Person:
Augenbroe, Godfried

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    Planning Energy-Efficient Buildings and Cities
    (Georgia Institute of Technology, 2018-03-08) Augenbroe, Godfried ; Guhathakurta, Subhro ; Mallen, Evan ; Yang, Perry Pei-Ju
    Urban built form plays a crucial role in energy consumption, hence planning energy-efficient cities requires thoughtful design at multiple scales -- from buildings, to neighborhoods, to cities.
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    The Future of Energy and Design
    (Georgia Institute of Technology, 2016-11-11) Augenbroe, Godfried ; Breen, Rita ; Gentry, T. Russell ; Jackson, Roderick ; MIller, Justin ; Schmidt, Jacqueline
    Every year, the world’s population increases by 65 million people, and over the next 13 years, 600 cities will account for nearly 65 percent of global GDP growth. This afternoon symposium will explore ideas related to 21st urban housing in the context of changing urban demographics, sustainability targets and alternative energy requirements, via guest presentations, audience involvement and exemplary design projects. There are a number of new initiatives focused on understanding the forces in play as urbanized areas like Tokyo, Seattle, Atlanta, San Francisco, and New York City work to address issues associated with designing livable, energy efficient and affordable urban dwellings. As cities like Atlanta continue to experience a move away from satellite single family bedroom communities towards center city, mid and high-rise housing blocks, our challenge is to create sustained focus and dialogue on ecology, opportunity and affordability.
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    Energy Saving in the Built Environment
    (Georgia Institute of Technology, 2011-03-31) Augenbroe, Godfried
    A brief overview of the energy saving opportunities in the built environment followed by a review of energy saving strategies for new "green building" design as well as retrofits of existing buildings. The talk will conclude with a critical review of current energy design methods, a reality check of the design of zero energy buildings, and a discussion of the much needed large scale energy retrofits.
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    Fried Augenbroe: COA Research Forum
    (Georgia Institute of Technology, 2009-10-29) Augenbroe, Godfried
    Since 1997, Fried Augenbroe heads the Building Technology area in the Doctoral Program in the College of Architecture at the Georgia Institute of Technology in the USA, where he teaches graduate courses and conducts research in the fields of building performance concepts and simulation, control of smart systems, e-Business, system monitoring and diagnostics. He has also established an active research record in building process studies, construction project management, web hosted collaboration, and knowledge management, dealing with the development of software tools, their interoperability and their business integration. In the field of energy modeling Augenbroe has led large building energy simulation projects for the development and application of energy saving technologies in buildings and residential construction. He has developed building energy performance metrics for large institutional real estate managers such as the General Services Administration in the US. He is also active in the development of Communities of Practice exploiting the emergence of WEB 2.0 social computing environments. As one of the first applications he is developing a CoP in healthcare design.
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    Uncertainty Analysis in Using Markov Chain Model to Predict Roof Life Cycle Performance
    (Georgia Institute of Technology, 2005) Zhang, Yan ; Vidakovic, Brani ; Augenbroe, Godfried
    Making decisions on building maintenance policies is an important topic in facility management. To evaluate different maintenance policies and make rational selection, both performance and maintenance cost of building components need to be of concern. For roofing sytem Markov Chain model has been developed to simulate the stochastic degrading process to evaluate the life cycle perfornance and cost. [Van Winden and Dekker 1998; Lounis et al. 1999] Taking value in a discrete state space, this model is especially appropriate when scaled rating regular inspections and related mainteance policies are implemented in large organizations. [Van Winden and Dekker 1998] However, many parameters in this Markov Chain model are associated with variance of significant magnitude. The propagation of these variances through the model will result in uncertainties in predicted life cycle performance and cost results. Without a solid uncertainty analysis on the simulation, decisions based on these simulation results can be unrealiable. In this paper we provide methods to estimate the range of parameter values and represent them in a probabilistic framwork. Monte Carlo method is used to analyze simulation output (life cycle cost and performance) variance propagated from these parameters through the model. These probablisitc informnation can be used to make better informed decisions. An example is provided to illustrate the Markov Chain model development, parameter identification method, Monte-Carlo uncertainty assessment and decision making with probabilistic information. It is shown that the uncertainty propagating through this process is not negligible and may significantly influence or even change the final decision