Doctor of Philosophy with a Major in Architecture

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Now showing 1 - 10 of 33
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    (Georgia Institute of Technology, 2021-05-05) Althobaiti, Mohanned Mutlaq M.
    As building performance is increasingly improved and building energy consumption decreases, a greater percentage of the total energy loss of a building occurs through envelope leakage. This leakage is characterized by the effective leakage area or ELA, which is a proxy parameter to what is essentially a complex flow phenomenon through cracks driven by pressure differences. Moreover, different façades and façade parts have different ELA and are typically subjected to different pressure differences in a given wind condition. This poses major challenges to building energy models. Current building performance simulation (BPS) uses software modules that approximately calculate envelope infiltration, but the literature shows that their calibration and validation is still unsatisfactory. In fact, calibration and validation of BPS models is still an important subject of study in our quest to improve the fidelity of simulation-based predictions in various applications. The high level of interaction and subsumption between parameters can result in a model that approximates the measurements well (and thus meets the ASHRAE auditing threshold) but whose “best estimates” of parameters are unreliable. This can be a problem in performance contracting when limits have been agreed on certain parameters such as ELA and U-value. It can also be problematic in the use of the model for certain performance assessments. This thesis exemplifies the underlying issues by comparing the results of direct and indirect calibration at different fidelities. The study focuses on the calibration of building energy models of existing buildings. It does so by conducting calibration for different experiments, i.e., for different sources of data, and for different model fidelities. The calibration is anchored around ELA and its impact on “best estimates” of other parameters is verified. The study is done with explicit quantification of uncertainties in the experiments as well as in model parameters. The two major experiments considered are (a) direct ELA calibration through tracer gas experiments, (b) indirect ELA calibration with consumption data enhanced by spot temperature measurements. Two case studies on existing buildings are performed. The thesis develops a new framework to address calibration and validation for different combinations of data and model fidelity, where each combination leads to probability distributions of the calibration parameter set. For each combination the ultimate aim is to determine the fitness of the resulting building energy model for given application studies such as building energy benchmarking, fault detection, unmet hour verification, etc. This requires the introduction of a novel fitness measure that determines the confidence level of a particular calibrated model for decisions in a predefined building performance assessment scenario. The thesis shows an early example of how to develop and quantify fitness. The results will be meaningful for better understanding façade infiltration, better understanding of the limits of calibrated models, and the way this translates into fitness of the resulting model. The thesis focuses exclusively on existing buildings, but its findings may lead to large scale data sets of calibrated ELA values in existing buildings, that may find their way into better ELA quantification in energy models of new designs.
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    (Georgia Institute of Technology, 2020-12-08) Rajput, Mayuri
    The application of building simulation serves to assess the performance of a building throughout its lifetime. But the proper use of these applications relies heavily on the boundary conditions under which the behavior of a model is simulated. One of the most important inputs for simulation models is the stimulus by the weather conditions (actual or typical) in which it is supposed to operate. Traditionally, weather data for building simulation is a composed of 8760 hourly values of weather variables (temperature, humidity, solar insolation etc.) derived through statistical means from historical weather data acquired conventionally from remote (usually airport) weather stations. The derived data is taken to represent a typical weather year for a city. However, due to rapid increase in urbanization, weather in city centers with high urban density is significantly different from rural areas, a large part of which is due to localized effects, e.g. urban heat islands, increased albedo of man-made surfaces and anthropogenic emissions. This thesis investigates the relative importance of spatial weather variability in predicted building performance simulation outcomes. Ranking the importance cannot be looked at in isolation but needs to be determined relative to all other sources of uncertainty, predominantly in the parameters of the energy model which in this thesis is EnergyPlus. The latter stem from lack of information or ignorance about many physical and scenario of use parameters. Together they are the ensemble of sources of uncertainties that need to be recognized in any simulation. A sensitivity analysis is conducted to reveal their relative ranking. An inspection of the resulting rank of the effect of spatial weather variability reveals whether the knowledge of local weather, in contrast to the assumption of uniform weather throughout the city, significantly reduces the overall uncertainty in the outcomes of the simulation. It should be recognized that there is only limited availability of localized weather data that reflect variability of urban contexts throughout a city. This recognition leads to the first contribution of this thesis: the development of a high fidelity statistical urban weather model fitted on local urban morphology and recorded weather. This is accomplished with a Multiple Tensor on Tensor (MTOT) regression model. The model can be applied universally and enables building modelers to create synthetic meso scale weather data for their site, essentially putting the individual building in the urban fabric of the city. The resulting model is a new cornerstone in the uncertainty analysis of the building simulation with inclusion of spatial weather variability. It is consequently used to inspect the role of spatially diverse weather in two critical applications. First, at the single building scale it is verified in three applications whether spatially diverse weather plays an important role when the assessment is conducted for a non-specific location in the city. Secondly, the role of spatial variability is tested in a three urban decision making cases where the question is answered whether decisions should be diversified per location. The thesis offers answers to both questions that elevate our understanding of the role of meso scale weather information in building simulation practice.
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    Building thermal load control: Potential, Strategy, and Implementation
    (Georgia Institute of Technology, 2020-12-08) Lu, Di
    The HVAC system consumes 30-50% of the energy delivered to a building, providing heating and cooling to maintain suitable thermal conditions for occupants. In recent years, advanced control methods, such as model predictive control (MPC), are being studied to lower building energy cost (e.g., by deferring consumption to low rate hours of the day) while still satisfying comfort requirements to an acceptable degree. Two main research gaps are identified from the literature on MPC and human thermal comfort. First, zonal control flexibility employed by MPC in terms of thermal requirements is not well defined. Second, confusion persists about the contribution of MPC vis a vis other energy conservation methods. These two research gaps weaken the acceptance of existing models and thereby frustrate the real-life application of MPC. The objective of the undertaken research is to analyze the potential, strategy, and implementation of thermal load control with the aim to quantify its ability to minimize the operation cost of HVAC systems. This is achieved in five consecutive steps, 1) understanding zonal control flexibility, 2) evaluating the potential of building thermal load control with zonal control flexibility, 3) analyzing the potential for varying climate zones and construction types, 4) investigating the performance of MPC under scenario uncertainties, and 5) developing a thermal load control strategy that is ready for implementation. In each step, a mathematical formulation of the optimal control problem is formulated and consequently solved by appropriate algorithms. A novel comfort tolerance model for occupant cohorts is developed and implemented as constraints on the control envelope. The research outcomes expand the understanding of the multiple aspects of building thermal load control.
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    Uncertainty quantification of building energy model that assume ideal temperature control
    (Georgia Institute of Technology, 2020-08-17) Shi, Yifu
    An increasing number of studies conduct uncertainty analyses to investigate discrepancies between predicted energy performance of buildings and their actual measured energy use. Based on prior uncertainty quantification studies, there is evidence that there remain unquantified uncertainties related to the HVAC system. Most current studies of HVAC system uncertainties focus on investigating the probabilistic nature of building thermal loads and assume this nature to be the key factor to impact the accuracy of performance predictions of the HVAC system. To verify this one has to acknowledge that instead of reacting ideally to the “thermal load”, HVAC systems sense space temperature and use it as the control state in HVAC control loops, thus deciding on the heating or cooling requirement of a space based on sensed space temperature. For a VAV terminal box which serves multiple spaces, temperature controllability only applies to the space where a thermostat is installed. The temperature in other spaces may consequently not be maintained and the delivered cooling/heating from the terminal box will typically not (fully) satisfy the removal or supply of the room cooling and heating load. Commonly used EnergyPlus simulations introduce an idealization on the space temperature controllability by matching building zone partition with the HVAC supply network topology and using thermal zone as the atomic control object. This thesis targets an uncertainty quantification approach to identify model form uncertainties in EnergyPlus stemming from such idealized space temperature controllability. A high-fidelity co-simulation model which integrates an EnergyPlus building energy model with a Modelica HVAC system model is developed as the high-fidelity reference model. The differences between outcomes of the EnergyPlus simulation and outcomes of the reference model are then established. Two new characteristic parameters, “spatial-HVAC mismatch” and “occupant load diversity”, are introduced in this thesis. The first defines the area of non-sensed spaces in relation to directly controlled areas where the space temperature is sensed. Occupant load diversity expresses the variabilities of occupancy related load profiles in each space. The uncertainty analysis of the impact of the idealized temperature control of the EnergyPlus representation of VAV system considering the stochastic usage pattern of occupants in two space functions with five alignment configurations in three boundary situations focusing on the risk of underestimating energy consumption and over estimating occupant comfort (unmet hours in particular). The thesis quantifies the differences between low and high fidelity predictions in the outcomes of space air temperature, cooling energy in different time interval (hourly, daily, and monthly), fan power, and unmet hours as a result of the idealizations used in routine EnergyPlus simulations. It then correlates them with the mismatch and load diversity factors introduced above. Based upon the uncertainty analysis, this study explores the characterizations of the results from the case studies and discuss the methodologies and steps for “post corrections” or MFU inclusion in the low fidelity model by using fan power as an example. The research outcomes generate significant knowledge to the understanding of the origins of building energy model deficiency generated by idealization assumptions about temperature control and how it contributes to the performance gap.
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    Rational decision making in early urban design based on uncertain performance predictions
    (Georgia Institute of Technology, 2020-03-06) Hashim, Alya Abdul Sattar Yaqoob
    The world is currently undergoing the largest wave of urban growth in human history. More than half of the global population is now concentrated in urban areas, and by 2060 `two third of the expected population of 10 billion will live in cities. While accommodate this tremendous growth, reducing urban energy consumption of resilient and livable cities should be seen as associated priorities. Meeting these priorities head on requires complex decision-making at the early phase of urban design, when a large number of parameters are still undecided, and their level of uncertainty is high. The thesis proposes a rational decision framework that responds to these challenges for a specific set of measures within the following limited scope: energy efficiency in urban layout, indoor daylight level, network connectivity, outdoor public space visibility and thermal comfort. The early stage of urban design is characterized by its iterative nature of repeated alternative generation (divergent phase), and alternative assessment and selection (convergent phase). Decision making occurs during or at the end of these phases with considerable uncertainty in the many as yet unresolved design parameters. Therefore, methods and tools applied during these phases should account for the iterative and unpredictable nature of later design evolution. Currently there is no consistent support for rational decision making at the early stage of urban design. Typically, single deterministic predictions are generated based on assumed parameter values when in fact many of those parameters have not been decided yet. This dissertation starts from a hierarchical structure that outlines consecutive steps in the design process by geometric output type. This is not the main focus of the thesis but merely a structuring principle that is employed by the rational decision framework. This framework supports the comparative assessment of competing design alternatives under uncertainty. This is the main focus of the research. It introduces explicit information about uncertainty in undecided design parameters and analyzes their effects on the confidence with which one design variant can be prioritized over another. The approach is implemented in a Rhino-Grasshopper platform for five concrete performance measures: network connectivity, visibility in open space, outdoor thermal comfort, building energy consumption and daylight utilization. Low-resolution simulation models are developed for each of these measures to service the iterative nature of design with fast computation of results. The resulting models serve as normative substitutes for more accurate physics-based prediction models. The research has developed a systematic verification approach showing when these reduced order models are indeed as adequate for the targeted comparative analysis in early design as their high-fidelity counterparts. In the comparative analyses of design variants, point values of inputs are replaced with probability distributions that quantify the expected variability (treated as design uncertainty) in later decided design variables using a Monte Carlo technique. Hence each generated outcome is a probability distribution that represents the uncertainty in the performance prediction of a design alternative under study. The performance predictions are the inputs into the decision making allowing the designer to make a rational choice of one design alternative over a competing one. In the developed framework such decisions are driven by minimum required confidence levels that a decision maker is comfortable with when prioritizing a variant. As an associated issue the research tested the effectiveness of current rules of thumb and found that design choices that they suggest typically fall short of the confidence level required by the decision maker. This dissertation introduces the methodology, the development of a framework for comparative analysis with embedded normative models (implemented as grasshopper components) and their execution in the current prototype.
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    Building thermoregulation based on the adaptive building envelope
    (Georgia Institute of Technology, 2020-01-09) Zeng, Zhaoyun
    In contrast to the traditional building envelope, which tries to block the thermal and mass exchange between the indoor and outdoor environments as much as possible, the adaptive building envelope (ABE) can admit the favorable environmental factors while block the adverse ones to reduce the building load as well as improve the thermal and visual comfort of the occupants. This thesis is intended to facilitate the research of ABE by (1) Clarifying the definition of ABE and offer conceptualizations that are important to the research. (2) Investigating the energy saving potential of ABE technologies by associating these technologies with four weather variables. The results of this investigation can be used in the selection of ABE technologies. (3) Summarizing the existing modelling methods for ABE technologies. If the modelling methods for certain ABE technologies do not exist, they will be developed in this thesis. (4) Reviewing and categorizing the optimization approaches adopted in previous studies on ABE. Recommendations are also made for choosing the appropriate optimization approaches in different application scenarios. (5) Developing a generic optimization framework for ABE that can guide the formulation of optimization problems in different application scenarios. (6) Conducting three application studies that can enrich the optimization framework and serve as paradigms for using the optimization framework in different application scenarios.
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    Integrated assessment of buildings and distributed energy resources (DER) at the neighborhood scale
    (Georgia Institute of Technology, 2017-11-13) Carneiro, Gustavo Antonio
    In urban regions, traditionally a main electric grid fed by centralized power plants serves the growing energy demand of residential and commercial buildings. However, the advent of new technologies, such as distributed renewable energy generation, local energy storage, and smart controls, is transforming the way buildings interact and transact with the electric grid. When operating in coordination, several buildings or households can leverage their aggregate potential and use their energy flexibility and distributed resources to improve the operation of both the main grid and the pool of integrated and intelligent buildings. Much attention has been drawn to the potential benefits of these types of integration, especially the capabilities they can provide in terms of aggregate demand management and local power resilience. Nevertheless, building energy modeling at the urban level has not yet reached the necessary computational manageability and simulation robustness to assess these novel scenarios. To address this hiatus, the current thesis presents a computer-aided energy simulation method to model the integration of multiple buildings and distributed energy resources (DER) at the neighborhood scale. The proposed methodology uses a reduced order simulation approach to achieve a reliable and tractable dynamic modeling framework that can manage multiple transacting building energy models and DER models in a single platform. To test the modeling approach, this study first carries out a virtual experiment of a small community in Miami, FL, where it is possible to compare the outcomes of community energy consumption from our reduced order model to the outcomes from a higher order simulation approach. When using the community energy model to evaluate the performance of different DER options for community peak load shaving, we can observe that the influence of the model order reduction reveals to be very minor when compared to other uncertainties related to scenario variability and, especially, systems’ efficiencies. Secondly, we apply the reduced order modeling approach to an existing residential community in Rancho Cordova (Sacramento County), CA, with solar energy generation and battery energy storage. With this case study, we demonstrate the viability of our approach to construct and calibrate a reduced order model of fifteen households based only on limited and general data related to energy performance of the entire neighborhood. The developed reduced order model is used to evaluate the performance of different energy storage arrangements for reducing the occurrence of community super peak loads. In this virtual experiment, we can demonstrate how the model allows for uncertainty analyses over the influence of input parameters, as well as for more sophisticated optimization studies, including stochastic optimization, in a timely and transparent fashion. Finally, the proposed reduced order simulation approach is used to construct and test relevant energy performance measures at the neighborhood scale. Using the model unique features of manageability, reliability and flexibility, we propose the foundations for quantifying and measuring “community energy resilience” for outage situations, based on concepts of number of sustained hours and respective energy end-use convenience levels. We also measure and monetize DER options for providing “community energy flexibility”, aimed at shaping the load profile of a residential community to match the electric grid needs.
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    Addressing data informativeness in risk-conscious building performance simulation applications
    (Georgia Institute of Technology, 2017-08-02) Li, Qi
    Building performance management remains an important aspect in reducing building energy consumption and enhancing occupants’ thermal comfort and work productivity. Recent decades witnessed the maturity and proliferation of numerous methods, software and tools that span the whole spectrum of common building performance management practice. Among those related research and applications, the use of information and data in calibration and validation of building performance simulation (BPS) models constitutes an important subject of study especially in fault detection, operations management, and retrofit analysis. An extensive review of BPS model calibration and validation studies reveals two major research gaps. First, contemporary model calibration practice calls for an effective and robust method that can systematically incorporate a variety of information and data, handle modelling and prediction uncertainties, and maintain consistent model performance. Second, current approaches to collecting information and data in real practice largely depend on individual experience or common practice; further study is needed to understand the value of information and data, i.e. assess data informativeness, such as to support specific decision-making processes in choosing data monitoring strategies and to avoid missed opportunities or wasted resources. To this end, this dissertation develops a new framework to address data informativeness in model calibration and validation to answer two major research questions: 1) how to make optimal use of available information and data to calibrate a building simulation model under uncertainty, and 2) how to quantify the informativeness of information and data for risk-conscious building performance simulation applications. This framework builds upon uncertainty propagation using detailed measurements, and inverse modelling using Bayesian inference. It introduces probabilistic performance metrics to assess model prediction consistency and quantify data informativeness. Following an explanation of the framework’s theoretical soundness, this dissertation provides two case studies to demonstrate its practical effectiveness. The first is a controlled experiment in the Flexlab test facility at Lawrence Berkeley lab. A new validation methodology is proposed to validate a simulation model under uncertainty, in which the validation criteria build upon the introduced probabilistic performance metrics. Given the experiment setup, uncertainty propagation based on synthetic measurements is applied, which effectively improves prediction agreement and reduces the risk of accepting invalid simulation outcomes. The second is to determine the appropriate model form and metering data for a hypothetical intervention analysis of an existing building with hydronic heating on the Cambridge, UK campus. A three-level modelling method is proposed to enable modelling all thermal processes occurring in individual rooms while efficiently modelling the whole building to estimate heating system performance. Different sets of metering data are then used to calibrate the physical model, and the result indicates the superiority of Bayesian inference in exploiting the value of data, the necessity of room temperature and electricity monitoring under uncontrolled conditions, and the potential of daily metering data for calibration in real building performance management practice.
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    Optimal strategies for demand charge reduction by commercial building owners
    (Georgia Institute of Technology, 2017-05-23) Zhang, Yuna
    A substantial part of electricity bills in various types of commercial buildings, such as office buildings, hospitals and retails can consist of demand charges. Demand charges represent the penalty for an electricity consumer levied by the utility provider. They are typically a direct result of the shape of the power duration curve, in particular, the hours that a certain power level is exceeded in a given billing period (normally a month). Lowering the peak and/or reducing the hours that a power threshold is exceeded can drastically reduce demand charges. The ability to do so by dynamic, operational adjustments reflects the “energy flexibility” of the building. This term is now widely used in Europe and is the subject of a new international effort (IEA Annex 67) in this area. This thesis targets the optimal choice among design and operational measures in a retrofit or new design project that delivers the most effective way of reducing demand charges and increasing energy flexibility of commercial buildings. This goal will be achieved through an analysis of all feasible energy and peak reduction measures in different building types and in different use contexts. A search algorithm that compares all possible interventions will deliver the optimum, first with a deterministic analysis then with the recognition of the effects of all possible sources of uncertainty. This thesis evaluates the measures that are commonly adopted to decrease energy consumption and increase energy flexibility and thus reduce demand charges, including (1) upgrading building components and installing energy efficient equipment; (2) applying dynamic building load control strategies such as demand-side management; (3) installing a rooftop photovoltaic (PV) panel array. Operational interventions include the manipulation of thermostat settings and possibly the voltage reduction of lighting and appliances (in some cases including HVAC components) in the building, which may reduce thermal and visual comfort for certain periods. In order to support retrofit and design improvement decisions, an approach is developed that finds the optimal mix of measures that maximize the net present value of the investment in energy flexibility measures over twenty years for the owner. This study will analyze optimal solutions for three commercial building types. Differences between them in terms of energy use and peak demand will be investigated and a generically applicable measure of energy flexibility will be developed. These three buildings are chosen (by scaling their total floor area) such that their demand charges are in the same range. The monetary benefit of energy flexibility will be studied under different demand charge rate structures and under variable building consumption scenarios. This research will result in a new optimization framework for choosing the optimum among multiple options. Based on the proposed framework, this research will determine optimal ways to increase energy flexibility, leading to the best investment decisions for different commercial building types in different locations and under different rate structures.
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    Integrated performance based design of communities and distributed generation
    (Georgia Institute of Technology, 2016-08-26) Street, Michael
    The vertically integrated utility market within the U.S. is undergoing rapid changes due to the rise of small-scale distributed power generation known as microgrids, which are local networks of power generation and distribution typically serving a demand less than 40 MW. Primary drivers for microgrid investment are the performance benefits these systems return to their owners, which include increased reliability, reduced emissions and reduced operating costs. We define a novel modeling methodology to represent the microgrid as an integrated system of the demand and supply. Previous work to develop an integrated system model does not adequately model the building thermal demand, incorporate a modeler’s knowledge of the grid’s availability or allow for a user to model their tolerance for unmet demand. To address these modeling issues, we first demonstrate a technique for representing a building stock as a reduced order hourly demand model. Next, as demand side measures are typically defined at the building level as discrete options, we demonstrate a technique for converting a large discrete optimization problem into a simplified continuous variable optimization problem through the use of Pareto efficient cost functions. The reduced problem specification results in 90% fewer function evaluations for a benchmark optimization task. Then, we incorporate two new features into the Distributed Energy Resource Customer Adoption Model (DER-CAM) developed by Lawrence Berkeley National Laboratory (LBNL) that allow users to define grid outage scenarios and their limit of expected energy demand not served. Applying the integrated model to a microgrid design scenario return solutions that exhibit on average an 8% total annual cost reduction and 18% reduction in CO2 emissions versus a Supply Only case. Similarly, the results on average reduce total annual cost by 5% and annual emissions by 17% for a Demand First case. In summary, we present a modeling methodology with application to joint decision making that involve renewable power supply, building systems and passive building design measures and recommend this model for performance based microgrid design.