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Fabio Rinaldi

Associate Professor

Politecnico di Milano

Italy

Research Interests

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Professors in Italy Professors in Energy Engineering Energy Engineering professors in Italy

About

Fabio Rinaldi is an Associate Professor at Politecnico di Milano in Italy. His research areas focus on machine learning applications for estimating energy savings in smart buildings and warehouses, as well as optimizing HVAC systems. Recent publications include studies on predictive modeling for energy efficiency, simulation of dynamic systems, and fault diagnosis in energy systems. He contributes significantly to the field of sustainable energy technologies through innovative data-driven approaches.

Articles (11)

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2026

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2020

Collaborators (4)

SARA PEROTTI

Associate Professor

Politecnico di Torino

ITALY

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Luigi Pietro Maria Colombo

Full professor

Politecnico di Milano

ITALY

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Amin Moazami

Associate Professor

Norwegian University of Science and Technology

NORWAY

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Behzad Najafi

Associate Professor

Politecnico di Milano

ITALY

View details

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Fabio Rinaldi

Research Interests

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Articles (11)

Development of a Machine Learning-Driven HVAC Load Shifting Strategy to Facilitate Balancing Electric Vehicle Charging Load in a Logistics Node

Just-in-Time Morning Ramp-Up Implementation in Warehouses Enabled by Machine Learning-Based Predictive Modelling: Estimation of Achievable Energy Saving through Simulation

Estimating morning ramp-up duration for the cooling season in a smart building using machine learning: Determining most promising features

Pressure Drop Estimation of Two-Phase Adiabatic Flows in Smooth Tubes: Development of Machine Learning-Based Pipelines

Assessing the impact of employing machine learning-based baseline load prediction pipelines with sliding-window training scheme on offered flexibility estimation for different building categories

Machine learning-based estimation of buildings' characteristics employing electrical and chilled water consumption data: Pipeline optimization

Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses

Simplified finite volume-based dynamic modeling, experimental validation, and data-driven simulation of a fire-tube hot-water boiler

Implementation of a multi-setpoint strategy for fire-tube boilers utilized in food and beverage industry: Estimating the fuel saving potential

Reduced FV modelling based on CFD database and experimental validation for the thermo-fluid dynamic simulation of flue gases in horizontal fire-tubes

Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests

Collaborators (4)

SARA PEROTTI

Luigi Pietro Maria Colombo

Amin Moazami

Behzad Najafi

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