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.
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
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
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
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
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
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
Augmented Ensemble Calibration of lumped-parameter building models
2019; Building Simulation; C Andrade-Cabrera, WJN Turner, DP Finn
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.
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.