Smart buildings are the main starting point from which to transform cities into smart cities where various technologies, including data analytics, data acquisition, data storage, and data visualization, are integrated to deliver high-quality, safe, secure, and cost-effective services to the occupants. The benefits of today’s smart buildings include not only energy efficiency and delivering comfort, but also learning the occupants’ characteristics, preferences, as well as changing environmental conditions, and interacting with or adapting to them. To attain the maximum possible benefits from smart buildings, occupant-related parameters and environmental variables need to be included in the management system. Designing a real-time Buildings Management System (BMS) that integrates all building aspects is a challenging task due to the vast differences in building components, large volumes of data, heterogeneity of building dynamics, and inevitable uncertainties.

Thanks to the emergence of Internet of Things (IoT) technologies as well as the recent developments in Machine Learning (ML) and Deep Learning (DL), the concept of smart buildings and smart homes has become a reality. IoT-based buildings management systems combine advancements in sensor technologies, communications and advanced control algorithms for optimized and efficient energy use in buildings/homes. Thus, the adoption of those systems leads to economic benefits for both, the end- users and the Distribution Network Operators (DNOs) while reducing the environmental impact of the buildings sector. Over the past decade, there has been a surge in Cloud Computing and in the development of smart building/home devices that can connect to the Internet and be controlled remotely. This has led to cloud-centric IoT-based solutions for the development of smart buildings/homes. However, due to an ever-increasing number of IoT devices, network bandwidth and security weaknesses have become a bottleneck, hindering the performance of these systems. Edge computing is a recent computing paradigm that has been proposed as a potential technology for the construction of a scalable network infrastructure in the user’s vicinity, which would be rich in IoT resources (i.e. storage, compute, and bandwidth), processing real-time security-crucial data in a one-hop manner to minimize latency. Edge computing is viewed as the most promising technology for future IoT-based smart buildings. By coupling it with Distributed Ledger Technologies (DLT) such as blockchain, security and privacy may be increased further.

There are nowadays two challenges in building management and control systems:

Various ML and DL techniques have been proposed in the building sector for optimized management systems by forecasting , classical ML models, especially those based on DL, cannot be properly interpreted, impeding the understanding of the system’s recommendations.

On the one hand, recent explainable Artificial Intelligence (XAI) approaches can tackle the first problem, such as hybrid neuro-symbolic AI algorithms that increase interpretability, where Deep Symbolic Learning has emerged as a promising solution that will be investigated and integrated in the proposed Edge computing architecture. On the other hand, a Social Computing approach, based on Virtual Organizations is proposed as a solution, where the data measures are suggested on the basis of the preferences of users with a similar behavior profile.

In this 3-year project, a BUILDings intelligent MAnagement system based on edge computing (BUILDMA) will be designed and validated by conducting two pilot tests in a real-world setting. It will be based on a secured and scalable 3-tier Edge Computing architecture supported by virtual organizations that will orchestrate distributed XAI algorithms to obtain and analyse occupant- related parameters and environmental variables, integrating local generation and storage systems. The estimations and predictions of the data available will be recommended to users by following a social computing approach, that will try to maximize indoor comfort (thermal comfort, visual comfort and indoor air quality), moreover the system will flexibly adapt to occupancy patterns, following consumers’ preferences and considering the reputation of efficiency actions.