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With the desire of high standards of comfort, huge amount of energy is being consumed to maintain the indoor environment. In US building consumes 40% of the total primary energy while residential buildings consume about 21%. A large proportion of this consumption is due to cooling of buildings. Deteriorating environmental

With the desire of high standards of comfort, huge amount of energy is being consumed to maintain the indoor environment. In US building consumes 40% of the total primary energy while residential buildings consume about 21%. A large proportion of this consumption is due to cooling of buildings. Deteriorating environmental conditions due to excessive energy use suggest that we should look at passive designs and renewable energy opportunities to supply the required comfort. Phoenix gets about 300 days of clear sky every year. It also witnesses large temperature variations from night and day. The humidity ratio almost always stays below the 50% mark. With more than six months having outside temperatures more than 75 oF, night sky radiative cooling promise to be an attractive means to cool the buildings during summer. This technique can be useful for small commercial facilities or residential buildings. The roof ponds can be made more effective by covering them with Band Filters. These band filters block the solar heat gain and allow the water to cool down to lower temperatures. It also reduces the convection heat gain. This helps rood ponds maintain lower temperatures and provide more cooling then an exposed pond. 50 μm Polyethylene band filter is used in this study. Using this band filter, roof ponds can be made up to 10% more effective. About 45% of the energy required to cool a typical residential building in summer can be saved.
ContributorsSiddiqui, Mohd. Aqdus (Author) / Bryan, Harvey (Thesis advisor) / Reddy, T Agami (Committee member) / Kroelinger, Michael D. (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Rapid urbanization in Phoenix, Arizona has increased the nighttime temperature by 5°C (9 °F), and the average daily temperatures by 3.1°C (5.6 °F) (Baker et al 2002). On the macro scale, the energy balance of urban surface paving materials is the main contributor to the phenomenon of the Urban Heat

Rapid urbanization in Phoenix, Arizona has increased the nighttime temperature by 5°C (9 °F), and the average daily temperatures by 3.1°C (5.6 °F) (Baker et al 2002). On the macro scale, the energy balance of urban surface paving materials is the main contributor to the phenomenon of the Urban Heat Island effect (UHI). On the micro scale, it results in a negative effect on the pedestrian thermal comfort environment. In their efforts to revitalize Downtown Phoenix, pedestrian thermal comfort improvements became one of the main aims for City planners. There has been an effort in reformulating City zoning standards and building codes with the goal of developing a pedestrian friendly civic environment. Much of the literature dealing with mitigating UHI effects recommends extensive tree planting as the chief strategy for reducing the UHI and improving outdoor human thermal comfort. On the pedestrian scale, vegetation plays a significant role in modifying the microclimate by providing shade and aiding the human thermal comfort via evapotranspiration. However, while the extensive tree canopy is beneficial in providing daytime shade for pedestrians, it may reduce the pavement surfaces' sky-view factor during the night, thereby reducing the rate of nighttime radiation to the sky and trapping the heat gained within the urban materials. This study strives to extend the understanding, and optimize the recommendations for the use of landscape in the urban context of Phoenix, Arizona for effectiveness in both improving the human thermal comfort and in mitigating the urban heat island effect.
ContributorsRosheidat, Akram (Author) / Bryan, Harvey (Thesis advisor) / Lee, Taewoo (Committee member) / Chalfoun, Nader (Committee member) / Arizona State University (Publisher)
Created2014
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Description
The green building movement has been an effective catalyst in reducing energy demands of buildings and a large number of `green' certified buildings have been in operation for several years. Whether these buildings are actually performing as intended, and if not, identifying specific causes for this discrepancy falls into the

The green building movement has been an effective catalyst in reducing energy demands of buildings and a large number of `green' certified buildings have been in operation for several years. Whether these buildings are actually performing as intended, and if not, identifying specific causes for this discrepancy falls into the general realm of post-occupancy evaluation (POE). POE involves evaluating building performance in terms of energy-use, indoor environmental quality, acoustics and water-use; the first aspect i.e. energy-use is addressed in this thesis. Normally, a full year or more of energy-use and weather data is required to determine the actual post-occupancy energy-use of buildings. In many cases, either measured building performance data is not available or the time and cost implications may not make it feasible to invest in monitoring the building for a whole year. Knowledge about the minimum amount of measured data needed to accurately capture the behavior of the building over the entire year can be immensely beneficial. This research identifies simple modeling techniques to determine best time of the year to begin in-situ monitoring of building energy-use, and the least amount of data required for generating acceptable long-term predictions. Four analysis procedures are studied. The short-term monitoring for long-term prediction (SMLP) approach and dry-bulb temperature analysis (DBTA) approach allow determining the best time and duration of the year for in-situ monitoring to be performed based only on the ambient temperature data of the location. Multivariate change-point (MCP) modeling uses simulated/monitored data to determine best monitoring period of the year. This is also used to validate the SMLP and DBTA approaches. The hybrid inverse modeling method-1 predicts energy-use by combining a short dataset of monitored internal loads with a year of utility-bills, and hybrid inverse method-2 predicts long term building performance using utility-bills only. The results obtained show that often less than three to four months of monitored data is adequate for estimating the annual building energy use, provided that the monitoring is initiated at the right time, and the seasonal as well as daily variations are adequately captured by the short dataset. The predictive accuracy of the short data-sets is found to be strongly influenced by the closeness of the dataset's mean temperature to the annual average temperature. The analysis methods studied would be very useful for energy professionals involved in POE.
ContributorsSingh, Vipul (Author) / Reddy, T. Agami (Thesis advisor) / Bryan, Harvey (Committee member) / Addison, Marlin (Committee member) / Arizona State University (Publisher)
Created2011
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Description
The Urban Heat Island (UHI) has been known to have been around from as long as people have been urbanizing. The growth and conglomeration of cities in the past century has caused an increase in the intensity and impact of Urban Heat Island, causing significant changes to the micro-climate and

The Urban Heat Island (UHI) has been known to have been around from as long as people have been urbanizing. The growth and conglomeration of cities in the past century has caused an increase in the intensity and impact of Urban Heat Island, causing significant changes to the micro-climate and causing imbalances in the temperature patterns of cities. The urban heat island (UHI) is a well established phenomenon and it has been attributed to the reduced heating loads and increased cooling loads, impacting the total energy consumption of affected buildings in all climatic regions. This thesis endeavors to understand the impact of the urban heat island on the typical buildings in the Phoenix Metropolitan region through an annual energy simulation process spanning through the years 1950 to 2005. Phoenix, as a representative city for the hot-arid cooling-dominated region, would be an interesting example to see how the reduction in heating energy consumption offsets the increased demand for cooling energy in the building. The commercial reference building models from the Department of Energy have been used to simulate commercial building stock, while for the residential stock a representative residential model prescribing to IECC 2006 standards will be used. The multiyear simulation process will bring forth the energy consumptions of various building typologies, thus highlighting differing impacts on the various building typologies. A vigorous analysis is performed to see the impact on the cooling loads annually, specifically during summer and summer nights, when the impact of the 'atmospheric canopy layer' - urban heat island (UHI) causes an increase in the summer night time minimum and night time average temperatures. This study also shows the disparity in results of annual simulations run utilizing a typical meteorological year (TMY) weather file, to that of the current recorded weather data. The under prediction due to the use of TMY would translate to higher or lower predicted energy savings in the future years, for changes made to the efficiencies of the cooling or heating systems and thermal performance of the built-forms. The change in energy usage patterns caused by higher cooling energy and lesser heating energy consumptions could influence future policies and energy conservation standards. This study could also be utilized to understand the impacts of the equipment sizing protocols currently adopted, equipment use and longevity and fuel swapping as heating cooling ratios change.
ContributorsDoddaballapur, Sandeep (Author) / Bryan, Harvey (Thesis advisor) / Reddy, Agami T (Committee member) / Addison, Marlin (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Learning from the anatomy of leaves, a new approach to bio-inspired passive evaporative cooling is presented that utilizes the temperature-responsive properties of PNIPAm hydrogels. Specifically, an experimental evaporation rate from the polymer, PNIPAm, is determined within an environmental chamber, which is programmed to simulate temperature and humidity conditions common in

Learning from the anatomy of leaves, a new approach to bio-inspired passive evaporative cooling is presented that utilizes the temperature-responsive properties of PNIPAm hydrogels. Specifically, an experimental evaporation rate from the polymer, PNIPAm, is determined within an environmental chamber, which is programmed to simulate temperature and humidity conditions common in Phoenix, Arizona in the summer. This evaporation rate is then used to determine the theoretical heat transfer through a layer of PNIPAm that is attached to an exterior wall of a building within a ventilated cavity in Phoenix. The evaporation of water to the air gap from the polymer layer absorbs heat that could otherwise be conducted to the interior space of the building and then dispels it as a vapor away from the building. The results indicate that the addition of the PNIPAm layer removes all heat radiated from the exterior cladding, indicating that it could significantly reduce the demand for air conditioning at the interior side of the wall to which it is attached.
ContributorsBradford, Katherine (Author) / Reddy, T A (Thesis advisor) / Bryan, Harvey (Thesis advisor) / Ramalingam, Muthu (Committee member) / Arizona State University (Publisher)
Created2018
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Description
The poor energy efficiency of buildings is a major barrier to alleviating the energy dilemma. Historically, monthly utility billing data was widely available and analytical methods for identifying building energy efficiency improvements, performing building Monitoring and Verification (M&V;) and continuous commissioning (CCx) were based on them. Although robust, these methods

The poor energy efficiency of buildings is a major barrier to alleviating the energy dilemma. Historically, monthly utility billing data was widely available and analytical methods for identifying building energy efficiency improvements, performing building Monitoring and Verification (M&V;) and continuous commissioning (CCx) were based on them. Although robust, these methods were not sensitive enough to detect a number of common causes for increased energy use. In recent years, prevalence of short-term building energy consumption data, also known as Energy Interval Data (EID), made available through the Smart Meters, along with data mining techniques presents the potential of knowledge discovery inherent in this data. This allows more sophisticated analytical tools to be developed resulting in greater sensitivities due to higher prediction accuracies; leading to deep energy savings and highly efficient building system operations. The research explores enhancements to Inverse Statistical Modeling techniques due to the availability of EID. Inverse statistical modeling is the process of identification of prediction model structure and estimates of model parameters. The methodology is based on several common statistical and data mining techniques: cluster analysis for day typing, outlier detection and removal, and generation of building scheduling. Inverse methods are simpler to develop and require fewer inputs for model identification. They can model changes in energy consumption based on changes in climatic variables and up to a certain extent, occupancy. This makes them easy-to-use and appealing to building managers for evaluating any general retrofits, building condition monitoring, continuous commissioning and short-term load forecasting (STLF). After evaluating several model structures, an elegant model form was derived which can be used to model daily energy consumption; which can be extended to model energy consumption for any specific hour by adding corrective terms. Additionally, adding AR terms to this model makes it usable for STLF. Two different buildings, one synthetic (ASHRAE medium-office prototype) building and another, an actual office building, were modeled using these techniques. The methodologies proposed have several novel features compared to the manner in which these models have been described earlier. Finally, this thesis investigates characteristic fault signature identification from detailed simulation models and subsequent inverse analysis.
ContributorsJalori, Saurabh (Author) / Reddy, T. Agami (Thesis advisor) / Bryan, Harvey (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2013
Description

Better methods are necessary to fully account for anthropogenic impacts on ecosystems and the essential services provided by ecosystems that sustain human life. Current methods for assessing sustainability, such as life cycle assessment (LCA), typically focus on easily quantifiable indicators such as air emissions with no accounting for the essential

Better methods are necessary to fully account for anthropogenic impacts on ecosystems and the essential services provided by ecosystems that sustain human life. Current methods for assessing sustainability, such as life cycle assessment (LCA), typically focus on easily quantifiable indicators such as air emissions with no accounting for the essential ecosystem benefits that support human or industrial processes. For this reason, more comprehensive, transparent, and robust methods are necessary for holistic understanding of urban technosphere and ecosphere systems, including their interfaces. Incorporating ecosystem service indicators into LCA is an important step in spanning this knowledge gap.

For urban systems, many built environment processes have been investigated but need to be expanded with life cycle assessment for understanding ecosphere impacts. To pilot these new methods, a material inventory of the building infrastructure of Phoenix, Arizona can be coupled with LCA to gain perspective on the impacts assessment for built structures in Phoenix. This inventory will identify the origins of materials stocks, and the solid and air emissions waste associated with their raw material extraction, processing, and construction and identify key areas of future research necessary to fully account for ecosystem services in urban sustainability assessments. Based on this preliminary study, the ecosystem service impacts of metropolitan Phoenix stretch far beyond the county boundaries. A life cycle accounting of the Phoenix’s embedded building materials will inform policy and decision makers, assist with community education, and inform the urban sustainability community of consequences.