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Giovanni Tardioli
Giovanni Tardioli
Research Lead IES R&D Ltd.
Bestätigte E-Mail-Adresse bei ucd.ie
Titel
Zitiert von
Zitiert von
Jahr
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
1182015
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
1072018
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
422020
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
132022
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
72021
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
72017
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
52022
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
42019
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
22019
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
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
Use of District Energy Modelling and Stakeholder Engagement in Developing Decarbonisation Strategies
S Pierce, L De Donatis, F Pallonetto, G Tardioli
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
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