AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

Abstract

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

“…This reviews the urban potential of BID detection while ensuring the integration of key dimensions of BID classifications, which are unknown to be primordial in the successful integration of SC to compliance with the sustainable development goal and the new urban agenda to better multimedia streaming [42]. In order to acquire valuable insights for the advancement of intelligent information services, it is imperative to store the image dataset produced from diverse domains in a local repository and scrutinize the accumulated data for global information propagation.…”

“…As a result, while the BID offers immense potential to improve the moral quality of urban life, its use in smart cities presents several challenges [42]. Therefore, there is still a need for an urban infrastructure that can provide economic, unified, speedy, reliable, robust, and simple access to a significant number of public services, from image transmission and diffusion to information production and knowledge extraction.…”

“…Data visualizations can also be done in 2D or 3D graphs, depending on the use case, in standard forms such as images, videos, or virtual reality (See Fig. 11) [42]. The subject of visualization in the real-time multimedia domain also raises questions about how interdisciplinary exchanges between art historians and computer scientists should develop in the future.…”

The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

“…Himeur et al ( 2022 ) explained that AI is facing many practical and operational challenges, ranging from the need for a basic familiarity with these systems to finding the necessary technical talent to managing the quality of big-data inputs and being able to understand and explain how AI models produce their outputs.…”

Monitoring of public buildings via energy-efficient Z-Wave wireless sensors

“…A correct and unsupervised interpretation of monitoring data can indeed be a challenging task. To overcome these challenges, advanced analytical methods, such as machine learning and data-driven modelling, have been employed [15][16][17][18]. The present paper discusses the use of advanced monitoring techniques for optimal control of building management systems (BMS) in public shared buildings, with the objective of guaranteeing the appropriate air quality, minimizing energy consumption.…”