Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review

• Type: Another Publication in an International Scientific Journal
• Date: 2025-06-02

Authors

Bruno Palley (Author) FEUP; João Poças Martins (Author) FEUP; Hermano Bernardo (Author) Other; Rosaldo J. F. Rossetti (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Urban Science

Serial No. 7 No. 12

Pages: 202-202

Publisher: MDPI AG

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-018-WGJ

DOI: 10.3390/urbansci9060202

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific