Fabiano Pallonetto

Professor

National University of Ireland, Maynooth

Ireland

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Fabiano Pallonetto is a Professor at the National University of Ireland, Maynooth, specializing in energy management and optimization in urban environments. His recent research encompasses energy flexibility, building energy optimization, and decarbonization strategies, with a focus on integrating innovative technologies such as deep learning and advanced materials. He actively contributes to the discourse on sustainable practices in the building and transportation sectors.

Fabiano Pallonetto

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Articles (17)

Comprehensive examination of thermal energy storage through advanced phase change material integration for optimized building energy management and thermal comfort

Energy flexibility and management software in building clusters: A comprehensive review

A hybrid Gaussian process-integrated deep learning model for retrofitted building energy optimization in smart city ecosystems

Cutting-edge developments in active and passive photovoltaic cooling for reduced temperature operation

A micro-credential approach for life-long learning in the urban renewable energy sector

District energy modelling for decarbonisation strategies development—The case of a University campus

Unveiling the Potential Pattern Representation of RNA 5-Methyluridine Modification Sites Through a Novel Feature Fusion Model Leveraging Convolutional Neural Network and Tetranucleotide Composition

A Bayesian deep-learning framework for assessing the energy flexibility of residential buildings with multicomponent energy systems

Technologies and Strategies to Support Energy Transition in Urban Building and Transportation Sectors

Enhancing the Efficiency of Electric Vehicles Charging Stations Based on Novel Fuzzy Integer Linear Programming

Diversification, concentration and renewability of the energy supply in the European Union

An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems

Forecast electricity demand in commercial building with machine learning models to enable demand response programs

Spine Toolbox: A flexible open-source workflow management system with scenario and data management

Environmental and economic benefits of building retrofit measures for the residential sector by utilizing sensor data and advanced calibrated models

A fundamental unified framework to quantify and characterise energy flexibility of residential buildings with multiple electrical and thermal energy systems

Impact of intelligent control algorithms on demand response flexibility and thermal comfort in a smart grid ready residential building

Collaborators (13)

Mohammad Saffari

Henrik Barth

Muhammad Waseem

John G. Breslin

Carlos Vargas-Salgado

subhasis thakur

Iasonas Kouveliotis Lysikatos

Eleni Mangina

Shahid Hussain

Patricia Pereira da Silva

Mattia De Rosa

Donal P. Finn

Lorenzo De Donatis

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