Green Building Solutions
Thursday, October 15, 2009
Presented by the Green Buildings Discussion Group
Advances towards low-energy buildings depend substantially on improving building system controls based on real-time data collection and analysis. However, our ability to acquire data about a building’s real-time performance has outstripped our ability to effectively interpret and use it. In most existing buildings, significant energy waste remains invisible to the operator.
Energy-saving systems - such as mixed-mode ventilation, low-temperature radiant heating/cooling with slab thermal storage and optimized multi-source central plants -require evolving control technologies to interpret data and operate efficiently. These technologies include predictive control, learning algorithms, data-intensive sensor arrays and the processing intelligence to make sense of them.
This meeting will use case studies to explore this important area of technology application, and address questions such as:
• What is the current state of practice in building controls systems?
• What capabilities in information analytics are emergent today and why are they important?
• How is science providing the groundwork for what we can expect in tomorrow’s building control systems?
This event is the first in a four-part series “Green Building Solutions: What’s Working?”. The series will showcase existing green building technologies, demonstrate case studies of building design and construction incorporating these technologies and examine the lessons learned in the process. Technologies to be covered may include energy efficiency measures, renewable energy generation, water efficiency and reuse, waste reduction and the use of environmentally responsible materials.
This program qualifies for two professional development hours (PDHs) for Professional Engineers or two learning units (LUs) for Licensed Architects under sponsorship of New York Institute of Technology (NYIT). To receive these credits, obtain the supplemental registration form at the meeting and submit to NYIT along with a $50 registration fee.
Gregory Provan, PhD
University College Cork, Ireland
Gregory Provan is a professor in the Computer Science Department, and joined UCC in 2004 as the Head of Department. Prior to that, he was a technical manager at Rockwell Scientific Company and on the faculty at the University of Pennsylvania. He has a DPhil in Mathematics from Oxford University, an MSc from Stanford University, and a BSE from Princeton University. He has published over 80 peer-reviewed papers in the areas of control, model-based diagnosis, analysis of complex systems, probabilistic networks and stochastic algorithms, and bioinformatics. In the ITOBO project he leads the work in the design of system level models/system-level control, diagnostics and middleware specifications.
Stephen Samouhos, MS
MIT Building Technology Laboratory
Stephen Samouhos is a PhD candidate in mechanical engineering at MIT completing the last year of his doctoral studies at the MIT Building Technology Laboratory. His thesis “Intelligent Infrastructure for Energy Efficiency” is focused on the convergence of complex systems analysis, data acquisition, and information presentation to enable scalable and measurable building energy efficiency. Stephen’s PhD thesis is solely supported by the Hertz Fellowship Organization, but is fundamentally enabled by growing up in his family's mechanical contracting company. Prior to his doctoral studies, Stephen received a BS (2004) and MS (2007) at MIT, both in mechanical engineering and with a focus on thermal fluids engineering across various applications.
Jane L. Snowdon, PhD
IBM T. J. Watson Research Center
Dr. Jane L. Snowdon is a Senior Manager and Research Staff Member in the Industry Solutions and Emerging Business Department at the IBM T. J. Watson Research Center in Yorktown Heights, NY. In this role, she is responsible for developing strategies and driving research efforts worldwide to create innovative solutions for small and medium-sized businesses in areas such as intelligent buildings, collaboration, security, remote systems management, and cloud computing. In 2008, Jane co-led the IBM Corporate Strategy sponsored study on Industry Impacts of Climate Change. In 2008-2009, Jane is an active contributor to the IBM Corporate Strategy Intelligent Building Study and co-leader for the Strategic Initiatives portion of the IBM Corporate Strategy Smart Cities Study. Jane is a member of the National Institute of Standards’ (NIST) Smart Grid Interoperability Roadmap Building to Grid (B2G) Domain Expert Working Group (DEWG), the OASIS Energy Interoperation Technical Charter on OpenADR (Open Automated Demand Response) and the OASIS Blue Steering Committee. She is a thought leader and contributor to IBM’s Global Technology Outlook and Global Innovation Outlook.
Prior to her current position, Jane managed the Emerging Systems Design Department at IBM Research where she led work in the design of new high performance computing servers and next generation technologies to simplify the personal computer life cycle process. Jane has conducted research in schedule planning optimization, journey management, crew pairing, and recovery from irregular operations for domestic and international airlines. She also has deep expertise in business process modeling and business process simulation for manufacturing and transportation domains.
Jane received her Ph.D. in Industrial and Systems Engineering and a Certificate in Manufacturing Systems from the Georgia Institute of Technology, a M.S. degree in Industrial and Operations Engineering from the University of Michigan, and a B.S. degree with distinction in Industrial and Management Systems Engineering from the Pennsylvania State University. She was inducted into the Connecticut Academy of Science and Engineering (CASE) in 2006 and the Academy of Distinguished Engineering Alumni at Georgia Tech in 2008. Jane was selected as the General Chair for the 2002 Winter Simulation Conference (WSC), Technical Program Chair and Council Member for the 2000 and 2001 AGIFORS (Airline Group of the International Federation of Operations Research Societies) Symposiums, Advisory Council Member of the 2001 INFORMS (Institute for Operations Research and the Management Sciences) Practice Meeting. Jane is an Advisory Board member for the Department of Industrial and Systems Engineering at Georgia Tech and the Center of Excellence in Wireless and Information Technology (CEWIT) Medical Division at Stony Brook University. She is a Senior Member of the Institute of Industrial Engineers (IIE) and the Institute for Electrical and Electronic Engineers (IEEE), and a Member of INFORMS.
Jane is a co-chair and leadership catalyst for the Watson Women’s Network (WWN) and is the WWN representative to the Watson Diversity Council. She mentors researchers in the United States and China, and is part of IBM’s Makocha Minds initiative for mentoring African university students.
Kurt Roth, PhD
Fraunhofer Center for Sustainable Energy Systems
Dr. Kurt Roth leads the Building Energy Efficiency Group at the Fraunhofer Center for Sustainable Energy Systems (CSE). His group builds upon the extensive expertise and capabilities of the Fraunhofer Institute for Solar Energy Systems (ISE) and the Fraunhofer Institute for Building Physics (IBP) to develop, demonstrate, test, evaluate, and analyze advanced energy-saving building technologies. Prior to joining Fraunhofer CSE, he was a Principal in the Mechanical Systems group of TIAX LLC, formerly Arthur D. Little’s Technology & Innovation business. Dr. Roth has led several studies funded by the Department of Energy to assess the energy savings and commercialization potentials of HVAC, building controls and diagnostics, toplighting, and IT technologies. In addition, he led analyses of building energy consumption, including the energy consumed by commercial and residential IT, consumer electronics, and residential and commercial miscellaneous electricity consumption. Dr. Roth has presented the results of these studies at several conferences and meetings and authored a chapter on "Information Technology and Energy Use" for the Encyclopedia of Energy (Academic Press). Furthermore, he has authored more than sixty "Emerging Technology" articles for the ASHRAE Journal. Dr. Roth received his B.S., M.S., and Ph.D. degrees from the Massachusetts Institute of Technology (MIT), all in mechanical engineering. He is a member of Sigma Xi, the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE), and the American Solar Energy Society.
Hierarchical Monitoring and Diagnostics for Sustainable-Energy Building Applications
Gregory Provan, PhD, University College Cork, Ireland
Improving the energy efficiency of buildings is a complex task that involves the use of advanced building materials technology together with intelligent control, monitoring, diagnosis and control-reconfiguration. Here, we focus on the role that software for monitoring, fault diagnosis and control-reconfiguration play in sustainable-energy buildings. In typical applications, monitoring and diagnostics are an after-thought and are developed after the design process, being composed of rules that are developed independently of the design models. This has many drawbacks, including a failure to make use of the information in the design models, and the difficulty of maintaining consistency between the design and diagnosis models during the process of continuous commissioning.
To circumvent these problems, we propose a hierarchical approach for monitoring, diagnosis and control-reconfiguration that uses a core set of high-fidelity models, such as those used for design and simulation, as the basis for generating condition-based monitoring (CBM), fault-detection/isolation (FDI) and model-based diagnosis (MBD) models. The meta-model consists of topological relations for system components, augmented with system functional behaviours described by hybrid-systems models with dynamical equations to specify state changes in the plant, e.g., dynamical equations for heat diffusion. We show how we can apply model-transformation techniques to the high-fidelity meta-model to generate the target CBM, FDI and MBD models, given a specific building instance (as encoded by the building topology, material parameters, etc.). We derive a reduced-order monitoring model M from the meta-model, and estimate the parameters in M using machine learning given the building sensor data. This model M can be used to estimate anomalous conditions, from which potential faults or inefficient energy usage can be estimated. We derive a reduced-order model-based diagnosis model D from the meta-model by augmenting the meta-model with component-failure information. Given an anomaly indicated by M from sensor data S, we use D to isolate the root causes of the anomaly. Finally, we can extend the diagnosis model D with control information from the meta-model to define a dynamic control-reconfiguration policy that aims, if possible, to maintain set-points that optimise energy usage given system faults.
Human Factors in Smarter Buildings
Stephen Samouhos, MIT Building Technology Laboratory
Increased energy efficiency in our current and coming building stock is a critical component to any sustainable energy future. Building controls and information technology hold promise for enabling that energy efficiency at scale, but will do so only once human factors in buildings are properly accommodated by those technologies. Ultimately, buildings are built, serviced and inhabited by people, not computers, hence regardless of intelligent building controls or equipment we are still the deciding factor in building energy efficiency. In the context of this panel discussion, we will explore several examples of human factors that govern the success of building control technologies. We will find that information systems play the essential role in mitigating the effects of human factors that cause efficiency projects to fail. Finally, we will draw conclusions about the necessary careful design of information systems that will help transform our pathologically energy in-efficient building stock to one that maximizes the utility of its resources.
Advances in the Science of Analytics for Smarter Buildings
Jane L. Snowdon, PhD, IBM T. J. Watson Research Center
Advances in hardware and software technology have made it possible for organizations to store and record large streams of transactional data. The development of sensor technology has resulted in the possibility of monitoring many events in real time. The challenge of extracting knowledge from data draws upon research in statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and insights for decision-making. This panel will discuss the progress and open research problems associated with analyzing large data sets.
Travel & Lodging
The New York Academy of Sciences
7 World Trade Center
250 Greenwich Street, 40th floor
New York, NY 10007-2157
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