| Data driven approaches for prediction of building energy consumption at urban level G Tardioli, R Kerrigan, M Oates, OD James, D Finn Energy Procedia 78, 3378-3383, 2015 | 121 | 2015 |
| Identification of representative buildings and building groups in urban datasets using a novel pre-processing, classification, clustering and predictive modelling approach G Tardioli, R Kerrigan, M Oates, J O'Donnell, DP Finn Building and Environment 140, 90-106, 2018 | 112 | 2018 |
| A methodology for calibration of building energy models at district scale using clustering and surrogate techniques G Tardioli, A Narayan, R Kerrigan, M Oates, J O’Donnell, DP Finn Energy and Buildings 226, 110309, 2020 | 45 | 2020 |
| An innovative modelling approach based on building physics and machine learning for the prediction of indoor thermal comfort in an office building G Tardioli, R Filho, P Bernaud, D Ntimos Buildings 12 (4), 475, 2022 | 14 | 2022 |
| A data-driven modelling approach for large scale demand profiling of residential buildings G Tardioli, R Kerrigan, M Oates, J O'Donnell, D Finn Barnaby, CS, Wetter, M.(eds.). Building Simulation 2017, 2017 | 8 | 2017 |
| Filho, R.; Bernaud, P.; Ntimos, D. An Innovative Modelling Approach Based on Building Physics and Machine Learning for the Prediction of Indoor Thermal Comfort in an Office … G Tardioli Environ. Sci. Proc 11, 25, 2021 | 7 | 2021 |
| Assessment of carbon-aware flexibility measures from data centres using machine learning MS Misaghian, G Tardioli, AG Cabrera, I Salerno, D Flynn, R Kerrigan IEEE Transactions on Industry Applications 59 (1), 70-80, 2022 | 5 | 2022 |
| Applying modeling and optimization tools to existing city quarters MP Prieto, PMÁ de Uribarri, G Tardioli Urban energy systems for low-carbon cities, 333-414, 2019 | 4 | 2019 |
| A novel hybrid technique for building demand forecasting based on data-driven and urban scale simulation approaches G Tardioli, R Kerrigan, M Oates, J O'Donnell, D Finn Corrado, V., Fabrizio, E., Gasparella, A., and Patuzzi, F.(eds.). Building …, 2019 | 2 | 2019 |
| A multilevel demand response profiling and modeling solution enabled by digital twins integration C Mountzouris, S Karatzas, G Protopsaltis, J Gialelis, A Chassiakos, ... EC3 Conference 2023 4, 0-0, 2023 | | 2023 |
| Use of district energy modelling and stakeholder engagement in developing decarbonisation strategies S Pierce, L De Donatis, F Pallonetto, G Tardioli Building Simulation 2021 17, 3236-3243, 2021 | | 2021 |
| Integration of Data-driven Methods and Building Physics Modelling for Prediction of Building Energy Use in Urban Contexts G Tardioli University College Dublin, 2019 | | 2019 |
| 10.1 Energy conversion and district heating systems in Vienna1 MP Prieto, PMA de Uribarri, G Tardioli Urban Energy Systems for Low-Carbon Cities, 333, 2018 | | 2018 |
| A data-mining approach for energy behavioural analysis to ease predictive modelling for the smart city LC Tagliabue, S Rinaldi, MF Ragusini, G Tardioli, ALC Ciribini ISARC. Proceedings of the International Symposium on Automation and Robotics …, 2018 | | 2018 |
| SPECIAL ISSUE ON SMART BUILDINGS FOR SMART CITIES A González-Vidal, J Mendoza-Bernal, S Niu, AF Skarmeta, H Song, ... | | |
| A Multi-Level Digital Twin for Optimising Demand Response at the Local Level without Compromising the Well-being of Consumers N Byrne, A Chassiakos, S Karatzas, D Sweeney, V Lazari, A Karameros, ... | | |
| PREDICTION OF BUILDING ENERGY USE IN AN URBAN CASE STUDY USING DATA DRIVEN APPROACHES G Tardioli, R Kerrigan, MR Oates, J ODonnell, D Finn | | |
