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"BUILDING MANAGEMENT"
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AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
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.
Journal Article
Towards an Occupancy-Oriented Digital Twin for Facility Management: Test Campaign and Sensors Assessment
This study focuses on calibration and test campaigns of an IoT camera-based sensor system to monitor occupancy, as part of an ongoing research project aiming at defining a Building Management System (BMS) for facility management based on an occupancy-oriented Digital Twin (DT). The research project aims to facilitate the optimization of building operational stage through advanced monitoring techniques and data analytics. The quality of collected data, which are the input for analyses and simulations on the DT virtual entity, is critical to ensure the quality of the results. Therefore, calibration and test campaigns are essential to ensure data quality and efficiency of the IoT sensor system. The paper describes the general methodology for the BMS definition, and method and results of first stages of the research. The preliminary analyses included Indicative Post-Occupancy Evaluations (POEs) supported by Building Information Modelling (BIM) to optimize sensor system planning. Test campaign are then performed to evaluate collected data quality and system efficiency. The method was applied on a Department of Politecnico di Milano. The period of the year in which tests are performed was critical for lighting conditions. In addition, spaces’ geometric features and user behavior caused major issues and faults in the system.Incorrect boundary definition: areas that are not covered by boundaries; thus, they are not monitored
Journal Article
Green BIM-based study on the green performance of university buildings in northern China
Background
Energy-efficient university campuses will play a vital role in the development of future sustainable cities, and will be important for achieving the Chinese carbon-neutrality goals. It is, therefore, necessary to develop new decision-making tools for evaluating the sustainability of campus buildings. Since university campuses typically comprise a broad variety of building types, standardized evaluation methods and tools, such as Green BIM, are needed. Green BIM (Building Information Modeling) emphasizes the importance and role of BIM technology in the design and construction of green buildings, providing a standardized framework for the decision-making process, and methods for improving the green performance of buildings.
Methods
This study develops a method based on the Green BIM framework, using BIM architecture to analyse building performance, and the
Assessment Standard for Green Building
(GB/T 50378-2019) standard to establish benchmark values for evaluation, and project objectives. The method is evaluated on three examples of the most representative university buildings in northern China. The goal is to understand common denominators and differences between different types of campus buildings, in terms of green building indicators, that are important to consider in the early design stages of campus building complexes.
Results
In this study, a library is used as a case study to demonstrate the tools for evaluating green performance. The study optimizes green performance from five aspects: surrounding environment, function layout, envelope performance and system transformation, and management measures improvement. The results show that this optimization scheme can achieve reductions of the annual loads of about 47.4%, in line with the national energy efficiency standards for public buildings. In particular, the heating load was reduced by 59.1%, and the cooling load reduced by 21.5%.
Conclusion
A comprehensive approach, combining the aspects of planning, building design, system design, energy management, and energy conservation planning, is required to improve the green performance of university buildings to meet the goals. In the future, it will be further necessary to perform data mining of energy consumption patterns, and continue energy retrofitting of existing buildings and energy systems, to achieve the goal of green and low-carbon campuses.
Journal Article
Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis
The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.
Journal Article
Impact of COVID-19 Pandemic on Energy Consumption in Office Buildings: A Case Study of an Australian University Campus
Building energy management, in terms of both adopted technologies and occupant consumption behaviour, is becoming an essential element of sustainability and climate change mitigation programs. The global COVID-19 pandemic and the consequential lockdowns and remote working had a notable impact on office building operations and provided a unique opportunity for building energy consumption studies. This paper investigates the COVID-19 effects on energy consumption in office buildings, particularly in the education sector. We studied different buildings at the University of Technology Sydney (UTS) campus before and during the pandemic period. The results demonstrate that the changes in energy consumption due to COVID-19 in different UTS faculties are not as strongly correlated with occupant activity. The comparison shows that buildings with administrative offices or classrooms are easier to switch to a remote-working mode than those housing laboratories and special equipment. During weekends, public holidays, or conditions requiring working from home, the per capita energy consumption increases significantly translating into lower energy efficiency. Our findings highlight the essential need for some changes in office building energy management systems. We provide recommendations for office and commercial buildings in general to deal with similar crises and to reduce energy overconsumption in normal situations.
Journal Article