3I Buildings: Intelligent, Interactive and Immersive Buildings

Abstract This research presents the architecture of a technology platform capable of integrating different types of data from building sensors and providing an interface to manage and operate facility devices, which is supported by advanced optimization algorithms. This interface is potentiated by a BIM-based interface presenting real-time data of the building. The solution, called 3i buildings - Intelligent, Interactive, and Immersive Buildings, is a tool to monitor and manage smart buildings, as well as optimize users experience, energy consumptions and environment quality. This is achieved by a grid of sensors and devices that continuously gather information (structural conditions of the building, occupancy, comfort of occupants, energy consumptions and CO2, COV's and Humidity levels, etc.), which is processed by predictive models able to learn over time. The 3D representation of the models allows managers to take advantage of the virtual environment, by augmenting the facility model and including information about the facility, making it easier and perceptible to users and owners, helping them to make better decisions. To support our research, the system will be installed in three different environments, Luz's hospital, Lisbon Aquarium and Norte Shopping, to test the solution under different conditions, objectives and users. In the first two cases the objectives are to monitor building air quality, consumptions and occupancy and in the Norte Shopping case the objectives are to monitor people flows, interact with tem and help the response in case of crisis according to the adopted emergency plan. These types of systems might help reducing energy consumptions as well as increasing comfort and satisfaction of occupants, maintaining a constant concentration of CO2 and humidity within the facility. The optimized algorithms will allow the system to learn, predicting and reacting to different conditions, giving a more reliable and smooth response to occupants needs.

[1]  Sandhya Patidar,et al.  Understanding the energy consumption and occupancy of a multi-purpose academic building , 2015 .

[2]  Mikael Johansson,et al.  Efficient Real-Time Rendering of Building Information Models , 2009, CGVR.

[3]  Stefan Boeykens.  Using 3D Design Software, BIM and Game Engines for Architectural Historical Reconstruction , 2011 .

[4]  Ahmad Faruqui,et al.  The impact of informational feedback on energy consumption d A survey of the experimental evidence , 2010 .

[5]  Yingxin Zhu,et al.  Evaluating thermal comfort in mixed-mode buildings: A field study in a subtropical climate , 2015 .

[6]  K. F. Fong,et al.  HVAC system optimization for energy management by evolutionary programming , 2006 .

[7]  Charles M. Eastman,et al.  BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors , 2008 .

[8]  Alexandre Santos,et al.  Internet of Things and Smart Objects for M-health Monitoring and Control , 2014 .

[9]  Hans Schaffers,et al.  Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation , 2011, Future Internet Assembly.

[10]  Luis A. Hernández Gómez,et al.  Smart Cities at the Forefront of the Future Internet , 2011, Future Internet Assembly.

[11]  Lingfeng Wang,et al.  Multi-objective optimization for decision-making of energy and comfort management in building automation and control , 2012 .

[12]  Antoine Guillemin,et al.  An innovative lighting controller integrated in a self-adaptive building control system , 2001 .

[13]  Gerhard P. Hancke,et al.  The Role of Advanced Sensing in Smart Cities , 2012, Sensors.

[14]  Jun Xu,et al.  A systematic approach for energy efficient building design factors optimization , 2015 .

[15]  Michael Stadler,et al.  Improving energy efficiency via smart building energy management systems. A comparison with policy measures , 2015 .

[16]  村上 昌史,et al.  Field experiments on energy consumption and thermal comfort in the office environment controlled by occupants' requirements from PC terminal , 2009 .

[17]  Di Wang,et al.  Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design , 2015 .

[18]  Tianzhen Hong,et al.  Simulation of occupancy in buildings , 2015 .

[19]  Thomas Froese,et al.  Future directions for IFC-based interoperability , 2003, J. Inf. Technol. Constr..

[20]  Alan McGibney,et al.  A wireless sensor network design tool to support building energy management , 2009, BuildSys '09.

[21]  I-Chen Wu,et al.  Applying Cloud Computing Technology to BIM Visualization and Manipulation , 2011 .

[22]  P. A. Mehta,et al.  Artificial intelligence and networking in integrated building management systems , 1997 .

[23]  Johnny Rodgers,et al.  Chasing the Negawatt: Visualization for Sustainable Living , 2010, IEEE Computer Graphics and Applications.