Alfonso Capozzoli

Associate professor

Politecnico di Torino

Italy

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About

Alfonso Capozzoli is an Associate Professor at Politecnico di Torino, Italy. His research focuses on advanced energy management strategies, particularly in the context of HVAC systems and renewable energy communities. Recent articles highlight his work on deep reinforcement learning for energy control, demand-side energy management, and the assessment of indoor environmental conditions. He is dedicated to the application of innovative methodologies to enhance energy efficiency in multi-zone buildings.

Articles (20)

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2025

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2021

Collaborators (10)

Antonio Rosato

Professore ordinario

Università degli Studi della Campania Luigi Vanvitelli

ITALY

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Vincenzo Moscato

ITALY

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Esther Borkowski

Technical University of Denmark

DENMARK

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Marco Savino Piscitelli

Assistant professor

Politecnico di Torino

ITALY

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Massimiliano Masullo

University of Campania “Luigi Vanvitelli”

ITALY

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Eleni Mangina

Professor & VP (International) College of Science

Dublin City University

IRELAND

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Arno Schlueter

Full professor

ETH Zürich

SWITZERLAND

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Donal P. Finn

Dublin City University

IRELAND

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Zoltan Nagy

Assistant Professor

University of Texas at Austin

UNITED STATES

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Luigi Maffei

Full Professor

Università degli Studi di Napoli Federico II

ITALY

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Alfonso Capozzoli

Research Interests

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Articles (20)

A scalable approach for real-world implementation of deep reinforcement learning controllers in buildings based on online transfer learning: The HiLo case study

A scalable demand-side energy management control strategy for large residential districts based on an attention-driven multi-agent DRL approach

Rule extraction from deep reinforcement learning controller and comparative analysis with ASHRAE control sequences for the optimal management of Heating, Ventilation, and Air Conditioning (HVAC) systems in multizone buildings

An interpretable data analytics-based energy benchmarking process for supporting retrofit decisions in large residential building stocks

Assessment of Indoor Thermo-Hygrometric Conditions and Energy Demands Associated to Filters and Dampers Faults via Experimental Tests of a Typical Air-Handling Unit During Summer and Winter in Southern Italy

Deploying deep reinforcement learning for low-level HVAC control in multi-zone buildings: A comparative study with ASHRAE G36 sequences

Scenario analysis for renewable energy communities through advanced co-simulation infrastructure

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

The role of advanced energy management strategies to operate flexibility sources in Renewable Energy Communities

Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

Data-Driven Fault Detection and Diagnosis: Research and Applications for HVAC Systems in Buildings

Bridging the gap between complexity and interpretability of a data analytics-based process for benchmarking energy performance of buildings

Faulty Operation of Coils’ and Humidifier Valves in a Typical Air-Handling Unit: Experimental Impact Assessment of Indoor Comfort and Patterns of Operating Parameters under Mediterranean Climatic Conditions

Enhancing energy management in grid-interactive buildings: A comparison among cooperative and coordinated architectures

Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context

A predictive and adaptive control strategy to optimize the management of integrated energy systems in buildings

Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings

Collaborators (10)

Antonio Rosato

Vincenzo Moscato

Esther Borkowski

Marco Savino Piscitelli

Massimiliano Masullo

Eleni Mangina

Arno Schlueter

Donal P. Finn

Zoltan Nagy

Luigi Maffei

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