Marco Savino Piscitelli

Assistant professor

Politecnico di Torino

Italy

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About

Marco Savino Piscitelli is an Assistant Professor at Politecnico di Torino, Italy. His research areas include energy management systems, HVAC systems, data analytics for energy benchmarking, and building thermal dynamics. He has contributed to several recent articles focusing on the use of Python libraries for RDF graphs, deep reinforcement learning for HVAC control, and data-driven fault detection in building energy systems.

Articles (14)

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2025

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2025

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2021

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2021

Collaborators (3)

Antonio Rosato

Professore ordinario

Università degli Studi della Campania Luigi Vanvitelli

ITALY

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Alfonso Capozzoli

Associate professor

Politecnico di Torino

ITALY

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Vincenzo Moscato

ITALY

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Marco Savino Piscitelli

Research Interests

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Articles (14)

BrickLLM: A Python library for generating Brick-compliant RDF graphs using LLMs

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

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

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

A holistic time series-based energy benchmarking framework for applications in large stocks of 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

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

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

Coordinated energy management for a cluster of buildings through deep reinforcement learning

Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings

A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

Collaborators (3)

Antonio Rosato

Alfonso Capozzoli

Vincenzo Moscato

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