Research Information: Sustainable Energy and Building Energy Engineering 


The Department of Process, Energy and Transport Engineering currently has active postgraduate research in a range of energy related areas. Most of the research is industry based with a focus on addressing real world problems in areas such as;- process and energy systems optimisation, energy reduction through demand side management, smart building performance and emerging technologies, micro grid management, and low carbon vehicles. 

Research has been conducted with a wide range of commercial partners, and academic partners both in Ireland and abroad. 

Funding partners include ; IRC, Teagasc, IOTI and CIT/RISAM.

The research applications include the following fields:

  • Renewable Energy Technologies
  • Building Energy Technologies, Nearly Zero Energy Buildings (NZEB), Smart Buildings, and Low Energy Ventilation.
  • Micro-grid Systems
  • Smart Grid Management
  • Milk Processing
  • Clean Rooms
  • Low Carbon Vehicles 

Overview of energy research projects


Cleanroom air flow & HVAC energy optimization

This project involves the analysis of the variables influencing particle deposition fraction in cleanrooms using computational simulation and scaled modelling for use in optimization of the airflow regime to control particle concentration levels. The project aim is to develop scientific evidence supporting a reduction in ventilation rates below existing FDA guideline values and developing correlations between cleanroom cleanliness requirements, airflow design and personnel gowning regimes. Practical implications for this research are optimization of the correct balance between personnel gowning costs and HVAC system operation for multi-national biotech, pharma and microelectronics industries.


Stock Aggregation Model and Scalable Low Energy Retrofit solutions for LAH

This project involves the development of a stock aggregation model and multi criteria sensitivity analysis study for local authority housing as a decision support tool for delivering scalable solutions to the neighbourhood level energy efficiency challenges. It is based on a case study of 11,000 homes in the cork area and uses modelling techniques to investigate the “best fit” envelope retrofit solution to a set of virtual building archetypes. The planned outcome of this research is an experimentally tested scalable retrofit solution at neighbourhood level (i.e. terrace level external retrofit techniques). The work is based in the Department of Architecture and is being undertaken in conjunction with Cork City Council. Results will be specific to Cork area but possible to extrapolate to other local area councils.


Climate cooling potential of single-sided ventilation techniques in low energy retrofit

This project involves experimental investigation and velocity propagating zonal flow modelling of single-sided ventilation in a low energy retrofit testbed. The project is focused specifically on climatic cooling potential of untreated outdoor air and quantifying the cooling effect of an unsteady flow single sided ventilation strategy. It assesses dominates mechanical and gravity forces acting on the envelope ventilation system and looks at improving existing single-sided modelling correlations coefficients. It uses the real live environment for testing and experimental work and modelling and simulation is based on emulation based simulation engine TRNSYS environment. The research should result in improved design and operation of single sided ventilation strategies as scalable retrofit solutions in high thermal mass structures. Work is based at zero2020. The project should result in an enhanced ventilation module design and control strategy that can be embedded into retrofit projects.


IEA-EBC Annex 62 – Ventilative Cooling 2014 - 2017

AERG are participants on an International Energy Agency research program from 2014 – 2017. 16 Countries are participating through approximately 25 Universities & Private organizations. The annex is focused on the development of new knowledge in the area of Ventilative Cooling. This is the application (distribution in time and space) of ventilation flow rates to reduce or even eliminate cooling loads. The air driving force can be natural, mechanical or a combination. The Annex will focus on analysis and development of ventilative cooling solutions from the perspective of utilization of the cooling and thermal perception potential of outdoor air. The Annex will address the impact of elevated air velocities on thermal comfort due to increased ventilation flow rates. The Annex will address the interaction of ventilative cooling with other measures to improve thermal comfort and cool buildings like the use of thermal mass, solar shading, desk and ceiling fans, water based cooling systems, etc.


Milk production forecasting techniques.

Forecasting milk production in pasture based systems is an inherently difficult undertaking. Accurate prediction of milk yield is greatly beneficial for optimization of dairy production on the producer level (farmers) and the processing level (cooperatives). Precise profiling of future milk production allows the farmer to predict energy consumption and equipment utilization levels. Processers require milk production forecasts on a macro level for plant optimization. Existing modeling techniques cater for stall fed cattle as the majority of the world’s milk is produced in this fashion. However, no compressive modeling technique currently exists for completely pasture based countries (Ireland, France & New Zealand). The agri-foods sector is Ireland’s largest industry and biggest indigenous exporter with 85% of all milk produced in Ireland exported to foreign markets. The aim of this research is to reduce to cost of producing milk by accurately forecasting the production and processing chain using novel modeling techniques. Even a modest reduction in cost per litre of milk produced would have a dramatic effect on the competitiveness and success of the Irish dairy industry.


High renewable generation penetration in a smart building with real time electricity pricing.

This body of research focuses on the integration of renewable energy sources with a smart building.  Facilitation of intermittent renewable energy sources for domestic and commercial dwellings has become a research area of increasing importance as of late. Operating strategies to balance building thermal and electrical demand with power production levels have been developed for island systems and users connected to a conventional power grid. A new key area of interest within this field is the integration of renewables power sources with a low energy consumption smart building connected to the smart grid. The introduction of smart buildings and the smart grid pose new challenges. A smart building reacts to the dynamic effects of varying occupancy levels, external climatic conditions and also the comfort needs of the dwellers. A smart grid exposes the building to real time electricity pricing from the national grid. The objective of this research will be to develop a control and optimisation strategy for a working low energy consumption smart building with integrated renewable energy supply on a smart grid. The outputs of the research will have wide range ramifications to current smart building energy control systems and for future smart grid optimisation.


Implementation of practical demand side management optimisation algorithms through human-machine symbiosis. 

There has been a recent spate of published research in the area of simulation and optimisation of micro grids and distributed generation in the built environment. As sophisticated as these methods might be; they do not take into the account the preferences or comfort of the human inhabitants of the space. In the majority of cases the occupant is simply modelled as an upper and lower temperature threshold or a cycle demand profile. Little or no attention has been paid to the practical ramifications of implementing an optimisation algorithm to a living/work space. This research sets out to investigate the practical aspects of optimising the energy consumption of a working environment (Zero2020).


Precision energy management on pasture based dairy farms. 

Reducing electricity consumption along with associated costs and environmental impacts through the integration of advanced sensing and load prediction algorithms with Demand Side Management (DSM) technologies (technologies that alter the farms energy consumption trends in order to minimise costs in a specific electricity pricing environment) in Irish milk production will become an important topic in the future for two reasons. First, the introduction of a dynamic electricity pricing system, with peak and off-peak prices, will be a reality for 80% of electricity consumers by 2020. If farmers carry out their evening milking during the peak period, energy costs may increase, which would impact farm profitability. Second, herd size and milking parlour infrastructure will become larger and more energy intensive in the future if farmers respond to national policy frameworks and are encouraged to increase output by the abolition of European Union milk quotas. This project will develop a suite of models to i) forecast energy demand using remote sensor networks and trending algorithms ii) compute dynamic energy costs and C02 output per litre of milk produced in multiple electricity tariff scenarios iii) automatically provide optimised feedback information to the farmer relating to the performance of the dairy farm infrastructure by employing demand side management (energy storage and load shifting) techniques iv) advise on the feasibility of specified renewable energy technologies for Irish dairy farms from technical and economic viewpoints. This project will serve to establish a new specialization within the area of precision agriculture which focuses on optimising resource-use based on predictive modelling from data collected from a specific production system. It is envisaged that this novel approach could be applied to all types of milk production systems, not just grazing systems, and even to other food production systems in the future such a pig or poultry production where energy costs represent an even higher proportion of the production costs.


Control and optimisation of thermal load-shifting dairy farms. 

In recent years there has been growing interest in Demand Side Management (DSM) strategies. Increased penetration of intermittent power sources such as wind may pose serious problems for national grid management in Ireland . Time Of Use (TOU) and Real Time Pricing (RTP) of electricity has long been proposed as a method of reducing the Peak to average load ratio and optimising consumer power consumption. While the proliferation of plug in electric vehicles and smart energy storage systems will create opportunities to capitalize on potentially cheap electricity prices during certain periods, it may also become problematic for residential load control management. To facilitate the ever increasing use of renewable energy in the power grid and the adoption of intelligent DSM techniques, new control practices for a smart grid have been explored. It is becoming apparent that the control of smart energy storage systems and appliances will play a major role in the evolution of the smart grid. Control schemes that schedule the operation of domestic appliances have the potential for large cost savings in RTP environments. The aim of this study was to develop a control strategy that optimises the charging of ice storage for milk cooling in a dynamic electricity pricing environment. The system used in this study was an external melt ice bank (IB). Due to the lack of accurate weather forecast information an ambient air temperature forecast model that utilises bi-daily predictions from the European Centre for Medium-range Weather Forecasts was developed. A Neural Network model of the IB was trained to simulate the charging characteristics of the IB under varying ambient temperature and varying states of charge. A multi-combination calculator was used to compute possible ice building trajectories within a given horizon. The paths were mapped in a three dimensional space with three axes (charge, cost and operation times). Dynamic programming was used to find the optimum path. Once the optimum path was found the ice building schedule was set to that particular trajectory. As new data becomes available after every time slot, the optimum trajectory is re-calculated and the operating schedule for the IB is reset. This optimisation algorithm was trialled in multiple applications. Firstly it was used to assess the cost saving potential under the current TOU pricing structure in Ireland and under a RTP structure based on the System Marginal Price of electricity on the Irish wholesale market. Secondly, the optimisation algorithm was used to shift the electrical demand to a time when the load on conventional power sources in the nation grid was at its lowest (wind power production data was used to calculate the load on conventional power sources). Energy consumption and carbon emissions from the charging of the IB were also simulated. It was found that applying the optimisation algorithm in a RTP scenario reduced the energy consumption and carbon emissions while yielding higher cost savings in comparison to the TOU structure. The optimisation algorithm was capable of reducing and shifting the electrical load to the optimal time when the load conventional power sources was lowest. The system developed in this study may be used for isolated demand side management applications, but also for macro level grid management.


Vehicle Energy Models for Estimation of Driving Range and Emissions Reduction from Alternatively Fuelled Vehicles

A vehicle energy model that allows potential customers to estimate the driving range of new low carbon vehicle drive train technologies under individual (customised) real world conditions is the principle objective of this research proposal. Existing vehicle modelling software is based on evaluating the performance of the Internal Combustion Engines (ICE) and very few models of electric drive train models exist. With ICE models, the primary focus is on improving the low efficiency of the drive train and the impact of auxiliary loads such as HVAC is not considered. In  low carbon vehicles such as Electric Vehicles (EV), Hybrid Electric Vehicles (HEV), Plug-in Hybrid Electric Vehicles (PHEV), Fuel Cell Electric Vehicles (FCEV) the drive train is highly efficient but energy storage is severely limited by battery capacity. Auxiliary loads such as HVAC and the type of driving style etc. will impact the driving range that these vehicles can achieve without recharging. The methodology in this research is to further develop the simplified energy models to incorporate real world conditions. This will involve re-engineering HVAC models for vehicles to take account of local ambient temperature and humidity conditions at any location in the world and to estimate the energy usage for this auxiliary load. The impact of driving style on energy consumption with an electric drive train will also be modelled. Finally, the effect of route elevations on vehicle energy regeneration will be modelled. The developed models will then be validated using test vehicles operated over selected routes.The ultimate goal is to offer potential buyers of these new vehicles an accurate range (minimum, nominal, maximum) that can be achieved on battery only power, under all possible ambient conditions and with a range of driving styles.


Dynamic convection coefficient modelling in well ventilated interstitial air layers for retrofitted buildings

A building’s façade is the primary moderator of dynamic heat transfer from outside to inside. Ventilated facades are double skin structures with continuous vertical air cavities behind the external skin. Warmed rising air within these vertical air cavities, facilitates a reduction of heat transfer through to the internal environment thereby reducing the building cooling load. Open jointed ventilated façades are a variation on the ventilated façade concept. They are a double skin structure but the external skin is constructed from a series of small panels, each separated by a gap, thereby forming individual, ventilated cells. Discontinuities in the ventilated air gap due to structural support of these panels, coupled with the facility to draw air in or vent air out around the perimeter of each panel results in a very complicated air flow pattern. To date very little research has been carried out on this form of façade and existing commercial building simulation software packages do not facilitate selection of appropriate convection coefficients to model the external skin under dynamic solar loads.   It is proposed to use computational fluid dynamic (CFD) software to model the complex thermodynamic and fluid-dynamic nature of convection currents behind these panels and to assess and optimise the parameters which characterise the dynamic convection coefficients. The ZERO2020 building has been constructed using an open joint ventilated façade and will provide the physical model to validate the CFD model.


The utilisation of Load Shifting to optimise the non-dispatchable contribution to the overall energy requirements of the Nimbus Centre

The primary objective of this thesis was to develop a multiple input multiple output (MIMO) predictive control algorithm to optimise the non-dispatchable contribution of a micro-grid to the overall energy requirements of NIMBUS, a Cork Institute of Technology (CIT) campus building, through the utilisation of "load shifting". In terms of electrical energy, load shifting can be described as the procedure of moving an energy demand to coincide with the predicted power output from renewable energy sources. Energy management in a smart-grid environment will involve making new economic choices based on the variable cost of electricity, the ability to shift loads, and the ability to produce and store energy. This study proposal espouses a 'real world' micro-grid (μG)  system comprising of a 10kW Bergey wind turbine, a 65kW co-generation or combined heat and power (CHP) unit, a 20kWh battery power storage unit, a 1500L thermal storage unit and the National Grid.  It is proposed to develop a control algorithm for the 'real world' μG that utilises predictions of the future building energy demand and predictions of the contribution of non-dispatchable energy sources, in order to optimise the energy contribution of non-dispatchable sources in a Real Time Pricing (RTP) environment. This thesis outlines the validated models established to derive the inputs to the MIMO control algorithm in addition to an overview of the algorithm itself.



Building Energy Systems Sustainable Energy and Building Services Engineering Research Test Beds.


Zero2020 - Building


Building Services and Building Energy Systems Research - ZERO 2020 Research Building

Cork Institute of Technology has completed the design and planning and implementation of an ambitious low energy retrofit project of a section of the original 1974 building on its main campus in Bishopstown.

The Net Zero Energy Retrofit 2020 Testbed project has upgraded approximately 290 sq metres of the existing building with a view to achieving net zero energy by 2020. A net zero energy building  produces as much energy as it uses in a year. The methology is based on minimising consumption and supplementing the balance with renewable energy.

The finished space will housse both the Centre for Advanced Manufacturing and Management Systems (CAMMS) and the research staff from the Department of Process, Energy and Transportation Engineering), both groups with significant external interactions.

The project which attracted  significant funding from the Department of Education and skills started construction in December 2011.

The ‘Mission’ for the project is, ‘to provide a live, controlled test bed environment to explore energy and resource performance through the use of pioneering technological solutions with emphasis on demonstrating zero energy public sector building in use operation’.

The Zero2020 test-bed currently employs:

•    High performance thermal insulation.
•    Passive cooling and ventilation systems.
•    Active Energy Management (BMS) system.
•    Dedicated wireless data logging and control system 
•    Full energy use metering and monitoring.
•    Standalone air source heating system.
•    Smart Energy Lighting Solutions.


Contacts : Fergus Delaney and Paul O'Sullivan  - Building Services Engineering and Building Energy Systems.

Email : and



Zero2020 - Micro-grid


The objective of this project is to create a whole-building energy and power management technology demonstrator within the Zer2020 building scalable to a district or campus level. The location of such a resource within CIT has lead to curriculum development, enhanced teaching capability and tools, postgraduate-level research, and the potential for commercialisation of advanced technologies. The test bed has been designed from the outset to be a strategic resource for the Institute and has been seen as a platform for CIT from which it can develop new, industry focused research, which in turn informs new curricula across the Faculty of Science and Engineering.  Key research areas are as follows:

  • Building demand-side energy management.
  • Building supply-side energy management.
  • Building energy control systems.
  • Microgrid control and optimisation.
  • Energy storage and load shifting.



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