The EU’s climate targets and the accompanying decarbonisation of the energy system must entail a deep transformation of the existing building stock. Buildings account for more than 1/3 of Europe’s total greenhouse gas emissions while 40% of Europe’s energy consumption goes to heating and powering our buildings. Raising the energy performance and intelligence of existing and future buildings is not only one of the most cost effective ways to advance defossilisation of energy demand, reduce the level of greenhouse gas emissions and support the transition to a low-carbon society but it is also an effective way to generate investment, growth and job opportunities. The European Clean Energy Package pushes for more ambitious approaches to energy efficiency propelled by coordinate research and innovation.
At the UCD Energy Institute, we focus on delivering energy modelling solutions for the built environment. The approaches leverage measured data from real residential or commercial buildings to develop mathematical models, data platforms and analytical tools to enable assessments of low carbon energy solutions and the integration of energy systems with the built environment. This work advances the areas of:
- Innovative demand response (DR) services for residential and commercial applications
- Value proposition of multi-utility consolidation to mitigate peak electrical loads
- Integration of low energy technologies (heat pumps, low temperature systems, energy storage technologies, onsite generation) into residential and commercial building stock
- Energy flexibility assessment of demand response measures in buildings
- Assessment of retrofit measures for a low carbon energy future
- Application of machine learning for improved system integration of smart energy technologies and for analysis of building and urban energy data
- New approaches to defining and manageable forms of building to city scale data.
- Novel data transformation methodologies to enable widespread usability of energy modelling tools and techniques
- Integration of improved behavioural models for demand prediction of smart energy technologies