Bruno Palley
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Bruno Palley has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Hermano Bernardo
INESC TEC—Institute for Systems and Computer Engineering, Technology and Science
2 shared publications - João Poças Martins
University of Porto
2 shared publications - Rosaldo J. F. Rossetti
University of Porto
2 shared publications - Maria Teresa Restivo
University of Porto
1 shared publication - Paulo Abreu
University of Porto
1 shared publication - Teresa Stingl Freitas
Construct- LFC, Faculty of Engineering University of Porto, Porto, Portugal
1 shared publication
Latest Publications
- 26 June 2025
- Published by Springer Nature
- p. 101-112
Forecasting Power Demand in Complex Buildings Using Machine Learning: A Shopping Center Case Study
- 26 June 2025
- Published by Springer Nature
- p. 101-112
Forecasting Power Demand in Complex Buildings Using Machine Learning: A Shopping Center Case Study
- Top Cited PapersOpen Access
- 2 June 2025
- Published by MDPI AG in Urban Science
- Vol. 9 (6) , 202
Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review
Artificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have been applied in several fields. In this context, combining Digital Twins, Machine Learning, and Smart Buildings offers significant potential to improve energy efficiency and operational effectiveness in building management. This review aims to identify and analyze studies that explore the application of Machine Learning and Digital Twins for operation and energy management in Smart Buildings, providing an updated perspective on these rapidly evolving topics. The methodology follows the PRISMA guidelines for systematic reviews, using Scopus and Web of Science databases. This review identifies the main concepts, objectives, and trends emerging from the literature. Furthermore, the findings confirm the recent growth in research combining Machine Learning and Digital Twins for building management, revealing diverse approaches, tools, methods, and challenges. Finally, this paper highlights existing research gaps and outlines opportunities for future investigation.
- 2 June 2025
- Published by MDPI AG in Urban Science
- Vol. 9 (6) , 202
Top Cited PapersOpen AccessIntegrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review
Artificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have been applied in several fields. In this context, combining Digital Twins, Machine Learning, and Smart Buildings offers significant potential to improve energy efficiency and operational effectiveness in building management. This review aims to identify and analyze studies that explore the application of Machine Learning and Digital Twins for operation and energy management in Smart Buildings, providing an updated perspective on these rapidly evolving topics. The methodology follows the PRISMA guidelines for systematic reviews, using Scopus and Web of Science databases. This review identifies the main concepts, objectives, and trends emerging from the literature. Furthermore, the findings confirm the recent growth in research combining Machine Learning and Digital Twins for building management, revealing diverse approaches, tools, methods, and challenges. Finally, this paper highlights existing research gaps and outlines opportunities for future investigation.