Hermano Bernardo

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.

Abstract

Abstract
Recent studies have focused on forecasting power demand in buildings to enhance energy management. However, the literature still lacks comparative analyses of power demand forecasting algorithms. In addition, more case studies involving different building typologies are needed, as each building exhibits distinct behavior and load profiles. This paper aims to develop machine learning models to forecast the power demand of a large shopping center in the northern region of Portugal. The main objective is to compare the performance of several machine learning models. The results are promising, demonstrating adequate performance even during most holidays.

Abstract
To achieve carbon neutrality by 2050, commercial buildings have installed photovoltaic systems to reduce carbon emissions and operational costs. Nevertheless, PV generation does not always match the building’s energy demand profile, therefore storage systems are needed to store excess energy and supply it when necessary. This paper presents a Mixed Integer Linear Programming optimisation algorithm designed to schedule the operation of the electric storage system, aiming to minimise the building’s energy-related costs. An annual hourly simulation of the optimised system was performed to assess the cost reduction. To prevent excessive operation of the electric storage system, an approach to penalise low energy charging was studied, with results showing a significant increase in the system’s lifespan.