NARROWING THE ENERGY PERFORMANCE GAP IN BUILDING ENERGY ASSESSMENT RATINGS

A central tenet of the Energy Performance of Building Directive (EPBD) is to accurately inform; (i) Building owners, to create a bottom-up consumer-driven demand market for energy efficient buildings, thereby accelerating reduction of energy consumed by European building stocks, and (ii) policy makers, leading to effective top-down interventions.

The directive mandates comparable Energy Performance Certificates (EPCs) for buildings constructed sold or leased across the European Union (EU).

EPCs have become the foremost source of information on energy use in EU building stocks and are a crucial instrument of the EPBD. However, an acknowledged barrier to realising the ambitions of accurately informing the building owner and policy, is the energy performance gap between the EPC rated and actual building energy consumption.

The PREDICT project, which launched mid-2024, funded by the Sustainable Energy Authority of Ireland (SEAI), aims to address this energy performance gap.

‘PREDICT’ stands for PRedicting Energy using Dynamic Indicators in a Calibrated Tool. The means of addressing the energy performance gap between theoretical rated energy and actual energy is through the creation of an adaptive in-use factor (IUF) tool to predict occupied building energy use for both commercial buildings and residential buildings.

Inherent in all EPC methodologies are trade-offs between reproducibility, accuracy, assessor expertise and costs. Since input data is often based on worst-case default and standardised operating conditions; the results outputted by EPC methodologies can only offer an estimation of the actual building energy consumption. This level of standardisation, common across EPC methodologies, allows for a degree of consistency in building assessments but a lack of specificity for individual buildings. Indeed, there can be a major gap between theoretical and actual measured energy consumed in buildings when occupied by real people.

In the project, the relative influence of building energy use parameters will be established by EPC calculation, by use of dynamic simulation models of representative archetype digital twins, informed by measured field data over a weather year. Using parametric analysis, the stochastic influence of occupancy types and profiles will be quantified. The resultant data will be used to create the IUF tool

This project will use large datasets from SEAI projects to define and validate the IUF based on monitored data, surveys, interviews, and calibrated physics models. The development of an in-use factor will enable the provision of a personalised roadmap for homeowners on how to upgrade their home leading to better informed costs and more realistic payback periods on investment.

EPCs and resultant EPC databases are also used as inputs to national stock models leading to climate policy, meaning that an improved EPC scheme will lead to better, more targeted, policy. In turn, this can feed into standards and guidance for retrofit by identifying key energy performance factors while quantifying the affect of behaviour on energy use and energy ratings. Better information also supports the estimation of required exchequer funding for retrofit and applications for funding from Europe.

The current, and first, phase of the project is the development of a consolidated open access database on Irish housing which will be published in the coming months here: https://zenodo.org/communities/predict/about. This will be based initially on comprehensive monitored data acquired by existing and past projects led by research team members.  Through analysis of the information within distinct databases, PREDICT will generate best practice protocols for the generation of such data in future projects, issuing guidance on how future research projects can follow a standardised best-practice approach.

PREDICT is a 3-year Project funded by the SEAI National Energy Research Development and Demonstration (RD&D) fund grant number 23/RDD/1046.

It is led by TU Dublin partnering with the University of Galway, IES R&D, Tyndall, and collaborating with DCSix Technologies and the Irish Green Building Council as well as multiple other partners through the Commerical Steering Committee including IN2 Engineering, The Office of Public Works, and Building Design Partnership (BDP).

Follow updates on the project on LinkedIn:  https://www.linkedin.com/company/seai-predict/