The Experts below are selected from a list of 7860 Experts worldwide ranked by ideXlab platform
Derek Clementscroome - One of the best experts on this subject based on the ideXlab platform.
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sustainable Intelligent Buildings for people a review
Intelligent Buildings International, 2011Co-Authors: Derek ClementscroomeAbstract:Intelligent Buildings need to be sustainable (i.e. sustain their performance for future generations), healthy and technologically up to date; meet regulatory demands; meet the needs of the occupants; and be flexible and adaptable enough to deal with change. Buildings will contain a variety of systems devised by many people, and yet the relationship between Buildings and people can only work satisfactorily if there is integration between the supply- and demand-side stakeholders as well as between the occupants, the systems and the building. To achieve this, systems thinking is essential in planning, design and management, together with the ability to create and innovate while remaining practical (see Glossary). The ultimate objective should be simplicity rather than complexity. This requires not only technical ability but also the powers of interpretation, imagination and even intuition. Building Regulations can stifle creativity but are necessary to set a minimum level of expectation and obey health and s...
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key performance indicators kpis and priority setting in using the multi attribute approach for assessing sustainable Intelligent Buildings
Building and Environment, 2010Co-Authors: Husam Alwaer, Derek ClementscroomeAbstract:The main objectives of this paper are to: firstly, identify key issues related to sustainable Intelligent Buildings (environmental, social, economic and technological factors); develop a conceptual model for the selection of the appropriate KPIs; secondly, test critically stakeholder's perceptions and values of selected KPIs Intelligent Buildings; and thirdly develop a new model for measuring the level of sustainability for sustainable Intelligent Buildings. This paper uses a consensus-based model (Sustainable Built Environment Tool- SuBETool), which is analysed using the analytical hierarchical process (AHP) for multi-criteria decision-making. The use of the multi-attribute model for priority setting in the sustainability assessment of Intelligent Buildings is introduced. The paper commences by reviewing the literature on sustainable Intelligent Buildings research and presents a pilot-study investigating the problems of complexity and subjectivity. This study is based upon a survey perceptions held by selected stakeholders and the value they attribute to selected KPIs. It is argued that the benefit of the new proposed model (SuBETool) is a ‘tool’ for ‘comparative’ rather than an absolute measurement. It has the potential to provide useful lessons from current sustainability assessment methods for strategic future of sustainable Intelligent Buildings in order to improve a building's performance and to deliver objective outcomes. Findings of this survey enrich the field of Intelligent Buildings in two ways. Firstly, it gives a detailed insight into the selection of sustainable building indicators, as well as their degree of importance. Secondly, it tesst critically stakeholder's perceptions and values of selected KPIs Intelligent Buildings. It is concluded that the priority levels for selected criteria is largely dependent on the integrated design team, which includes the client, architects, engineers and facilities managers.
Michaiis Michaelides - One of the best experts on this subject based on the ideXlab platform.
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partitioning of Intelligent Buildings for distributed contaminant detection and isolation
IEEE Transactions on Emerging Topics in Computational Intelligence, 2017Co-Authors: Alexis Kyriacou, Michaiis Michaelides, Stelios Timotheou, Christos G Panayiotou, Marios M PolycarpouAbstract:Intelligent Buildings are responsible for ensuring indoor air quality for their occupants under normal operation as well as under possibly harmful contaminant events. An emerging environmental application involves the monitoring of Intelligent Buildings against harmful events by incorporating various sensing technologies and using sophisticated algorithms to detect and isolate such events. In this context, both centralized and distributed approaches have been proposed, with the latter having significant benefits in terms of complexity, scalability, reliability, and performance. This paper considers the automatic partitioning of the building into subsystems, which enables the distributed simulation, modeling, analysis, and management of the Intelligent building while ensuring the effective detection and isolation of contaminants in the building interior. Specifically, we develop both a high-quality heuristic algorithm and an optimal mixed integer linear programming (MILP) formulation for the building partitioning problem. The MILP formulation is based on graph partitioning techniques, while the heuristic is based on matrix clustering techniques. Both approaches partition the building into subsystems while ensuring 1) maximum decoupling between the various subsystems, 2) strong connectivity between the zones of each subsystem, and 3) control of the size of the subsystems with respect to the number of allocated zones. A combination of the two approaches is also proposed for reconfiguring an initial partitioning composition in real time in order to accommodate partitioning needs that arise from dynamic system changes.
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A cognitive monitoring system for contaminant detection in Intelligent Buildings
2014 International Joint Conference on Neural Networks (IJCNN), 2014Co-Authors: Giacomo Boracchi, Michaiis Michaelides, Manuel RoveriAbstract:Intelligent Buildings are equipped with sensing systems able to measure the contaminant concentration in the different building zones for safety purposes. The aim of these systems is to promptly detect the presence of a contaminant so that appropriate actions can be taken to ensure the safety of the people. At the same time, these sensing systems, which operate in real-world conditions, suffer from noise and sensor degradation faults. Both noise and faults can induce false alarms (resulting in unnecessary disruptive actions such as building evacuation) or missed alarms (when the presence of a contaminant is not detected). This paper proposes a novel cognitive monitoring system for performing contaminant detection in Intelligent Buildings with real-time point-trigger sensors. The proposed system reduces the occurrence of false alarms by means of a three-layered architecture, which employs cognitive mechanisms to validate possible detections and discriminate between the presence of a real contaminant source and a degradation fault affecting the sensors of the sensing system. In addition, the proposed system is able to isolate the building zone containing the contaminant source (or the faulty sensor) and estimate the onset time of the release (or the fault).
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security oriented sensor placement in Intelligent Buildings
Building and Environment, 2013Co-Authors: Demetrios G Eliades, Michaiis Michaelides, Christos G Panayiotou, Marios M PolycarpouAbstract:Intelligent Buildings are beginning to utilize sensor networks for monitoring and protecting indoor air quality against contamination events. This paper presents a methodology for determining where to install such sensors. In particular, a multi-objective optimization problem is formulated for minimizing the sensor cost, the average and the worst-case impact damage corresponding to a set of contamination event scenarios. Each contamination scenario is comprised of parameters characterized by some given probability distribution. Based on these distributions, a set of representative contamination scenarios is constructed through grid and random sampling, and the overall impact of each scenario is computed, thus providing a solution to the sensor placement problem. The proposed methodology is illustrated by two case studies, a simple building with five rooms and a realistic building with 14 rooms.
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contaminant event monitoring in Intelligent Buildings using a multi zone formulation
IFAC Proceedings Volumes, 2012Co-Authors: Michaiis Michaelides, Christos G Panayiotou, Vasso Reppa, Marios M PolycarpouAbstract:Abstract The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants’ comfort, health, productivity and safety. These events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. Furthermore, we demonstrate how the problem of monitoring the indoor air quality in Intelligent Buildings against the presence of contaminant sources fits the usual framework of fault detection, isolation, identification and accommodation. Specifically, we develop a multi-zone formulation using state space equations that enables the use of fault diagnosis and fault tolerant control techniques for monitoring contaminant events inside the building environment. We demonstrate our proposed formulation for the problem of isolating multiple contaminant sources using an estimation scheme in a nine zone building setting.
Marco Aiello - One of the best experts on this subject based on the ideXlab platform.
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UEMCON - Activity Learning for Intelligent Buildings
2019 IEEE 10th Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON), 2019Co-Authors: Ilche Georgievski, Prashant Gupta, Marco AielloAbstract:To be considered Intelligent, Buildings need to operate automatically in a user-centric fashion. Intelligent Buildings enable and ensure a healthy, comfortable and productive working environment for their occupants. The transition to Intelligent Buildings requires systems that can recognise the needs and anticipate the behaviour of the users. We foster such a transition by learning the activities that occupants perform in Buildings. This is done by first recognising the activities and then predicting the time of their next occurrences. The idea rests upon an existing formalism for a building environment and applies machine learning. We implement and deploy a system in a living lab environment, showing that it can learn diverse occupant activities with high accuracy, provided simple and unobtrusive sensors.
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planning meets activity recognition service coordination for Intelligent Buildings
Pervasive and Mobile Computing, 2017Co-Authors: Ilche Georgievski, Tuan Anh Nguyen, Faris Nizamic, Brian Setz, Aliaksandr Lazovik, Marco AielloAbstract:Abstract 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.
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energy Intelligent Buildings based on user activity a survey
Energy and Buildings, 2013Co-Authors: Tuan Anh Nguyen, Marco AielloAbstract:Abstract Occupant presence and behaviour in Buildings has been shown to have large impact on heating, cooling and ventilation demand, energy consumption of lighting and appliances, and building controls. Energy-unaware behaviour can add one-third to a building's designed energy performance. Consequently, user activity and behaviour is considered as a key element and has long been used for control of various devices such as artificial light, heating, ventilation, and air conditioning. However, how are user activity and behaviour taken into account? What are the most valuable activities or behaviours and what is their impact on energy saving potential? In order to answer these questions, we provide a novel survey of prominent international Intelligent Buildings research efforts with the theme of energy saving and user activity recognition. We devise new metrics to compare the existing studies. Through the survey, we determine the most valuable activities and behaviours and their impact on energy saving potential for each of the three main subsystems, i.e., HVAC, light, and plug loads. The most promising and appropriate activity recognition technologies and approaches are discussed thus allowing us to conclude with principles and perspectives for energy Intelligent Buildings based on user activity.
Marios M Polycarpou - One of the best experts on this subject based on the ideXlab platform.
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partitioning of Intelligent Buildings for distributed contaminant detection and isolation
IEEE Transactions on Emerging Topics in Computational Intelligence, 2017Co-Authors: Alexis Kyriacou, Michaiis Michaelides, Stelios Timotheou, Christos G Panayiotou, Marios M PolycarpouAbstract:Intelligent Buildings are responsible for ensuring indoor air quality for their occupants under normal operation as well as under possibly harmful contaminant events. An emerging environmental application involves the monitoring of Intelligent Buildings against harmful events by incorporating various sensing technologies and using sophisticated algorithms to detect and isolate such events. In this context, both centralized and distributed approaches have been proposed, with the latter having significant benefits in terms of complexity, scalability, reliability, and performance. This paper considers the automatic partitioning of the building into subsystems, which enables the distributed simulation, modeling, analysis, and management of the Intelligent building while ensuring the effective detection and isolation of contaminants in the building interior. Specifically, we develop both a high-quality heuristic algorithm and an optimal mixed integer linear programming (MILP) formulation for the building partitioning problem. The MILP formulation is based on graph partitioning techniques, while the heuristic is based on matrix clustering techniques. Both approaches partition the building into subsystems while ensuring 1) maximum decoupling between the various subsystems, 2) strong connectivity between the zones of each subsystem, and 3) control of the size of the subsystems with respect to the number of allocated zones. A combination of the two approaches is also proposed for reconfiguring an initial partitioning composition in real time in order to accommodate partitioning needs that arise from dynamic system changes.
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security oriented sensor placement in Intelligent Buildings
Building and Environment, 2013Co-Authors: Demetrios G Eliades, Michaiis Michaelides, Christos G Panayiotou, Marios M PolycarpouAbstract:Intelligent Buildings are beginning to utilize sensor networks for monitoring and protecting indoor air quality against contamination events. This paper presents a methodology for determining where to install such sensors. In particular, a multi-objective optimization problem is formulated for minimizing the sensor cost, the average and the worst-case impact damage corresponding to a set of contamination event scenarios. Each contamination scenario is comprised of parameters characterized by some given probability distribution. Based on these distributions, a set of representative contamination scenarios is constructed through grid and random sampling, and the overall impact of each scenario is computed, thus providing a solution to the sensor placement problem. The proposed methodology is illustrated by two case studies, a simple building with five rooms and a realistic building with 14 rooms.
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contaminant event monitoring in Intelligent Buildings using a multi zone formulation
IFAC Proceedings Volumes, 2012Co-Authors: Michaiis Michaelides, Christos G Panayiotou, Vasso Reppa, Marios M PolycarpouAbstract:Abstract The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants’ comfort, health, productivity and safety. These events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. Furthermore, we demonstrate how the problem of monitoring the indoor air quality in Intelligent Buildings against the presence of contaminant sources fits the usual framework of fault detection, isolation, identification and accommodation. Specifically, we develop a multi-zone formulation using state space equations that enables the use of fault diagnosis and fault tolerant control techniques for monitoring contaminant events inside the building environment. We demonstrate our proposed formulation for the problem of isolating multiple contaminant sources using an estimation scheme in a nine zone building setting.
Dixin Liu - One of the best experts on this subject based on the ideXlab platform.
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measuring indoor occupancy in Intelligent Buildings using the fusion of vision sensors
Measurement Science and Technology, 2013Co-Authors: Dixin Liu, Xiaohong Guan, Qianchuan ZhaoAbstract:In Intelligent Buildings, practical sensing systems designed to gather indoor occupancy information play an indispensable role in improving occupant comfort and energy efficiency. In this paper, we propose a novel method for occupancy measurement based on the video surveillance now widely used in Buildings. In our method, we analyze occupant detection both at the entrance and inside the room. A two-stage static detector is presented based on both appearances and shapes to find the human heads in rooms, and motion-based technology is used for occupant detection at the entrance. To model the change of occupancy and combine the detection results from multiple vision sensors located at entrances and inside rooms for more accurate occupancy estimation, we propose a dynamic Bayesian network-based method. The detection results of each vision sensor play the role of evidence nodes of this network, and thus, we can estimate the true occupancy at time t using the evidence prior to (and including) time t. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
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vision based indoor occupants detection system for Intelligent Buildings
International Conference on Imaging Systems and Techniques, 2012Co-Authors: Dixin Liu, Qianchuan Zhao, Xiaohong GuanAbstract:In Intelligent Buildings, practical sensing systems designed to gather indoor occupancy information play an indispensable role in improving occupant comfort and energy efficiency by optimizing control strategies of HVAC (Heating, Ventilation and Air Conditioning) system and lighting system. In this paper we propose a novel method for occupant detection based on video surveillances now widely used in Buildings. In our method, a two-staged static detector using both Haar-like and HOG (Histograms of Oriented Gradients) features and a template-based motion analysis module are concatenated to detect the heads of occupants rapidly and effectively. The accuracy can satisfy the requirements of the automation systems in Intelligent Buildings. Experimental results demonstrate the effectiveness and efficiency of the proposed method.