The Dublin Energy Lab (DEL) is TU Dublin’s interdisciplinary research centre working energy efficiency and renewable energy across all industry sectors. We use our expertise in science, engineering and business to deliver new knowledge for industry, government, the public and the international research community.

Our team of approximately 90 postgraduate and academic researchers work on the development of knowledge, devices and systems, business models and policies in the following areas:


This includes research on: renewables and network unbalance; anti-islanding and distributed generation; grid-tie inverters; intelligent electronic transformers; transmission network constraints; network integration of microgeneration technologies; power quality monitoring and analysis; and smart grid technology platforms.

Research activities include the novel designs for solar energy concentration using PV; incorporation of PV into the current state of the art devices to add autonomous power functionality; building integrated photovoltaic applications; and feasibility studies of PV and Irish industry for Government based organisations and private companies.

Research in this field includes:

  • Wind Turbine
  • Farm and Portfolio Performance Assessments
  • Techno-Economic Modelling
  • System Integration
  • Hybrid Analysis
  • Forecast Assessment
  • Wind Resource Device Development

DEL carries out research at both the individual building and building stock scale. Activity at the individual building scale encompasses demand-side and supply-side sustainable energy design strategy, fabric, system, construction, and operational issues. This work is supported by physical testing & monitoring and computer modeling tools. Activity at the building stock scale focuses on energy profiling, technology deployment, policy, and environmental and socio-economic aspects.

Widespread deployment of intermittent renewables and the high cost of storage make energy forecasting critical to the management of large-scale energy systems. Research here focuses on the use of methods such as time-series, inferential statistical, and machine learning techniques to probabilistically forecast future energy system states, and extend these to assessing economic and emissions impacts.

Improving the environmental performance of one part of an energy system may increase impacts elsewhere. Life cycle assessment (LCA) techniques can be used to optimise whole systems and interrelated systems. LCA includes: applying LCA methods to novel systems and devices; the development of new methodologies and assessment frameworks; and the integration of stochastic processes.