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College of Design Research Forum

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Item Type: Publication × Start date: 2010 × End date: 2019 × Author: Ashuri, Baabak ×

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    Decision Analytics in Design and Construction
    (Georgia Institute of Technology, 2019-09-26) Aflatoony, Leila ; Ashuri, Baabak ; Bartlett, Chris M. ; Rakha, Tarek
    Decision analytics stands to have a profound impact on how design and construction disciplines are woven together to solve today's most complex problems. Rigorous data collection and analysis are core to design and construction decision making. The nature of analysis is to study complexity and deduce a reasonable summary that will then inform design and construction decisions. Decision analytics is distinguished from analysis by the emphasis on causality and prediction. The proliferation of computing power and access to rich data sets has driven innovation in the analytics tools market, lowering the barrier for entry to powerful analytics tools for designers and constructors. This means that decision-makers can more accurately identify causality and leverage the predictive power of analytics to inform design and construction decisions that anticipate and solve for problems much further into the future. Opportunities are growing to align decision analytics across multiple disciplines to minimize economic waste, maximize energy efficiencies, and enhance the lives of individuals and communities. An intuitive example of this opportunity lies in new building design and construction. Construction Analytics is a distinctive discipline, bridging the fields of building construction, civil and environmental engineering, economics, and operations research. Designers and decision-makers use descriptive analytics to identify indicators to cost overruns, diagnostic analytics to predict construction market resiliency after natural disasters, predictive analytics to identify future building trends, and prescriptive analytics to optimize resource allocation during construction projects. Building performance analytics explores various performance measures linked to building energy investigations, including measuring existing building performance through detailed audits to achieve substantial energy savings in deteriorating infrastructures, as well as simulating and visualizing new building and urban energy-flows to formulate informed design decisions empowered by data analytics for a sustainable and energy efficient future. In the example of new hospital construction, human-centered analytics can produce powerful insights and unlock empathy for the people (pediatric doctors, nurses, patients) who actively use the hospital space. Merging and visualizing several sources of quantitative and qualitative data draws out causality and enables predictive decision making aimed at improving the experience and performance of the people using the space.
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    Pricing Flexibility in Solar Ready Homes
    (Georgia Institute of Technology, 2011-11-17) Ashuri, Baabak ; Irizarry, Javier ; Riether, Gernot
    Solar energy technologies, i.e., Photovoltaic (PV) panels, have promising features for renewable energy generation (i.e., energy savings) and greenhouse gas (GHG) emission reduction in the housing sector. Nevertheless, adopting these PV technologies requires substantial initial investments. The market for these technologies is often vibrant from the technological and economic standpoints. Therefore, investors typically find it more attractive to delay investment in PV technologies. Alternatively "Solar Ready Homes" are proposed. These flexible homes can easily adopt PV technologies later in future when the price of PV panels is lower, electricity energy price is higher, and stricter environmental regulations are in place. The investors need proper financial valuation models in order to avoid over- and under-investment in solar technologies. We apply Real Options Theory to evaluate investments in Solar Ready Homes. Our proposed investment analysis framework uses a probability distribution model to empirically characterize uncertainty about the performance of PV panels. Uncertainty about future retail price of energy is characterized with a stochastic model. Our framework uses the experience curve to model changes in price and efficiency of PV technologies over time. Our investment valuation model identifies the optimal time to install PV panels in Solar Ready Homes. Our valuation model characterizes the investor's financial risk profile in two investments: "fixed" Solar Home and "flexible" Solar Ready Home. The optimal time for installing PV panels in solar ready home is identified. Our framework determines the price of flexibility embedded in solar ready home. In other words, it calculates the difference between the expected value of investment in solar ready home and the expected value of investment in solar home. (Ashuri)