Associate Professor, Laboratory Director, and Head of System Optimization Section

Research Area

Multi-objective Optimization of Energy Systems, Evolutionary Optimization, Fuel Cell based Systems

fabio.rinaldi@polimi.it

Assistant Professor(RTDb) and Head of the Data Science Section

Research Area

Data-driven Building simulation, Energy Data Analytics, Physical modelling/long-term performance optimization of energy systems

behzad.najafi@polimi.it

Full Professor in Machine Learning and Big Data at HVL Norway and Key Research Collaborator

Research Area

Distributed Control, Data Analytics, Cyber-Physical Systems Resilience

Reza.Arghandeh.Jouneghani@hvl.no

Assistant Professor (Lecturer) at Concordia University (CA) and Key Research Collaborator

Research Area

Indoor Environment Modelling, Indoor Air Quality, Indoor Air Purification, Smart Buildings, Machine learning

alireza.haghighatmamaghani@uwaterloo.ca

Alec Shirazi, PhD

Senior Data Scientist, PhD in Mech. Eng. from UNSW

Research Area

Smart Buildings, Data Analytics, ML-based Predictive Modelling, System-level Simulation/Optimization of Applied Energy Systems

a.shirazi@protonmail.ch

Marco Tognoli

PhD Student

Research Area

Machine learning based modelling of district heating systes - Dynamic simulation of industrial boilers, model predictive control

marco.tognoli@mail.polimi.it

Farzad Dadras Javan

PhD Student

Research Area

Machine learning based simulation of indoor thermal behaviour - energy flexibility in smart buildings

farzad.dadrdas@polimi.it

Farhang Raymand

Research Assistant

Research Area

Machine learning based building characteristics, Smart Meter Analytics

farhang.raymand@mail.polimi.it

David Garrido Salgado

Research Assistant

Research Area

Smart ramp-up implementation in smart buildings; machine learning based thermal process modelling

nan

Hamed Khatam

Research Assistant

Research Area

Machine learning based estimation of occupancy status and indoor air quality

hamed.khatam@mail.polimi.it

Dimitrios Papadopoulos

Research Assistant

Research Area

Data reproduction in smart buildings, optimization of machine learning-based pipelines

dimitrios.papadopoulos@mail.polimi.it

Rafael Silva Ferrato

Research Assistant

Research Area

Thermal comfort estimation in smart buildings, Application of machine learning for user behavior classification

rafael.silva@mail.polimi.it

Italo A. Campodonico

Current Position

PhD Student at NTNU (NO)

M.Sc. Thesis Title

Development of IoT-driven Machine learning based pipelines for predictive modelling of ramp-up and ramp-down processes in indoor environments aiming at energy efficiency enhancement

Shayan Keivanmajd

Current Position

PhD Student at TU Eindhoven (NL)

M.Sc. Thesis Title

Intervention Assessment and Predictive Modeling of Boiler’s Behavior in a Smart Building Employing Machine Learning

Paolo Bonomi

Current Position

Associate in Intelligent Automation - PWC (IT)

M.Sc. Thesis Title

Machine learning based fault diagnosis and performance estimation of automotive PEM fuel cells through optimal EIS tests

Keivan Ardam

Current Position

AI HVAC Specialist at BrainBox AI (CA)

M.Sc. Thesis Title

Application of machine learning in frictional pressure drop estimation of two-phase flow : a dimensionless approach

Sadaf Moaveninejad,PhD

Current Position

Postdoctoral Researcher at Free University of Padova (IT)

M.Sc. Thesis Title

Book Chapter: Data Analytics for Energy Disaggregation: Methods and Applications

Pedro Obando Vega,PhD

Current Position

Senior CFD Engineer at Buildwind (BE)

M.Sc. Thesis Title

Numerical simulation of an incineration power plant employing OpenFoam

Andrej Hanušovský

Current Position

Data Scientist at Makers (SL)

M.Sc. Thesis Title

Reproducible machine-learning physical-based models for pressure drop estimation in two phase diabatic and adiabatic flows

Farshad Bolourchifard

Current Position

Dev Lead, AI HVAC Services at BrainBox AI (CA)

M.Sc. Thesis Title

Application of deep learning in thermal load forecasting and data-driven supply optimization of a district heating network

Arian Maghsoudnia

Current Position

Data scientist at Blockbax (NL)

M.Sc. Thesis Title

Deep learning-based pipelines for occupancy driven smart ramp-up implementation in commerical buildings

Manoj Manivannan

Current Position

Data Analyst/Engineer at Infovista (IT)

M.Sc. Thesis Title

Machine learning based short-term prediction of air-conditioning loads through smart meter analytics

Pegah Mottaghizadeh, PhD

Current Position

Energy Products at Tesla (US)

M.Sc. Thesis Title

Process system modeling of reversible solid oxide cell (rSOC) energy storage system

Mehrdad Biglarbeigian,PhD

Current Position

Senior Reliability Enigneer at Tesla (US)

M.Sc. Thesis Title

Design, implementation and evaluation of cooperative methods for dual demand side management

Monica Depalo

Current Position

Business Analyst at A2A Energia (IT)

M.Sc. Thesis Title

Machine learning based estimation of commercial buildings characteristics : determining the most influential temporal features

Arun Shaju

Current Position

Seniro Associate Manager at Alliant Group (IND)

M.Sc. Thesis Title

Machine learning based building characteristics and performance estimation through analyzing consumption profiles

Shayan Milani

Current Position

Thermal Research Engineer at Lightyear (NL)

M.Sc. Thesis Title

Machine learning based heat transfer estimation of evaporating and condensing R134a flow in micro-finned tubes : determination of the most promising dimensionless feature set

Debayan Paul

Current Position

PDeng Student at TU Eindhoven (NL)

M.Sc. Thesis Title

Benchmarking Methodologies for Solar Irradiance estimation on Vertical planes aiming at Improved Critical Design Analysis of a Glazed Building Facade

Giordano Bruno Zannini

Current Position

Project Developer at SunPrime (IT)

M.Sc. Thesis Title

Machine learning based estimation and measurement anomaly detection of NOx emissions in natural gas fired boilers

Ramiro Criach

Current Position

Energy Analyst at N-side (BE)

M.Sc. Thesis Title

Optimization of Machine Learning-based Pipelines for Indoor Temperature Forecasting in a Set-point Management Strategy for a Smart Building's Cooling System

Giulia Moret

Current Position

Junior Data Scientist at Edison (IT)

M.Sc. Thesis Title

Determination of the Most Promising Feature Sets in Machine Learning based Pipelines for Predictive Thermal Behaviour Modelling of Indoor Spaces

Alessandro Benetti

Current Position

Data Scientist at Prometeia (IT)

M.Sc. Thesis Title

Flow regime estimation in two-phase flows employing machine learning : investigation of the most promising dimensionless feature set

Ratomir Dimikj

Current Position

Product Engineer at LeafTech GMBH (DE)

M.Sc. Thesis Title

Incremental machine-learning based load prediction of a university campus aiming at performance improvement on day-ahead market and reduction of losses

Nicolas Fernando Marrugo

Current Position

Data Scientist at SIRAM (IT)

M.Sc. Thesis Title

Deep learning based occupancy prediction and HVAC behavior modeling for improving energy efficiency of commercial buildings

Enoch Nuamah Appiah

Current Position

Energy Analyst at Acquirente Unico (IT)

M.Sc. Thesis Title

Development of optimal machine learning based pipelines for predicting the dynamic thermal behavior of indoor environments

Danish Ahmad Mir

Current Position

MEP EngineerMEP Engineer at MS Constructions (IND)

M.Sc. Thesis Title

Development of optimal machine learning based pipelines for predicting the dynamic thermal behavior of indoor environments

Michela Silva

Current Position

Proposal Engineer at Siram (IT)

M.Sc. Thesis Title

Machine learning based consumption prediction and hourly optimization of heating system for a hospital complex

Luca Di Narzo

Current Position

Data Scientist at ACEA (IT)

M.Sc. Thesis Title

Machine learning based estimation of air-conditioning loads through temporal features extraction using smart meter data

Farzad Moghaddampour

Current Position

Technical software programmer at Erowa Technology, Inc (IT)

M.Sc. Thesis Title

Feasibility analysis of renewable energy systems for rural electrification in different climatic zones of Peru

Lorenzo Benevento

Current Position

Project Manager at Siemens (IT)

M.Sc. Thesis Title

Energy auditing and proposing energy saving measures for an Italian SME through building energy simulation

Lidia Premoli vilà

Current Position

Project Specialist E-Mobility at A2A (IT)

M.Sc. Thesis Title

Bottom-up modelling of Italian residential sector: Development, validation, and application in evaluation of decarbonisation scenarios

Darko Micev

Current Position

Power distribution engineer - EVN Distribution System Operator (MK)

M.Sc. Thesis Title

Incremental machine-learning based load prediction of a university campus aiming at performance improvement on day-ahead market and reduction of losses

Farshad hasanabadi

Current Position

PDeng Student at TU Eindhoven (NL)

M.Sc. Thesis Title

A Machine Learning based Approach for PV Self-consumption Enhancement in an NZEB Using a Geothermal Heat Pump Driven Heating System

Giovanni D. Temporelli

Current Position

Energy Design Engineer Edilclima s.r.l (IT)

M.Sc. Thesis Title

Data-driven dynamic modelling and implementation of an improved control strategy for a geothermal heat pump based heating system in a nearly zero energy building

Matteo Magni

Current Position

Technical Expert at Energy Power Technology SRL (IT)

M.Sc. Thesis Title

Modelling a steam reforming reactor utilized in PEM fuel cell based micro-generation System

Amin Solouki

Current Position

PhD Candidate at Ecole Polytechnique de Montreal (CA)

M.Sc. Thesis Title

Modeling infrastructure and implementation of state-of-the-art heat transfer models for plate fin heat exchangers

Dario Bertani

Current Position

Project Engineer at GSB Consulting (IT)

M.Sc. Thesis Title

Analysis of hybird off grid renewable electrification system for the case study of Val Codera

Veronica Galli

Current Position

Senior Procurement Engineer at Mandelli (IT)

M.Sc. Thesis Title

energy and exergy analysis of a waste to energy plant and its R1 energy recovery efficiency evaluation

Socio-economic Clustering of Resdiential Buildings through smart-meter analytics

Status:On-going

In the first phase of this project, building physics based filters are employed in order to convert the temporal characteristics of electrical consumption time-series into features. In the second phase, these features alongwith the socio-economic characteristics of residential-buildings are employed as the training dataset of machine learning algorithms. Several machine learning algorithms are employed in order to classify the residential building based on the given consumption profile and the one with the highest accuracy while predicting the test data-set is chosen.

Short-term/Medium-term prediction of Air-conditioning loads through energy disaggregation

Status:On-going

This Project is focused on short-term prediction of air-conditioning (AC) load of residential buildings using the data obtained from a conventional smart meter. The AC load, at each time step, is separated from smart meter aggregate consumption through energy disaggregation methodology. The obtained air-conditioning load and the corresponding historical weather data are then employed as input features for the prediction procedure. In the prediction step, different machine learning algorithms, including Artificial Neural Networks, Support Vector Machines, and Random Forests, are used in order to conduct hour-ahead and day-ahead predictions

Machine Learning based Prediction of Pressure-drop in Two Phase Flows

Status:On-going

In the firt phase of the project the pressure drop of a two-phase adiabatic flow at a wide range of operating conditions is measured. Next, physical phenomena based pre-processing procedure is carried out, in which the operating conditions of each test are converted into related dimensionless parameters. In the last phase, utilizing the obtained dataset, different machine-learning (ML) algorithms are trained in order to predict the pressure drop (target) being provided the dimensionless parameters as input features. The accuracy of the algorithms are then evaluated through cross validation and the most accurate algorithm is determined. The developed ML based model will eventually be provided public access as an open-source tool.

Data-driven Real-time Fault Diagnosis of PEM Fuel Cells Through Electrichemical Impendance Spectroscopy

Status:On-going

In this project the results of Electrochemical Impedance Spectroscopy (EIS) are utilized in order to train machine learning algorithms which are later employed for rapid and robust diagnosis of faults within PEM fuel cells. In the first phase of the project, the EIS data of the cell at various operating conditions which represent the common faults are measured. Next,a parat of the obtained data is utilized as the training dataset and machine learning (ML) algorithms are accordingly trained using the corrresponding EIS data as inputs and the type of the fault as output. Next, the trained machine learning model is tested with the remaining faulty EIS data (test Dataset) in order to evaluates its fault classification accuracy.

Data-driven Short-term/Meidum-term prediction of electrical demand and heating consumption of commercial buildings

Status:On-going

In this project, hour ahead, day ahead preditions of both heating and electrical consumption of commercial buildings are conducted employing the available dataset for a large set of commercial buildings. Measured smart meter and heating management system data alongwith corresponding climatic data obtained from external resources are utilized as the training dataset. Combinations of machine learning algorithms alongwith LSTM methodology are evalauted in order to determine the most accurate algorithm with the least possible computation cost.

Dynamic modeling and optimal sizing of fire-tube boilers for varous demand profiles

Status:On-going

In this project, detailed dynamic model of an industrial fire-tube boiler is first developed and five different geometrical configurations, each of which corresponds to a boiler model, are considered. Next, a PID controller is implemented and tuned for each configuration aiming at controlling the steam pressure, while addressing a demand with a variable flow rate. The operation of the developed boiler models, while providing four different steam demand profiles, are next simulated. The resulting cumulative average efficiency along with the cumulative pressure deviations and minimum and maximum pressure levels, which are achieved in each simulation, are then determined. The obtained results provides practical information regarding the trade-off between the size of the boiler and its corresponding performance and controllability.

Developing a Big Data Analytics Tool for Large-scale Building Consumption Prediction using Spark

Status:On-going

In this project, in the context of hadoop eco-system, building energy consumption tools, which facilitate performing the operations in parallel on multiple nodes in a fault tolerant, manner, are developed. The developed tools provide the possibility of applying time-series prediction algorithms on a large set of building in order to predict the consumption of a specific district. Hadoop Distributed Files System (HDFS) is employed in order to store data on multiple nodes while Spark is utilized in order to automizing the mapping and reducing processes on multiple nodes. State of the art Machine learning algorithms implemented in python are instead used in order to implement time-series prediction.

Developing Optimization Algorithms over IoT based Building Management Systems for Commercial Buildings

Status:On-going

In this project, starting from related open-source platforms, an IoT based building management system is first developed which facilitates integration of various commercial building facilities. State-of-the-art Machine learning algorithms are employed to predict heating/cooling and electrical demand of the building in the next hour using it consumption history and weather predictions. Next, optimal operating conditions of the Air-conditioning units are determined and applied employing the developed IoT based BMS system. Similar methodologies are also employed for controlling Domestic hot water generation units and lighting systems.

Dynamic Modelling, Experimental Validation and Thermo-economic Analysis of Industrial Fire-tube Boilers with Stagnation Point Reverse Flow Combustor

Status:On-going

In this project, a detailed dynamic model of fire-tube boilers with Stagnation Point Reverse Flow type combustor is developed. The data obtained through an experimental testing procedure is then employed in order to validate the developed model at various operating conditions. Next, a PID controller is implemented and tuned in order to control the steam pressure while supplying steam with a variable flow rate. In the second part, a set of daily vapour request profiles is considered and the overall efficiency of the boilers with different sizes, while addressing the considered demand profiles, is obtained. The smallest boiler, which can provide the demand with an acceptable efficiency, is then determined. A similar procedure is carried out for determining the optimal size as investment venture of the boiler while considering several different daily load profiles.

StatisticalAnalysis of accelerated life test data for useful life prediction of residential boilers

Status:terminated

The useful life of residential boilers at various test-benches while undergoing different levels of increased stresses is first measured. Next, life distributions of boilers at different stress levels are determined. Finally, utilizing state-of-the-art statistical methods for analysing accelerated life test data, the characteristic useful life of boilers while operating at normal conditions is predicted. The implemented method facilitates predicting the life of products in a notably shorter time and consequently with significantly lower operating cost and test bench invesment.