending any money. It's basically a flight simulator for urban energy management.
Urban planners lack a way to predict how energy changes affect a whole district without expensive trial-and-error. Current tools often fail to integrate real-time data from buildings, transport, and energy grids into one view.
A Digital Twin platform consisting of a design-platform for 'what-if' simulations and a run-time engine for real-time energy optimization. It includes a data backbone using Kafka and ThingsBoard for IoT telemetry.
If you are a city authority dealing with high carbon emissions in urban areas — this project developed a design-platform that allows you to simulate planning actions to reach climate neutrality. This ensures evidence-based decision-making before physical implementation.
If you are an energy utility dealing with inefficient energy distribution — this project developed a run-time engine that optimizes energy utilization efficiency. It monitors real-time energy flows to reduce waste across a district.
If you are a tech firm dealing with fragmented urban data — this project developed a modular open architecture and a data backbone using ThingsBoard and Kafka. This allows for the seamless integration of sensor networks and building management systems.
Based on available project data, specific pricing or cost details are not provided; however, the project aims to provide cost-effective modeling tools for planners.
Yes, the project provides practical guidelines, reusable models, and algorithms specifically designed to help other cities replicate the Digital Twin in their own districts.
Based on available project data, the system follows a modular open architecture, but specific licensing terms are not detailed in the provided text.
The project includes a BMS Integration component and a Data Spaces Integration Suite using NGSI-LD energy data models to connect with existing building management systems.
The project period runs from 2024-01-01 to 2026-12-31, with current work focused on system architecture and data management.
The consortium is well-balanced for technology transfer, featuring 19 partners across 10 countries. With a 37% industry ratio (7 companies, including 4 SMEs), there is a strong commercial pull to ensure the Digital Twin is practical. The mix of 6 universities and 2 research centers provides the necessary theoretical depth for the energy modeling algorithms.
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