Current Building Management Systems Research Articles

Real-world implementation of a cloud-based MPC for HVAC control in educational buildings

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building’s heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively.

Analyzing energy consumption patterns of an educational building through data mining

Green building standards were adopted around the world to minimize energy consumption and carbon emission from the building sector. However, designing buildings following the standards does not guarantee a low-energy building. Actual energy consumptions in buildings were reported, on average, 2.5 times higher than predictions during the design stage, which is known as the performance gap. Most building operation managers do not have the tool and knowledge to understand the reasons for such differences. Moreover, current building management systems (BMS) do not have the intelligence to identify such differences and their sources. Understanding the root causes of the performance gap is even more difficult in an educational building because of its different usage patterns and various space types. This research aims to analyze the energy consumption patterns of different spaces in a mixed-use educational buildings and identify possible sources of energy waste using an unsupervised data mining approach. A 5-star Green Star-rated educational building in Melbourne, Australia, was considered for the case study. The results showed electrical and gas energy performance gaps of 2.4 and 3.1 times, respectively, in the studied building. Further analysis revealed that energy consumption during non-working hours was 48% of total energy consumption during the one-year studied period, which is very high and was one of the possible sources of waste. During the holidays, the mechanical system and plug loads ran as per the weekday operating schedule in an empty building, resulting in energy waste. Actual hourly lighting and plug load consumption profiles differed significantly from the predicted profile during design. Based on the findings, several recommendations were made to minimize the performance gap in an educational building.

Towards a Service-Oriented Architecture for the Energy Efficiency of Buildings: A Systematic Review

Currently, smart buildings generate large amounts of data due to the many devices and equipment available. Hence, buildings implement building management systems (BMSs), which monitor, control, manage and analyze each of these components. However, current BMSs are incapable of managing a massive amount of data (big data) and therefore cannot extract knowledge or make intelligent decisions in quasi real time. In addition, there are serious limitations to integrating BMSs with other services since they generally use proprietary software. In this sense, service-oriented architecture (SOA) is an architectural style that allows one to build distributed systems and provide functionalities such as services to end users or other types of services. Therefore, an SOA has the great advantage of allowing the expansion of the functionalities of BMSs. In fact, there are several studies that address SOAs for building management. However, we have not found any description or systematic analysis in the literature that allows the development of a versatile and interoperable SOA focused on the energy efficiency of buildings and that can integrate massive data analysis features. For these reasons, this study seeks to fill this knowledge gap and, more specifically, to identify and analyze the various software requirements proposed in the literature and the characteristics of big data that allow for improving the energy efficiency of buildings. To this end, we performed an in-depth review of the literature according to the methodology proposed by Kitchenham. As a result of this review, we provide researchers with a specific vision of the requirements and characteristics to consider for software development aimed at the energy efficiency of unique or historic buildings.

Internet of Things for Green Building Management: Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence

Buildings consume 60% of global electricity. However, current building management systems (BMSs) are highly expensive and difficult to justify for small- to medium-sized buildings. The Internet of Things (IoT), which can collect and monitor a large amount of data on different aspects of a building and feed the data to the BMS's processor, provides a new opportunity to integrate intelligence into the BMS for monitoring and managing a building's energy consumption to reduce costs. Although an extensive literature is available on, separately, IoTbased BMSs and applications of signal processing techniques for some building energy-management tasks, a detailed study of their integration to address the overall BMS is limited. As such, this article will address the current gap by providing an overview of an IoT-based BMS that leverages signal processing and machine-learning techniques. We demonstrate how to extract high-level building occupancy information through simple, low-cost IoT sensors and study how human activities impact a building's energy use-information that can be exploited to design energy conservation measures that reduce the building's energy consumption.

Planning meets activity recognition: Service coordination for intelligent buildings

Building managers need effective tools to improve occupants’ experiences considering constraints of energy efficiency. Current building management systems are limited to coordinating device services in simple and prefixed situations. Think of an office with lights offering services, such as turn on a light, which are invoked by the system to automatically control the lights. In spite of the evident potential for energy saving, the office occupants often end up in the dark, they have too much light when working with computers, or unnecessary lights are turned on. The office is thus not aware of the occupants’ presence nor anticipates their activities. Our proposal is to coordinate services while anticipating occupant activities with sufficient accuracy. Finding and composing services that will support occupant activities is however a complex problem. The high number of services, the continuous transformation of buildings, and the various building standards imply a search through a vast number of possible contextual situations every time occupants perform activities. Our solution to this building coordination problem is based on Hierarchical Task Network (HTN) planning in combination with activity recognition. While HTN planning provides powerful means for composing services automatically, activity recognition is needed to identify occupant activities as soon as they occur. The output of this combination is a sequence of services that needs to be executed under the uncertainty of building environments. Our solution supports continuous context changes and service failures by using an advanced orchestration strategy. We design, implement and deploy a system in two cases, namely offices and a restaurant, in our own office building at the University of Groningen. We show energy savings in the order of 80% when compared to manual control in both cases, and 60% when compared to using only movement sensors. Moreover, we show that one can save a figure of €600 annually for the electricity costs of the restaurant. We use a survey to evaluate the experience of restaurant occupants. The majority of them are satisfied with the solution and find it useful. Finally, the technical evaluation provides insights into the efficiency of our system.

Multi-agent system for energy consumption optimisation in higher education institutions

Global warming is one of the most serious issues faced by today's world. The increase in world population and adoption of modern lifestyle have dramatically increased the demand for energy. Over the last decade Higher Educational Institution (HEI) buildings have seen massive increase in energy consumption due to increased use of IT equipments, longer occupancy and increased use of Heating Ventilation and Air Conditioning (HVAC) systems. Current Building Management Systems (BMS) fail to optimize energy consumption of HVAC systems in commercial and educational buildings. In this paper we present an intelligent agent based system to optimize energy consumption of HVAC system in HEI buildings. The system employs artificial intelligence techniques to predict the demand of the system and optimize energy consumption of the HVAC system. The experimental results have shown that the deployment of the system has resulted in 3% reduction in energy consumption of HVAC.

A thermal preference scale for personalized comfort profile identification via participatory sensing

Thermal comfort is the main driving factor in determining heating, ventilation and air conditioning (HVAC) systems' operational settings. Current building management systems (BMS) operate according to conservatively defined and standardized settings. Many dynamic environmental and human related variables affect occupant thermal comfort in buildings, calling for flexibility in HVAC operations by integrating dynamic user preferences. This paper builds on the vision that a participatory sensing based approach could be used for integrating user thermal comfort preferences into the operational logic of HVAC systems on a real time and continuous basis. The paper emphasizes on participatory sensing requirements for improving human data acquisition by proposing a thermal comfort preference scale. Several guiding features, which could potentially improve the users' expressions of thermal comfort preferences, were evaluated. Interface usability evaluation methods, including think aloud usability assessment studies and task execution surveys, were used in order to assess the proposed alternatives. Moreover, a field experiment was conducted to explore the driving factors, which affect users' thermal preference vote, for determining HVAC control parameters. The results showed that the proposed sensation scale outperforms a five-level ASHRAE like scale and the addition of the guiding features helps increase the consistency between users' expectations and their votes. The results of the field study showed that the ambient temperature is the most effective factor on users' votes. Consequently, HVAC set points for temperature could be used as a control parameter for personalized thermal comfort without any need for retrofitting HVAC system components.

Holistic system architecture for energy efficient building operation

Abstract Buildings account for almost 40% of the total energy usage and 30% of the total CO2 emissions in Europe. Environmental, legislative and economical drivers require more efficient and accurate energy management of buildings. Current building management systems do not have the capabilities of energy specific monitoring and management. In order to address these issues, in this paper, the Holistic Multi-Dimensional Information Management System will be described with its components: Data Warehouse Core, Extraction, Transformation and Loading (ETL) tool and Information Representation tools. The purpose of the developed system is to store, integrate, analyse complex data sets from multiple data and information sources such as wired/wireless sensing devices (e.g. sensor and meter readings) and Building Information Modelling (BIM) Tools (e.g. Autodesk Revit Architecture and MEP). The developed system is demonstrated and validated in the Environmental Research Institute (ERI) Building located on the campus of National University of Ireland-University College Cork.