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- Creators: Arizona State University
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 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
Description
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
Description
Canal oriented development (COD) is a placemaking concept that aims to create mixed use developments along canal banks using the image and utility of the waterfront as a natural attraction for social and economic activity. COD has the potential to for landlocked cities, which are lacking a traditional harbor, to pursue waterfront development which has become an important economic development source in the post-industrial city. This dissertation examines how COD as a placemaking technique can and has been used in creating urban development. This topic is analyzed via three separate yet interconnecting papers. The first paper explores the historical notion of canals as an urban economic development tool with particular attention paid to the Erie Canal. The second paper explores the feasibility of what it would take for canal development to occur in the Phoenix region. The third and final paper explores the importance of place in urban design and the success or nonsuccess of COD as a place maker through the examination of three different CODs.
ContributorsBuckman, Stephen Thomas (Author) / Talen, Emily (Thesis advisor) / Ellin, Nan (Committee member) / Crewe, Katherine (Committee member) / Arizona State University (Publisher)
Created2013
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 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
Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to reduce cost and increase reliability and transparency. As a great amount of efforts have been devoted to these two initiatives by either exploring the smart grid designs or developing technologies for smart buildings, the research studying how the smart buildings and smart grid coordinate thus more efficiently use the energy is currently lacking. In this dissertation, a "system-of-system" approach is employed to develop an integrated building model which consists a number of buildings (building cluster) interacting with smart grid. The buildings can function as both energy consumption unit as well as energy generation/storage unit. Memetic Algorithm (MA) and Particle Swarm Optimization (PSO) based decision framework are developed for building operation decisions. In addition, Particle Filter (PF) is explored as a mean for fusing online sensor and meter data so adaptive decision could be made in responding to dynamic environment. The dissertation is divided into three inter-connected research components. First, an integrated building energy model including building consumption, storage, generation sub-systems for the building cluster is developed. Then a bi-level Memetic Algorithm (MA) based decentralized decision framework is developed to identify the Pareto optimal operation strategies for the building cluster. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity. Secondly, a multi-objective PSO based decision framework is developed to reduce the computational effort of the MA based decision framework without scarifying accuracy. With the improved performance, the decision time scale could be refined to make it capable for hourly operation decisions. Finally, by integrating the multi-objective PSO based decision framework with PF, an adaptive framework is developed for adaptive operation decisions for smart building cluster. The adaptive framework not only enables me to develop a high fidelity decision model but also enables the building cluster to respond to the dynamics and uncertainties inherent in the system.
ContributorsHu, Mengqi (Author) / Wu, Teresa (Thesis advisor) / Weir, Jeffery (Thesis advisor) / Wen, Jin (Committee member) / Fowler, John (Committee member) / Shunk, Dan (Committee member) / Arizona State University (Publisher)
Created2012
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 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
Description
Cities are experiencing rapid warming due to the urban heat island (UHI) effect, which causes the city center to have higher air temperatures than the surrounding rural areas. This dissertation studies the effects of building design on the surrounding environment, particularly for heat release.The first paper in this dissertation (Chapter 2) quantifies the anthropogenic heat emissions from buildings and focuses on an archetype office building, the study is considering four U.S. cities with different climates. The results demonstrate that the building envelope is the main contributor to heat emission from a building, accounting for over 60% of the total heat emission in all cities for four-story buildings. Additionally, the study finds that substituting bare terrain with a constructed building increases sensed heat by more than 70% in all cities and building heights. The second paper (Chapter 3) of this dissertation identifies the key design variables that affect heat emissions and energy consumption in buildings. The study considers 15 U.S. cities that represents all 15 climate zones as defined by American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). 10 design variables known for their impacts on energy consumption were identified via a literature review and used in the analysis. The results show that the window-to-wall ratio (WWR) consistently has a strong correlation with energy consumption in all climate zones. Roof and wall solar reflectance variables showed a very strong correlation with heat emissions from a building. The final paper of this dissertation (Chapter 4) presents the results of a survey distributed to experts in the architectural field, to evaluate the importance of different design variables that are related to heat emission and energy consumption. The survey also assessed the importance of considering heat emission as a design criterion during the design process when compared to energy consumption. These survey results provide new insights into how heat emission can be incorporated into the early design process. The dissertation then highlights the difference found via the survey results from the expert with the simulation results to identify the key design variable that relates to both heat emission and energy consumption.
ContributorsAlhazmi, Mansour (Author) / Yeom, Dongwoo (Thesis advisor) / Sailor, David (Committee member) / Sanguinetti, Paola (Committee member) / Arizona State University (Publisher)
Created2023
Description
Phase change materials (PCMs) are combined sensible-and-latent thermal energy storage materials that can be used to store and dissipate energy in the form of heat. PCMs incorporated into wall-element systems have been well-studied with respect to energy efficiency of building envelopes. New applications of PCMs in infrastructural concrete, e.g., for mitigating early-age cracking and freeze-and-thaw induced damage, have also been proposed. Hence, the focus of this dissertation is to develop a detailed understanding of the physic-chemical and thermo-mechanical characteristics of cementitious systems and novel coating systems for wall-elements containing PCM. The initial phase of this work assesses the influence of interface properties and inter-inclusion interactions between microencapsulated PCM, macroencapsulated PCM, and the cementitious matrix. The fact that these inclusions within the composites are by themselves heterogeneous, and contain multiple components necessitate careful application of models to predict the thermal properties. The next phase observes the influence of PCM inclusions on the fracture and fatigue behavior of PCM-cementitious composites. The compliant nature of the inclusion creates less variability in the fatigue life for these composites subjected to cyclic loading. The incorporation of small amounts of PCM is found to slightly improve the fracture properties compared to PCM free cementitious composites. Inelastic deformations at the crack-tip in the direction of crack opening are influenced by the microscale PCM inclusions. After initial laboratory characterization of the microstructure and evaluation of the thermo-mechanical performance of these systems, field scale applicability and performance were evaluated. Wireless temperature and strain sensors for smart monitoring were embedded within a conventional portland cement concrete pavement (PCCP) and a thermal control smart concrete pavement (TCSCP) containing PCM. The TCSCP exhibited enhanced thermal performance over multiple heating and cooling cycles. PCCP showed significant shrinkage behavior as a result of compressive strains in the reinforcement that were twice that of the TCSCP. For building applications, novel PCM-composites coatings were developed to improve and extend the thermal efficiency. These coatings demonstrated a delay in temperature by up to four hours and were found to be more cost-effective than traditional building insulating materials.
The results of this work prove the feasibility of PCMs as a temperature-regulating technology. Not only do PCMs reduce and control the temperature within cementitious systems without affecting the rate of early property development but they can also be used as an auto-adaptive technology capable of improving the thermal performance of building envelopes.
The results of this work prove the feasibility of PCMs as a temperature-regulating technology. Not only do PCMs reduce and control the temperature within cementitious systems without affecting the rate of early property development but they can also be used as an auto-adaptive technology capable of improving the thermal performance of building envelopes.
ContributorsAguayo, Matthew Joseph (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Underwood, Benjamin (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2018
Description
Lighting systems and air-conditioning systems are two of the largest energy consuming end-uses in buildings. Lighting control in smart buildings and homes can be automated by having computer controlled lights and window blinds along with illumination sensors that are distributed in the building, while temperature control can be automated by having computer controlled air-conditioning systems. However, programming actuators in a large-scale environment for buildings and homes can be time consuming and expensive. This dissertation presents an approach that algorithmically sets up the control system that can automate any building without requiring custom programming. This is achieved by imbibing the system self calibrating and self learning abilities.
For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.
For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.
The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.
For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.
For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.
The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.
ContributorsWang, Yuan (Author) / Dasgupta, Partha (Thesis advisor) / Davulcu, Hasan (Committee member) / Huang, Dijiang (Committee member) / Reddy, T. Agami (Committee member) / Arizona State University (Publisher)
Created2015
Description
In the United States, buildings account for 20–40% of the total energy consumption based on their operation and maintenance, which consume nearly 80% of their energy during their lifecycle. In order to reduce building energy consumption and related problems (i.e. global warming, air pollution, and energy shortages), numerous building technology programs, codes, and standards have been developed such as net-zero energy buildings, Leadership in Energy and Environmental Design (LEED), and the American Society of Heating, Refrigerating, and Air-Conditioning Engineers 90.1. However, these programs, codes, and standards are typically utilized before or during the design and construction phases. Subsequently, it is difficult to track whether buildings could still reduce energy consumption post construction. This dissertation fills the gap in knowledge of analytical methods for building energy analysis studies for LEED buildings. It also focuses on the use of green space for reducing atmospheric temperature, which contributes the most to building energy consumption. The three primary objectives of this research are to: 1) find the relationship between building energy consumption, outside atmospheric temperature, and LEED Energy and Atmosphere credits (OEP); 2) examine the use of different green space layouts for reducing the atmospheric temperature of high-rise buildings; and 3) use data mining techniques (i.e. clustering, isolation, and anomaly detection) to identify data anomalies in the energy data set and evaluate LEED Energy and Atmosphere credits based on building energy patterns. The results found that buildings with lower OEP used the highest amount of energy. LEED OEP scores tended to increase the energy saving potential of buildings, thereby reducing the need for renovation and maintenance. The results also revealed that the shade and evaporation effects of green spaces around buildings were more effective for lowering the daytime atmospheric temperature in the range of 2°C to 6.5°C. Additionally, abnormal energy consumption patterns were found in LEED buildings that used anomaly detection methodology analysis. Overall, LEED systems should be evaluated for energy performance to ensure that buildings continue to save energy after construction.
ContributorsKim, Jonghoon (Author) / Ariaratnam, Samuel T (Thesis advisor) / Chong, Oswald W (Committee member) / Bearup, Wylie K (Committee member) / Arizona State University (Publisher)
Created2016