Digital Twin technology for decarbonising any built environment.
Integrated analysis tools for the design & retrofit of buildings.
Create a sustainable masterplan for a city, community or campus.
Optimise building performance at an individual level or across a portfolio.
Analyse the feasibility of energy network decarbonisation strategies
A customisable range of operational dashboards, portfolio management and community engagement tools.
Exceptional room & zone loads analysis for building & HVAC design.
Predict building energy consumption, CO2 emissions, peak demands, energy cost & renewable production.
All Consultancy Projects
SunHorizon is a Research & Development project funded by the European Union’s Horizon 2020 Research and Innovation Programme. The project involves 21 partners from 11 EU countries, including IES, all collaborating together over a five year period.
The overall objective of the project, through the partnership, is to demonstrate the successful installation of innovative heat pump systems, coupled with solar thermal technologies and storage, in five technology packages in residential and commercial buildings across eight different climatic regions of Europe.
IES’ specific role is to provide the cloud monitoring platform with KPIs and maintain the digital twin accuracy at all times.
Out of all the energy used in buildings during their life-cycle, it is estimated that 80-90% is used during the operational phase. SunHorizon will prove that the efficient operation and management of the heating and cooling systems to residential and tertiary buildings can enable lower emissions, lower energy bills and less fossil fuel dependency for a domestic or commercial building.
Machine Learning (ML) algorithms on the cloud
IES R&D consultants led the development of integrated self-learning algorithms for demand and production forecasting. The self-learning algorithms are fed by monitoring information, end user feedback and simulation results, with the aim of continuously re-adapting and re-parametrising a digital twin simulation model.
The ML algorithms use the data based on the following elements:
· the behaviour of occupants, from feedback collected by a third party partner
· the Building Management IoT data collected from the demo sites
· the simulated and weather data.
The Machine Learning technology can then predict profiles in VE modelling, thanks to the occupancy profiles, and fill in the gaps in metered and weather data.
“The purpose of Machine Learning algorithms in the SunHorizon platform is two-fold: firstly, to predict the future conditions of the building, including the occupants’ comfort, and secondly, to look at the past data and fill in the gaps when sensors are offline. All the implementation and testing is performed by IES’ R&D consultants.”
Dimitrios Ntimos, IES Project Manager
ICL integration and VE modelling
IES uses robust model re-calibration methodology on the cloud, with specific components of ICL Digital Twin technology (calibrated building model), integrated with partners’ software, which controls the heat pumps and solar panels. By developing a digital twin model of the building, IES can identify operations away from the ‘performance gap’, track and predict the performance of the building over time. The captured data covers components such as IoT and heat sensors, as well as humidity levels.
The Virtual Environment (VE) is an in-depth suite of integrated analysis tools for the design and retrofit of buildings, embedding energy and performance analysis into the heart of the design process, and across the entire building lifecycle. The unique feature of this project was removing the physics-based simulation engine from the VE to run it autonomously.
The next step is integration between VE and third party simulation tools, using partners’ detailed models of real life innovative retrofit technologies installed on the demo buildings of the project, in conjunction with the VE model, to create calibrated building models. The co-simulation functionality is flexible, to allow VE to co-simulate with various third party simulation tools.
Functional monitoring platform and optimisation tool
IES creates intelligent reports and dashboards in near real-time for building users and occupants where the comfort parameters and energy consumption will be shown together, along with alerts for potential mechanical faults. IES provides the infrastructure to enable fault detection and proactive maintenance by partners.
The thermal comfort systems, built on the functional monitoring platform, aim to continuously re-adapt and re-parameterise the calibrated simulation model to ensure it reflects the actual performance of the building and determines optimal control settings for building HVAC (heating, ventilation and air-conditioning) systems.
“The key benefit of IES’ Digital Twin technology is that it integrates with many other partner technologies. IES’ role is to facilitate the integration of all the other tools onto a single platform, having collated all the data. IES coordinates the demonstration, testing, validation and fine tuning of the final platform, to enhance energy performance. The beauty of the simulation technology is that not only can highly accurate predictions be made, but also used in practice to save energy in the building.”
Dimitrios Ntimos, IES Project Manager