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EUI3: Application of self-learning techniques for energy systems integration in future residential heating systems

Decarbonisation of the residential thermal sector is a key pathway to meeting EU targets on carbon emissions. Future large-scale electrification of residential heating, beyond its present level, in tandem with the expansion of renewables on the electricity grid, provides one possible pathway to decarbonisation. Switching from fossil fuels to electricity is technically very challenging without a significant investment in the electricity generation and distribution network. In order to facilitate the switch, the residential sector will need to reduce its thermal demand by minimising losses, increasing system efficiencies, in tandem with increased demand flexibility.

 

​Energy integration of building energy conversion systems is one promising way to address these challenges. Buildings contain many energy systems (such as heating, ventilation, etc.) that provide a necessary service to meet occupant comfort expectations and health requirements. However, the different systems are often operated independently and in a sub-optimal way. Machine learning algorithms integrated into building controls systems, coupled with relevant environmental sensing, can be used to unify the monitoring and control of the separate systems. Moreover, actions identifying wasteful energy behaviour can be identified and with targeted efforts, be corrected. This holistic approach will allow the operation of the systems to be optimised with respect to reducing energy consumption and maximising occupant comfort and health. The use of advanced control systems will also facilitate the integration and operation of new technologies that are expected to provide increased levels of service to the power system. For example, electric vehicles, thermal energy storage and renewable energy systems (e.g., photovoltaics), more efficient heating systems (e.g., heat pump systems) combined with ICT and cloud technology, will enable demand response management measures to shift peak electricity loads to times of high renewable energy production or low system demand.

 

Members


Dr Donal Finn
Funded Investigator, Associate Professor, School of Mechanical & Materials Engineering, University College Dublin
donal.finn@ucd.ie
01 716 1947
Dr Eleni Mangina
Funded Investigator, Associate Professor, School of Computer Science, UCD
eleni.mangina@ucd.ie
+353 1 7162858
Dr Mohammad Saffari
Postdoctoral Researcher
mohammad.saffari@ucd.ie
Adamantios Bampoulas
PhD Researcher
adamantios.bampoulas@ucdconnect.ie

Research Outputs


Conference

Optimising Supply Chain Logistics System Using Data Analytics Techniques

2019; INTSYS 2019 – the 3rd EAI International Conference on Intelligent Transport Systems, Portugal; Eleni Mangina, Pranav Kashyap Narasimhan, Mohammad Saffari, and Ilias Vlachos

DOI

Conference

Controlled natural ventilation coupled with passive PCM system to improve the cooling energy performance in office buildings

2019; 16th IBPSA International Conference & Exhibition Building Simulation 2019, Italy; M. Saffari, M. Prabhakar, A. de Gracia, E. Mangina, D. P. Finn, L. F. Cabeza

Conference

Quantification and characterization of energy flexibility in the residential building sector

2019; 16th IBPSA International Conference & Exhibition Building Simulation 2019, Italy; A. Bampoulas, M. Saffari, F. Pallonetto, M. de Rosa, E. Mangina, D. P. Finn

Journal

Cool Roof Impact on Building Energy Need: The Role of Thermal Insulation with Varying Climate Conditions

2019; Energies; Cristina Piselli, Anna Laura Pisello, Mohammad Saffari, Alvaro de Gracia, Franco Cotana and Luisa F. Cabeza

DOI

Conference

Self-Learning Control Algorithms for Energy Systems Integration in the Residential Building Sector

2019; IEEE IoT World Forum on Internet of Things (IoT) 2019, Ireland; Bampoulas A, Saffari M, Pallonetto F,Mangina E, Finn D

DOI

Journal

Mapping the energy flexibility potential of single buildings equipped with optimally-controlled heat pump, gas boilers and thermal storage

2019; Sustainable Cities and Society; D'Ettorre F, De Rosa M, Conti P, Testi D, Finn D

DOI

Conference

Multi-objective optimisation of an active distribution system using normalised normal constraint method

2019; IEEE PowerTech 2019, Italy; M. Saffari, M.S. Misaghian, M. Kia, V. Vahidinasab, D. Flynn, M. Lofti, M. Shafie-khah, J.P.S. Catalão

Conference

Energy assessment of hybrid heat pump systems as a retrofit measure in residential housing stock

2019; 13th REHVA World Congress CLIMA 2019, Romania; D. Keogh, M. Saffari, M. de Rosa, D. P. Finn

DOI | OA

Journal

Augmented Ensemble Calibration of lumped-parameter building models

2019; Building Simulation; C Andrade-Cabrera, WJN Turner, DP Finn

DOI

Conference

Economic assessment of flexibility offered by an optimally controlled hybrid heat pump generator: a case study for residential building

2018; ATI 2018 - 73rd Conference of the Italian Thermal Machines Engineering Association, Italy; D'Ettorre F, de Rosa M, Conti P, Schito E, Testi D, Finn D.

DOI | OA

Conference

Next Generation Building and District Metrics to Enable Energy Systems Integration

2016; CLIMA 2016: 12th REHVA World Congress, Denmark; Beagon, P., Warren, J., Finn, D. and O'Donnell, J.

OA