New Building Management Systems for Smart Cities: A Brief Analysis of Their Potential

Abstract

This chapter explores how smart cities can enhance building management through technologies like the Internet of Things (IoT) and advanced predictive models, focusing on energy efficiency and air quality. The escalating reliance on technology as the primary solution to contemporary and future challenges has highlighted Internet of Things (IoT), digitalization, and machine learning, among others, as new methodologies for assessing management in smart cities. Moreover, in the realm of defining innovative building management systems, pressing issues such as climate change and pandemic episodes like COVID-19 underscore the need to prioritize energy efficiency and air quality. This imperative has led to the emergence of digital twins, a technology integrating 3D models with real-time data, enabling a comprehensive understanding of building dynamics. In addition, automated prediction models leveraging advanced statistical and machine learning techniques contribute significantly to enhancing climatization control, energy efficiency, and air quality management. These predictive models analyze historical data, enabling accurate forecasts to assess future behavior, which is crucial for effective maintenance planning. The application of linear and non-linear regression models, alongside techniques like Support Vector Machinesand neural networks, further refines predictions. Additionally, real-time monitoring and decision algorithms optimize information transmission during incidents, ensuring a rapid response to environmental factors or anomalies, thereby mitigating risks and maximizing operational efficiency.