Abstract
This work presents a multi-criteria classification system considering multiple, conflicting and incommensurate evaluation aspects influencing energy efficiency in school buildings. The multi-criteria ELECTRE TRI method is used to classify energy performance of school buildings into categories of merit using the IRIS software. The alternatives under evaluation are assigned to predefined ordered categories according to their absolute performance and not in comparison with the performance of other alternatives. Each alternative is assessed using reference profiles defming the boundaries of the categories in which the alternatives should be sorted. The model was applied using a set of performance indicators obtained in the framework of a research and development project aimed at assessing the energy performance of a sample of Portuguese school buildings. IRIS allowed inferring robust conclusions by indicating the range of categories for each alternative, considering the decision maker's preferences, captured by the parameters of ELECTRE TRI. (C) 2017 The Authors. Published by Elsevier Ltd.

Abstract
The main goal of this paper is to present a set of well-defined and structured procedures to establish guidelines for the application of an integrated assessment of energy performance and indoor climate in schools. Increasing the knowledge about how energy is consumed in schools is a way to enhance the awareness of school managers (board of directors) about the importance of improving energy efficiency and reducing energy costs. The proposed methodology helps to identify major energy consuming equipment in school buildings and potential energy conservation measures. The assessment of indoor climate identifies potential corrective measures to problems related to indoor air quality and thermal comfort, also supporting the study of further energy conservation measures associated with ensuring environmental quality. Results of a case study showed that the expected energy consumption reduction is about 11.2% due to a better usage of daylighting and 4.5% due to the reduction of fresh air flow rates, while extending the ventilation operation time. In addition, there is a considerable non-calculated potential for energy savings and improvement of indoor environmental conditions in school buildings, promoting students and teachers productivity and wellbeing.

Abstract
Students spend 30 % of their lives indoors; therefore, a healthy indoor air quality (IAQ) is crucial for their well-being and academic performance in Higher Education Institutions. This review highlights the interventions for improving Indoor Enviclassrooms considering climate change by discussing ventilation techniques, phytoremediation, and building features designed to improve noise levels, thermal comfort, lighting and to reduce odor. Awareness and literacy are enhanced through the student's engagement by offering real-time monitoring knowledge of Indoor Environmental Quality using inexpensive smart sensors combined with IoT technology. Eco-friendly strategies are also highlighted to promote sustainability.

Abstract
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.