Neighbourhood Scale Digital Twin

Neighbourhood Scale Digital Twin

Innovation Type: Software (Model)

What is this innovation about:

The DigiTwinN aims to help with performance checks, optimization, monitoring, and control of a neighbourhood, and can be applied during various stages of the neighbourhoods’ lifetime: design, construction, commissioning, and use phase. It leads to better use of RES and lower peak loads and avoid congestion of the electricity grid with model predictive control on building level.

What kind of challenges it will solve if deployed widely (and why this innovation creates new value or capture value in a new way)
The application of digital twins to enable energy efficiency and flexibility at the neighbourhood level is new.
Occupant behaviour is shown to be a major factor in the energy demand of a building. Therefore, it is essential that the consequences of behaviour on Energy use can be predicted by the model. Consequently, the prediction model will adapt itself continuously to reflect these changes.

Data from sensors, energy meters, PV production and equipment is derived and collected in the syn.ikia Cloud hub and enriched with weather data from a local weather station and a weather forecast for control. The DigiTwin N Neighbourhood is calibrated with this time series data. This updated DigiTwin N calculates an ensemble of prediction based on different control action for heating and domestic hot water. The goal of the controller is to maximize the use of PV production in the neighbourhood and choose the control action which contributes the most to this goal.

The following users will find this innovation useful:

• ESCO offering services to provide heat and domestic hot water and electricity for the household.
• Service providers for energy communities, companies offering energy services for neighbourhoods
• Maintenance and building services companies
• Housing corporation, building owners (Housing corporation are active in digitalization of their building stock, the DigiTwin can add value in performance assessment and optimalisation.)
• Aggregator, offering flexibility services for congestion & balancing
• Smart building systems and building control
• Project developers
• (Local) government
• Building users
• Energy coaches

Contact person:
Wouter Borsboom, TNO

For more information, please see:
D3.4: Guidelines for realizing energy flexibility (M24)
D4.1: Grey box models of the demonstration cases (M27)
D4.5: Operational neighbourhood’ models to control and optimise the operation of the HVAC systems and the overall energy flow (M48)