Ph.D. candidate in Energetica , 38th cycle (2022-2025)
Department of Energy (DENERG)
Profile
PhD
Research topic
Development of advanced algorithms for optimal control of multi-energy energy hubs through data analytics, energy modelling and co-simulation techniques
Tutors
Research interests
Biography
From May 2022, he is enrolled in the PhD programme at the Department of Energy of Politecnico di Torino with the thesis "Advanced control strategies for energy Management of energy hub and building integrated microgrids". His research focuses on developing advanced control algorithms based on Reinforcement Learning and Model Predictive Control for optimal energy management of Energy Hubs and multi-energy systems, integrating data analytics, energy modeling, and co-simulation methodologies for control strategy validation and optimization.
Publications
Latest publications View all publications in Porto@Iris
- Piscitelli, Marco Savino; Razzano, Giuseppe; Buscemi, Giacomo; Capozzoli, Alfonso (2025)
An interpretable data analytics-based energy benchmarking process for supporting retrofit decisions in large residential building stocks. In: ENERGY AND BUILDINGS, vol. 328. ISSN 0378-7788
Contributo su Rivista - Nweye, Kingsley; Kaspar, Kathryn; Buscemi, Giacomo; Fonseca, Tiago; Pinto, Giuseppe; ... (2024)
CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities. In: JOURNAL OF BUILDING PERFORMANCE SIMULATION. ISSN 1940-1493
Contributo su Rivista - Kaspar, Kathryn; Nweye, Kingsley; Buscemi, Giacomo; Capozzoli, Alfonso; Nagy, Zoltan; ... (2024)
Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn. In: ENERGY AND BUILDINGS, vol. 324. ISSN 0378-7788
Contributo su Rivista - Nweye, Kingsley; Kaspar, Kathryn; Buscemi, Giacomo; Pinto, Giuseppe; Li, Han; Hong, ... (2023)
A framework for the design of representative neighborhoods for energy flexibility assessment in CityLearn. In: 18th Conference of International Building Performance Simulation Association, Shangai (China), 4-6 September, pp. 1814-1821. ISSN 2522-2708
Contributo in Atti di Convegno (Proceeding) - Nweye, Kingsley; Kaspar, Kathryn; Buscemi, Giacomo; Pinto, Giuseppe; Li, Han; Hong, ... (2023)
CityLearn v2: An OpenAI Gym environment for demand response control benchmarking in grid-interactive communities. In: 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2023, Istanbul (TUR), 2023, pp. 274-275
Contributo in Atti di Convegno (Proceeding)