As a consequence, the big data analytics market in the building energy sector is expected to grow at a Compound annual growth rate (CAGR) of 11.28%, during the forecast period, 2021–2026. This allows for more targeted analysis, but also means that more powerful, intelligent, and sophisticated tools are needed to identify the most enormous patterns/variables (Muntean et al. Accordingly, with advanced sensing and metering technologies in BAMSs, data split into multiple modalities and many variables can create a comprehensive source of information to analyze. In this context, as the quantity of data collected in BAMSs is enormous, the ”big data” phenomena is surfacing this field and revolutionizing the way we manage data by using AI-big data analytics tools (Quinn et al. This has offered an excellent opportunity for implementing big data mining and analysis in BAMSs. Vast quantities of building automation and management data are produced, gathered and saved (Sardianos et al. With the broad utilization of information and communication technologies (ICTs), sensing and measurement technologies along with the cloud computing, big data storage and data analytics, conventional BAMSs are being revolutionized. This is possible by networking a plethora of sensors and components responsible for the monitoring and operation of mechanical, security, fire, lighting, HVAC and humidity control and ventilation systems (Su and Wang 2020). That said, BAMSs deliver crucial information to operators and/or users on the operational performance of buildings, which aim at promoting energy efficiency and optimizing water consumption, enhancing the safety and comfort of the occupants, reducing maintenance costs, extending the life cycle of the utilities, etc (Ippolito et al. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.īuilding automation and management systems (BAMSs) are intelligent systems of both hardware and software, connecting heating ventilation and air conditioning system (HVAC) systems, lighting, security, and other systems to communicate on a single platform. 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. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Moving on, a critical discussion is performed to identify current challenges. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. ![]() ![]() load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. ![]() This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. ![]() 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. 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. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. Therefore, many other tasks are left to the operator, e.g. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings.
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