The MU International Office is the first point of contact for international students applying for full-degree, Erasmus, Study Abroad, and Summer School programmes, and supports MU students who wish to study abroad.
I have a wide experience in the energy, IT and transport sectors working as an entrepreneur, data scientist and researcher across different industries and disciplines. My academic background in Computer Science and Engineering and my industry experience in Energy, Sustainable Mobility, Data Analytics, Control Algorithms and Optimisation makes me a pivotal asset for research based and commercial data centric projects. My goal is bridging academic research to industrial needs applying my multidisciplinary industry and entrepreneurship experience to build solutions and solve technical challenges for the deployment and integration of the smart grid and its associated carbon footprint reduction and promoting Sustainable Development Goals.
Next Generation Energy Systems
By 2027, NexSys will have identified credible, just, and accelerated pathways for a net zero energy system,
and have developed technologies and talent needed for the energy transition. In NexSys, we seek to collaborate as a multi-disciplinary team to provide technology development and evidence-based insights to the energy transition, through multi-dimensional research and outreach that is focused on equity for energy citizens and practical solutions for industry.
FLOW - Flexible Energy Systems by leveraging the optimal integration of EVs and their users
Principal Investigator and WP Leader
FLOW tests, validates and enhances user-centric V2X smart charging solutions and their orchestrated integration into energy grids that deliver flexibility assets to favour additional penetration of renewables and alleviate energy grid challenges.
RES4CITY - Renewable Energies System for Cities
A carbon-neutral European Union by 2050 is an ambitious target that presents technical and societal challenges. While technical research is advancing rapidly to reach a net-zero target, the overall skillsets of the working force are lagging behind in understanding, employing and mastering sustainable technologies for the business models. Therefore, the RES4City project envisions a codesigned educational framework for upskilling the workforce to integrate, influence and combine the current sustainability best practices for renewable energies and fuel technologies.
Pallonetto, Fabiano (2023) 'Towards a More Sustainable Mobility' In: Handbook of Computational Social Science for Policy. : Springer International Publishing.
Fabiano Pallonetto (2022) 'Advanced Energy Management Systems and Demand-Side Measures for Buildings towards the Decarbonisation of Our Society' In: Transitioning to Affordable and Clean Energy. Switzerland : MDPI. [Link] https://doi.org/10.3390/books978-3-03897-777-3-6
Peer Reviewed Journal
Bampoulas A.; Pallonetto F.; Mangina E.; Finn D.P. (2022) 'An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems'. Applied Energy, 315 . [DOI]
De Rosa M.; Gainsford K.; Pallonetto F.; Finn D.P. (2022) 'Diversification, concentration and renewability of the energy supply in the European Union'. Energy, 253 . [DOI]
Pallonetto F.; De Rosa M.; Finn D.P. (2021) 'Impact of intelligent control algorithms on demand response flexibility and thermal comfort in a smart grid ready residential building'. Smart Energy, 2 . [DOI][Full-Text]
Pallonetto F.; Jin C.; Mangina E. (2022) 'Forecast electricity demand in commercial building with machine learning models to enable demand response programs'. Energy And Ai, 7 . [DOI][Full-Text]
Kiviluoma J.; Pallonetto F.; Marin M.; Savolainen P.T.; Soininen A.; Vennström P.; Rinne E.; Huang J.; Kouveliotis-Lysikatos I.; Ihlemann M.; Delarue E.; O'Dwyer C.; O'Donnel T.; Amelin M.; Söder L.; Dillon J. (2022) 'Spine Toolbox: A flexible open-source workflow management system with scenario and data management'. Softwarex, 17 . [DOI][Full-Text]
Bampoulas A.; Saffari M.; Pallonetto F.; Mangina E.; Finn D.P. (2021) 'A fundamental unified framework to quantify and characterise energy flexibility of residential buildings with multiple electrical and thermal energy systems'. Applied Energy, 282 . [DOI][Full-Text]
Pallonetto F.; De Rosa M.; Finn D.P. (2020) 'Environmental and economic benefits of building retrofit measures for the residential sector by utilizing sensor data and advanced calibrated models'. Advances in Building Energy Research, . [DOI][Full-Text]
Pallonetto F.; De Rosa M.; D'Ettorre F.; Finn D.P. (2020) 'On the assessment and control optimisation of demand response programs in residential buildings'. Renewable and Sustainable Energy Reviews, 127 . [DOI][Full-Text]
Pallonetto F.; Galvani M.; Torti A.; Vantini S. (2020) 'A framework for analysis and expansion of public charging infrastructure under fast penetration of electric vehicles'. World Electric Vehicle Journal, 11 (1). [DOI][Full-Text]
Pallonetto F.; Mangina E.; Milano F.; Finn D. (2019) 'SimApi, a smartgrid co-simulation software platform for benchmarking building control algorithms'. Softwarex, 9 :271-281. [DOI][Full-Text]
Pallonetto F.; De Rosa M.; Milano F.; Finn D. (2019) 'Demand response algorithms for smart-grid ready residential buildings using machine learning models'. Applied Energy, 239 :1265-1282. [DOI][Full-Text]
Egan J.; Finn D.; Deogene Soares P.; Rocha Baumann V.; Aghamolaei R.; Beagon P.; Neu O.; Pallonetto F.; O'Donnell J. (2018) 'Definition of a useful minimal-set of accurately-specified input data for Building Energy Performance Simulation'. Energy and Buildings, 165 :172-183. [DOI][Full-Text]
de Oliveira da Costa P.; Mauceri S.; Carroll P.; Pallonetto F. (2018) 'A Genetic Algorithm for a Green Vehicle Routing Problem'. Electronic Notes in Discrete Mathematics, 64 :65-74. [DOI][Full-Text]
Pallonetto, F; Oxizidis, S; Milano, F; Finn, D (2016) 'The effect of time-of-use tariffs on the demand response flexibility of an all-electric smart-grid-ready dwelling'. Energy and Buildings, 128 :56-67. [DOI][Full-Text]
Bampoulas, A.; Pallonetto, F.; Mangina, E.; Finn, D.P. (2023) 'A Bayesian deep-learning framework for assessing the energy flexibility of residential buildings with multicomponent energy systems'. Applied Energy, 348 . [Link][DOI]
De Rosa, M.; Bianco, V.; Barth, H.; Pereira da Silva, P.; Vargas Salgado, C.; Pallonetto, F. (2023) 'Technologies and Strategies to Support Energy Transition in Urban Building and Transportation Sectors'. Energies, 16 . [Link][DOI]
Li, H.; Johra, H.; de Andrade Pereira, F.; Hong, T.; Le Dréau, J.; Maturo, A.; Wei, M.; Liu, Y.; Saberi-Derakhtenjani, A.; Nagy, Z.; Marszal-Pomianowska, A.; Finn, D.; Miyata, S.; Kaspar, K.; Nweye, K.; O'Neill, Z.; Pallonetto, F.; Dong, B. (2023) 'Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives'. Applied Energy, 343 . [Link][DOI]
Hussain, S.; Irshad, R.R.; Pallonetto, F.; Jan, Q.; Shukla, S.; Thakur, S.; Breslin, J.G.; Marzband, M.; Kim, Y.S.; Rathore, M.A.; El-Sayed, H. (2023) 'Enhancing the Efficiency of Electric Vehicles Charging Stations Based on Novel Fuzzy Integer Linear Programming'. IEEE Transactions on Intelligent Transportation Systems, . [Link][DOI]
Meer A.V.D.; Rigoni V.; Pallonetto F.; Keane A. (2022) 2022 11th International Conference on Power Science and Engineering, ICPSE 2022 Impact Analysis of Electric Vehicle Charging and Demand Response Potential on a University Campus[DOI]
Adamantios Bampoulas, Fabiano Pallonetto, Eleni Mangina, Donal P. Finn (2021) Building Simulation 2021 Conference A machine learning-based methodology for harnessing the energy flexibility potential of residential buildings Bruges, Belgium, 01/09/2021- 03/09/2021 [Link]
Susan Pierce, Lorenzo De Donatis, Fabiano Pallonetto, Giovanni Tardioli (2021) Building Simulation 2021 Conference Use of district energy modelling and stakeholder engagement in developing decarbonisation strategies Bruges, Belgium, 01/09/2021- 03/09/2021 [Link]
Bampoulas A.; Saffari M.; Pallonetto F.; Mangina E.; Finn D.P. (2019) IEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings Self-Learning Control Algorithms for Energy Systems Integration in the Residential Building Sector[DOI][Full-Text]
Pallonetto F.; Mangina E.; Finn D.; Wang F.; Wang A. (2014) BuildSys 2014 - Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings A RestFUL API to control a energy plus smart grid-ready residential building[DOI][Full-Text]
Pierce, S.; De Donatis, L.; Pallonetto, F.; Tardioli, G. (2022) Building Simulation Conference Proceedings Use of District Energy Modelling and Stakeholder Engagement in Developing Decarbonisation Strategies[Link][DOI]
De Lira, V.M.; Pallonetto, F.; Gabrielli, L.; Renso, C. (2022) Predicting Vehicles Parking Behaviour for EV Recharge Optimization[Link]
(2019) 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) Self-Learning Control Algorithms for Energy Systems Integration in the Residential Building Sector[DOI]
Neu, O.; Oxizidis, S.; Flynn, D.; Pallonetto, F.; Finn, D.; (2013) Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association High resolution space - Time data: Methodology for residential building simulation modelling
Pallonetto, F.; Oxizidis, S.; Duignan, R.; Neu, O.; Finn, D.; (2013) Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association Demand response optimisation of all-electric residential buildings in a dynamic grid environment: Irish case study
Fabiano Pallonetto (2021) ICPEA IEEE The 4th International Conference on Power and Energy Applications Pusan, South Korea, .