Alfonso Capozzoli
researcher ORCID ID = 0000-0002-0083-4983
Facts
6 properties- Instance of
- human
- Occupation
- researcher
- Employer
- Polytechnic University of Turin (2017–present)[2]
- Imported from
- wholeness_audit_2026-05-02
🏷️ Also known as
en Alfonso Capozzoli
🔖 Identifiers · 8
- ORCID iD
- 0000-0002-0083-4983
- OpenAlex ID
- A5024357919as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- DBLP author ID
- 158/7354
- Dimensions author ID
- 011752566121.19
- IRIS UNINA author ID
- 07477
- PORTO@Iris author ID
- 04006
- SBN author ID
- TO0V644870
- Scopus author ID
- 23018003200
📚 Sources · 1
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 32
A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings,
An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems,
A predictive and adaptive control strategy to optimize the management of integrated energy systems in buildings,
A scalable approach for real-world implementation of deep reinforcement learning controllers in buildings based on online transfer learning: The HiLo case study,
Bridging the gap between complexity and interpretability of a data analytics-based process for benchmarking energy performance of buildings,
Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset,
Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management,
Cooling Systems in Data Centers: State of Art and Emerging Technologies
+24 more
← Authored by 32
A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings,
An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems,
A predictive and adaptive control strategy to optimize the management of integrated energy systems in buildings,
A scalable approach for real-world implementation of deep reinforcement learning controllers in buildings based on online transfer learning: The HiLo case study,
Bridging the gap between complexity and interpretability of a data analytics-based process for benchmarking energy performance of buildings,
Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset,
Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management,
Cooling Systems in Data Centers: State of Art and Emerging Technologies
+24 more
Affiliated with 2
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