Trial / Demo
The global push towards a climate-neutral future has placed the building sector under an intense spotlight. Responsible for approximately 40% of energy consumption and 36% of greenhouse gas emissions in the EU, buildings are a critical frontier for decarbonisation. For decades, Energy Performance Certificates (EPCs) have been the primary tool for communicating a building's energy efficiency. However, traditional EPCs often fall short, providing a static, "as-designed" snapshot that rarely reflects the dynamic, real-world operational performance of a building over its life cycle.
To address this gap, IES is leading a Horizon Europe funded research project, SmartLivingEPC (Advanced Energy Performance Assessment towards Smart Living in Building and District Level). The project is pioneering a transformative approach with the aim to deliver a new generation of building certificates that are holistic, data-driven, and digitally native. By moving beyond simple energy metrics, SmartLivingEPC is developing a comprehensive framework that incorporates sustainability, water consumption, acoustics, and crucial human-centric variables. This new procedure leverages real data from the building’s entire life cycle, bridging the persistent gap between designed and in-use performance.
IES’s role is pivotal, focusing on the development and integration of the project's core digital and artificial intelligence (AI) components, which form the engine for this next-generation assessment.
The SmartLivingEPC Vision: Beyond a Simple Rating
SmartLivingEPC is fundamentally reimagining what a building certificate can be. Its innovation rests on two groundbreaking schemes:
1. Building-Level Assessment: A holistic evaluation of a single building that considers not just energy but a full spectrum of performance indicators.
2. District-Level Assessment: A novel scheme that assesses a building's performance within the broader context of its neighbourhood. This includes interactions with smart grids, the impact of street lighting, participation in energy communities, and the integration of electric vehicles (EVs).
This dual-level approach acknowledges that a building does not exist in a vacuum. By including citizen participation and a building’s relationship with its local environment, SmartLivingEPC is creating a methodology to certify entire energy-efficient neighbourhoods, paving the way for the future decarbonisation of our cities.
IES's Core Contribution: The Physics-Based Digital Twin
A cornerstone of IES's work within the project is the creation of physics-based Digital Twins. These are not mere 3D models but sophisticated virtual replicas of physical assets that simulate thermal processes and energy performance with unparallelled precision. By integrating building geometry (from IFC files), material properties, HVAC system specifications, and localized weather data, these digital twins establish a robust "as-designed" baseline energy model. This is crucial for accurately quantifying the performance gap between design-stage predictions and actual operational energy use.
A prime example of this work in action is the Energy Community of Leitza, Spain, one of the project's key test sites. This community consists of six different pilot buildings—including a single-family house, a school, and a sports centre—all sharing energy from a 720 m² photovoltaic (PV) installation on the sports centre's roof.
Leitza School and Sports Centre
For this site, IES developed a comprehensive community-level digital twin. This involved:
• Using its Intelligent Community Design (iCD) tool to create detailed 3D models for each of the six buildings.
• Simulating the yearly electrical consumption of each building, which was then validated against actual metered data to ensure a deviation of less than 10%.
• Utilising its Intelligent Virtual Network (iVN) tool to model the complex energy flows within the community, including PV self-consumption and surplus energy sold back to the grid.
iVN energy network model
This detailed, physics-based model of the Leitza community provides an incredibly rich dataset. It allows stakeholders to understand not only individual building performance but also the collective dynamics of a shared-energy ecosystem. This digital twin serves as the foundational layer upon which advanced AI analyses can be built in the future.
The Power of AI: Developing Added-Value Services
Beyond the Digital Twin, IES is spearheading the development of a suite of AI-enhanced tools designed to analyse real-time building data. These services move beyond raw data to provide actionable insights into building behaviour, occupant comfort, and operational efficiency. The suite includes five distinct engines:
1. Thermal Comfort Engine: This tool goes beyond simple temperature readings to predict the subjective thermal comfort of occupants (e.g., feeling 'cold', 'neutral', or 'hot'). By analysing factors like air temperature, humidity, and radiant temperature, it provides a human-centric view of comfort, enabling facility managers to create more pleasant and productive indoor environments.
2. Anomaly Detection Engine: Acting as an intelligent "check engine light" for building systems, this engine uses user-defined rules to continuously monitor data streams for unusual patterns. It can flag issues like a heater running in summer or abnormal CO2 levels, allowing maintenance engineers to proactively address faults before they lead to energy waste or equipment failure.
3. Activity Inference Engine: This engine uses unsupervised machine learning to identify and extract typical daily activity profiles from time-series data. It can learn common occupancy patterns, helping building modellers create more accurate simulations and enabling building managers to optimize HVAC and lighting schedules based on actual usage.
4. Disaggregation Engine: Perhaps one of the most innovative tools, this engine tackles the challenge of Non-Intrusive Load Monitoring (NILM). It takes a building's total aggregated energy consumption data—the kind from a single main meter—and intelligently breaks it down, estimating the energy used by different end-uses like heating, cooling, lighting, and major appliances. This provides invaluable granular insight without the need for expensive and disruptive sub-metering.
5. Cost Estimation Engine: By combining energy consumption data with definable energy tariffs and carbon emission factors, this engine calculates and forecasts energy costs. This empowers building owners and occupants with financial foresight, helping them understand the economic impact of their energy use and potential upgrades.
Innovation in Practice: Real-World Test Sites
The methodologies and tools developed by SmartLivingEPC are not theoretical. They are being rigorously tested and validated across a diverse range of European climates and building typologies. In addition to the Leitza Energy Community, the project test sites include:
• nZEB Smart House (Greece): A near-Zero Energy building used to test building-level AI and digital twin functionalities.
• Frederick University Campus (Cyprus): Representing educational buildings in a Mediterranean climate.
• Office nZEB (Lithuania): An office building providing data from a colder, Northern European climate.
These sites provide the real-world data and feedback necessary to refine the SmartLivingEPC framework, ensuring its tools are robust, reliable, and relevant for wide-scale deployment.
The Future of Energy Performance Certification
SmartLivingEPC represents a paradigm shift in how we measure, understand, and improve the performance of our buildings. By creating a dynamic, life-cycle-oriented certificate that is rich with digital intelligence, the project is providing the tools needed to accelerate the transition to a climate-neutral built environment.
IES’s central role in developing the project's foundational Digital Twins and advanced AI services underscores its position at the cutting edge of building performance technology.
Find out more about IES R&D via: https://www.iesve.com/research
