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.

Several technological, social and organizational factors influence energy management in school buildings, resulting in a complex situation away from the usual engineering approach. The selection of evaluation criteria to assess the energy performance of school buildings remains one of the most challenging aspects since these should accommodate the perspectives of the potential key stakeholders. This paper presents a comprehensive problem structuring approach combining Soft Systems Methodology and Value Focused Thinking to elicit and organize the multiple aspects that influence energy efficiency of school buildings. The main aim of this work is structuring the fundamental objectives to develop a criteria tree to be considered in a multi-criteria classification model to be used by management entities for rating overall energy performance of school buildings. This methodological framework helped grasping the main issues at stake for a thorough energy performance assessment of school buildings and the need to define adequate policies for improvement.

This paper presents results of work developed in the field of building energy benchmarking applied to the building stock of the Polytechnic Institute of Leiria, Portugal, based on a thorough energy performance characterisation of each of its buildings. To address the benchmarking of the case study buildings, an energy efficiency ranking system was applied. Following an energy audit of each building, they were grouped in different typologies according to the main end-use activities developed: Pedagogic buildings, canteens, residential buildings and office buildings. Then, an energy usage indicator was used to establish a metric to rank the buildings of each typology according to their energy efficiency. The energy savings potential was also estimated, based on the reference building energy usage indicator for each typology, and considering two different scenarios, yielding potential savings between 10% and 34% in final energy consumption.