Buildings energy consumption is rising continuously with massive urbanization progress, which then results in high greenhouse gas emission. A standing example is the urbanization process going on in the south-west part of China. Much has been discussed for improving building energy performance. However, to take another point of view, renewable energy source for buildings is a solution worth considering, for instance STES, which gains thermal energy from the sun, delivers it to buildings for space heating and hot tap water, also restores the solar energy in hot seasons in the storage system for the need of cold season.The aim of this paper is to couple the technology of STES with practical situation, explore the possibility of applying STES in south-west of China. This thesis work takes an estimation approach to weigh the possibility. The building project studied in this thesis is a campus project in the city of Guiyang, one of four major cities in the region of south-west China.Case study involves existing STES projects in Munich Germany and Anneberg Sweden, the performance evaluation of the Anneberg project is later to serve as an example in system gain & losses proportion, to guide the estimation work of the campus project.The estimation conclusion is drawn based on a cross-sectional analysis method, take the technology of STES, the practiced STES project and building projects in China as three loops visually, and observe how much they overlap each other. Behind the visual illustration, the overlapping is assessed with several factors, for instance possibility of storage system at location, possible STES performance and solar irradiation condition at site location etc. If most of these factors are checked to be “Ok” or “Good”, then the overlapping area is considered “large” enough, and therefore suggests a decent chance to implement STES system in the south-west China.A solar gain and sunlight simulation from a new police station energy consumption report assists in calculating the possible solar gain for the campus project, as the very close distance between these two sites (30 km) promises them the very similar solar irradiation condition. While the energy consumption of the studied campus project offers the energy demand for space heating and hot tap water in the need of 19,000 students, which is to be evaluated as the task of the STES system in the estimation work. Both building project reports are filed by GARDI (Architecture design research institution of Guizhou).Some key factors have been calculated and estimated, the heat demand of the studied campus project in Guiyang is 5,558 MWh/year, and the possible solar gain of this campus complexity is 4,900 MWh/year based on the gain & losses proportion of the Anneberg project evaluation. Due to the very different climate condition of Guiyang and Anneberg, as well as other uncertain factors such as effective roof area, solar collector efficiency, a sensitivity analysis evaluated the result with different parameters in changes of percentage. Final results in the changes of effective roof area at 80% and 85 %, borehole losses at 50% and 45%, available solar gain at 38%, STES system is shown to be capable of providing sufficient heat to buildings. If the heating demand and hot tap water, in the case of the campus project alone are all covered by STES system, there will be a reduction in CO2 emission of 5,368 tons/year.Cross-sectional analysis concludes four out of eight factors checked as “Good” and two as “Ok”, other two as “Unsure”. Other three cities (Chengdu, Kunming, and Chongqing) are brought to comparison later regarding climate condition. Besides Guiyang, two out of three are evaluated to have potential of STES implementation according to their sun hours, annual average temperature etc. STES system is estimated to be possible for implementation in south-west of China as the conclusion.

Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions. / M.S. / The notion of adjustable direction of airflow has been used in the car industry and airplanes for decades, enabling the users to manually adjust the direction of airflow to their satisfaction. However, in the building the introduction of the incoming airflow to the environment of the room is achieved either by non-adjustable uniform diffusors, aiming to condition the air in the environment in a homogeneous manner. In the present thesis, the possibility of adopting directional airflow in place of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle on average. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.

In the aspects of global sustainability, buildings are known to be one of the largest energy consumers. Though sustainable building construction through technological advances is helping in achieving environment friendly buildings, a considerable amount of energy is also being consumed by existing buildings. While many factors at all different stages of building life are responsible for this, the building material is one of the most important considerations. Glass being the most sensitive building material can lead to high energy consumption in the building if used in an improper way. This study takes this factor into account, and tries to investigate the potential of energy savings in buildings through the simple and basic considerations in design. An energy analysis model of an existing academic building in College Station, Texas was developed using Design Builder computer simulation software. This model was then analyzed for the total amount of energy consumption in the base case. The existing building model was then modified by replacing the glass used for external fenestrations. Latest building codes and standards for the site location, glass properties, and parametric simulation results were taken into consideration. Again the model was simulated for annual energy consumption and the results are noted. This formed the first option for the retrofitting scenario. A hypothetical redesign scenario was also established in which the revision of building orientation was taken into consideration. The building was re-oriented to suit the weather conditions and recommendations by Advanced Energy Design Guidelines (30 percent energy savings over ASHRAE Standard 90.1-1999). The building was then simulated for annual energy consumption. A comparative analysis was performed between the three cases and the study concluded by showing 23 percent savings in the annual fuel consumption, 23.35 percent reduction in CO2 emission of the building and 25 percent reduction in annual solar heat gain under Modified case 1. Modified case 2, however, did not show any further savings due to the form of the building (almost square). However, modified case 1 settings emitted 31.8 percent more CO2 over the Energy Star office building in Texas. This methodology sets up a set of guidelines which can be followed while investigating a building for minimum annual energy consumption.

Residential and light-commercial buildings comprise a significant portion of buildings in the United States. They account for a large fraction of the total amount of energy used in the U.S., and they also represent environments where people spend the majority of their time. Thus, the design, construction, and operation of these buildings and their systems greatly affect energy consumption and exposures to airborne pollutants of both indoor and outdoor origin. However, there remains a need to improve knowledge of some key source and removal mechanisms of indoor and outdoor pollutants in residential and light-commercial buildings, as well as their connections to energy use and peak electricity demand. Several standardized field test methods exist for characterizing energy use and indoor air quality in actual buildings, although few explicitly address residential and light-commercial buildings and they are generally limited in scope. Therefore, the work in this dissertation focuses on improving methods to characterize three particular building components for their impacts on exposures to indoor pollutants and their implications for energy consumption: (1) central forced-air heating and cooling (HAC) systems, (2) HAC filters, and (3) building envelopes. Specifically, the research in this dissertation is grouped to fulfill two primary objectives of developing and applying novel methods to: (1) characterize and evaluate central air-conditioning systems and their filters as pollutant removal devices in residential and light-commercial buildings, and to explore their implications for energy consumption, and (2) characterize and evaluate the ability of two particular outdoor pollutants of concern (ozone and particulate matter) to infiltrate indoors through leaks in building envelopes. The research in this dissertation is divided into four primary investigations that fulfill these two objectives. The first investigation (Investigation 1a) addresses Objective 1 by first providing a detailed characterization of a variety of operational characteristics measured in a sample of 17 existing central HAC systems in occupied residential and light-commercial buildings in Austin, Texas, and exploring their implications for exposure to indoor pollutants, energy use, and peak electricity demand. Among the findings in this study, central air-conditioning systems in occupied residential and light-commercial buildings did not operate most of the time, even in the hot and humid climate of Austin, Texas (i.e., ~25% of the time on average in the summer). However, average recirculation rates still make central air-conditioning systems competitive as particle removal mechanisms, given sufficient filtration efficiency. Additionally, this investigation used a larger, much broader, dataset of energy audits performed on nearly 5000 single-family homes in Austin to explore common inefficiencies in the building stock. Residential and light-commercial air-conditioning systems are often inefficient; in fact, residential central air-conditioning systems in particular likely account for nearly 20% of peak electric demand in the City of Austin. As much as 8% of peak demand could be saved by upgrading all single-family homes in Austin to higher-efficiency equipment. The second investigation (Investigation 1b) also addresses Objective 1 by developing and applying a novel test method for measuring the in-situ particle removal efficiency of HAC systems and filters in residential and light-commercial buildings. Results from the novel test method as performed with three test filters and 0.3–10 μm particles in an unoccupied test house agreed reasonably well with results from other field and laboratory test methods. Low-efficiency filters did not increase particle removal much more than simply running the HAC system without a filter, and higher-efficiency filters provided greater than ~50% removal efficiency for most particles greater than 1–2 μm in diameter. The benefit of this test method is that it can be used to measure how filters perform in actual environments, how filter removal efficiency changes with actual dust loading, and how much common HAC design and installation issues, such as low airflow rates, duct leakage, fouled coils, and filter bypass airflow, impact particle removal in real environments. The third investigation (Investigation 2a) addresses Objective 2 by developing and applying a novel test methodology for measuring the penetration of outdoor ozone, a reactive gas, through leaks in exterior building envelopes using a sample of 8 single-family residences in Austin, Texas. These measurements represent the first ever measurements of ozone penetration factors through building envelopes of which I am aware, and penetration factors were lower than the usual assumption of unity (i.e., P = 1) in seven of the eight test homes (ranging from 0.62±0.09 to 1.02±0.15), meaning that some building envelopes provide occupants with more protection from indoor exposures to ozone and ozone reaction byproducts than others. Additionally, ozone penetration factors were correlated with some building characteristics, including the amount of painted wood siding on the exterior envelope and the year of construction, suggesting that simple building details may be used to predict ozone infiltration into homes. Finally, the fourth investigation (Investigation 2b) also addresses Objective 2 by refining and applying a test methodology for measuring the penetration of ambient particulate matter through leaks in building envelopes, and using a sample of 19 single-family residences in Austin, Texas to explore correlations between experimentally-determined particle penetration factors and standardized fan pressurization air leakage tests. Penetration factors of particles 20–1000 nm in diameter ranged from 0.17±0.03 to 0.72±0.08 across 19 homes that relied solely on infiltration for ventilation air. Particle penetration factors were also significantly correlated with results from standardized fan pressurization (i.e., blower door) air leakage tests and the year of construction, suggesting that occupants of older and leakier homes are exposed to more particulate matter of outdoor origin than those in newer tighter homes. Additionally, blower door tests may actually offer some predictive ability of particle penetration factors in single-family homes, which could allow for vast improvements in making easier population exposure estimates. Overall, the work in this dissertation provides new methods and data for assessing the impacts of central air-conditioning systems, filters, and building envelopes on human exposure to indoor pollutants and energy use in residential and light-commercial buildings. Results from these four primary investigations will allow building scientists, modelers, system designers, policymakers, and health scientists to make better informed decisions and assumptions about source and removal mechanisms of indoor pollutants and their impacts on building energy consumption and peak electricity demand. / text

While more efficient use of energy is increasingly vital to the development of the modern industrialized world, emerging visualization tools and approaches of telling data stories provide an opportunity for the exploration of a wide range of topics related to energy consumption and conservation (Olsen, 2017). Telling energy stories using data visualization has generated great interest among journalists, designers and scientific researchers; over time it has been proven to be effective to provide knowledge and insights (Holmes, 2007). This thesis proposes a new angle of tackling the challenge of designing visualization experience for building energy data, which aims to invite the users to think besides the established data narratives, augment the knowledge and insight of energy-related issues, and potentially trigger ecological responsible behaviors, by investigating and evaluating the efficacy of the existing interactive energy data visualization projects, and experimenting with user-centric interactive interface and unusual visual expressions though the development of a data visualization prototype.

The process of energy consumption and monitoring of a buildingis time-consuming. Therefore, an feasible approach for using trans-fer learning is presented to decrease the necessary time to extract re-quired large dataset. The technique applies a bidirectional long shortterm memory recurrent neural network using sequence to sequenceprediction. The idea involves a training phase that extracts informa-tion and patterns of a building that is presented with a reasonablysized dataset. The validation phase uses a dataset that is not sufficientin size. This dataset was acquired through a related paper, the resultscan therefore be validated accordingly. The conducted experimentsinclude four cases that involve different strategies in training and val-idation phases and percentages of fine-tuning. Our proposed modelgenerated better scores in terms of prediction performance comparedto the related paper.

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Daylight harvesting is an essential strategy that is often used to enhance both the design and performance of an architectural project. Windows, as crucial architectural elements, not only admit natural light into spaces but also provide the occupants with visual connections. However, the excessive usage of windows brings an uncontrolled amount of solar energy to the spaces and negatively affect the building's energy performance. When utilizing passive design strategies such as daylight harvesting, several parameters, including the electrical lighting system, can impact the outcome. The current study investigates the role of lighting systems on daylight harvesting's effectiveness and their impact on window dimension and total energy consumption. In this study, the optimum window-to-wall ratio of an open office in the presence of two different light sources (LED and fluorescent) is explored through a computer simulation method. A combination of tools including AGi32, ElumTools, OpenStudio, EnergyPlus, Radiance, and MATLAB helps to conduct the simulation and deliver optimal results. In the results and conclusion chapter, the study provides guidelines to specify optimal window percentages considering two lighting systems in each cardinal direction. Importantly, the guideline focuses only on energy performance and not on the spatial quality of the design. / Master of Architecture / Harnessing daylight with the use of windows helps to offset parts of the electric lighting needs, and decrease the total building energy consumption. This is accomplished by using glazed materials to admit daylight and lighting control systems, which can respond to the dynamic light level. However, improper implementation of a passive daylighting strategy may cause increased energy consumption. Sunlight is accompanied by solar heat radiation which can increase the HVAC load of a space and compromise the energy savings achieved by daylighting. Therefore, a balance between solar heat and light gain is required to fully take advantage of solar energy without reverse impacts. Concerning the mentioned balance, recent advancements in lighting technology question the effectiveness of natural light in reducing whole-building energy consumption. Due to the high energy efficiency of LED luminaires, lighting power consumption is rather low, even when the lighting system operates at full capacity. Therefore, it is unclear whether the solar energy coming through glazed materials works to the advantage or disadvantage of total building energy consumption. This study hypothesized that the total energy consumption of an open office with LED luminaires would be less in absence of solar energy compared to a scenario which utilizes the solar energy. A simulation-based methodology, using a combination of photometric computation and building energy simulation tools, was utilized to examine the hypothesis and explore the impacts of lighting systems on the optimum window-to-wall ratio. The results provide a helpful guideline which highlights the impact of lighting systems on window dimensions and their mutual effect on whole-building energy consumption. Although the optimum window-to-wall ratios suggested by this study only concern energy consumption, integration of them with occupants' preferences can propose an acceptable window-to-wall ratio that satisfies both design quality and performance of a building.

<p dir="ltr">This thesis investigates approaches to classify and anticipate the energy consumption of commercial office buildings using external and performance benchmarking to reduce the energy consumption. External benchmarking in the context of building energy consumption considers the influence of climate zones that significantly impact a building's energy needs. Performance benchmarking recognizes that different types of commercial buildings have distinct energy consumption patterns. Benchmarks are established separately for each building type to provide relevant comparisons.</p><p dir="ltr">The first part of this thesis is about providing a benchmarking baseline for buildings to show their consumption levels. This involves simulating the buildings based on standards and developing a model based on real-time results. Software tools like Open Studio and Energy Plus were utilized to simulate buildings representative of different-sized structures to organize the benchmark energy consumption baseline. These simulations accounted for two opposing climate zones—one cool and humid and one hot and dry. To ensure the authenticity of the simulation, details, which are the building envelope, operational hours, and HVAC systems, were matched with ASHRAE standards.</p><p dir="ltr">Secondly, the neural network machine learning model is needed to predict the consumption of the buildings based on the trend data came out of simulation part, by training a comprehensive set of environmental characteristics, including ambient temperature, relative humidity, solar radiation, wind speed, and the specific HVAC (Heating, Ventilation, and Air Conditioning) load data for both heating and cooling of the building. The model's exceptional accuracy rating of 99.54% attained across all, which comes from the accuracy of training, validation, and test about 99.6%, 99.12%, and 99.42%, respectively, and shows the accuracy of the predicted energy consumption of the building. The validation check test confirms that the achieved accuracy represents the optimal performance of the model. A parametric study is done to show the dependency of energy consumption on the input, including the weather data and size of the building, which comes from the output data of machine learning, revealing the reliability of the trained model. Establishing a Graphic User Interface (GUI) enhances accessibility and interaction for users. In this thesis, we have successfully developed a tool that predicts the energy consumption of office buildings with an impressive accuracy of 99.54%. Our investigation shows that temperature, humidity, solar radiation, wind speed, and the building's size have varying impacts on energy use. Wind speed is the least influential component for low-rise buildings but can have a more substantial effect on high-rise structures.</p>

By 2050, the world’s population is expected to reach 9.7 billion, with over half living in urban settlements (United Nations, 2015). Planning and designing new urban developments and improving existing infrastructure will create or reshape urban landscapes and will carry significant implications for energy consumption, infrastructure costs, and the urban microclimate on a larger scale. Researchers and industry professionals must recognize how changes in land use affect the urban microclimate and, therefore, building energy consumption. Built environment and microclimate studies commonly involve modeling or experimenting with mass and energy exchanges between natural and the built environment. Current methods to quantify these exchanges include the isolated use of microclimate and building energy simulation tools. However, current urban planning and building design processes lack a holistic and seamless approach to quantifying all thermodynamic interactions between natural and built environments; nor is there a method for communicating and visualizing the simulated building energy data. This dissertation has developed a coupling method to quantify the effects of the urban microclimate on building energy consumption. The coupling method was tested on a medium-sized office building and applied to a design case, a redevelopment project in Pittsburgh, PA. Three distinct approaches were used. First, to develop the coupling method, a study was conducted to quantify the importance of accurate microclimate model initialization for achieving simulation results that represent measured data. This initialization study was conducted for 24 cases in the Pittsburgh climate. The initialization study developed a rule-based method for estimating the number of ENVI-met simulations needed to predict the microclimate for an annual period. Second, a coupling method was developed to quantify these microclimate effects on building energy consumption. The Center for Sustainable Landscapes (CSL) building was used as a test-case for this coupling method to measure improvement in predicting building heating and cooling energy consumption. Results show that the coupling method, more than the TMY3 weather data used for energy simulations, can improve building energy consumption predictions for the winter and summer months. Third, to demonstrate industry implications, the coupling method was applied to a design case, the Lower Hill District Redevelopment, Pittsburgh, PA. Comparing the decoupled energy model and TMY3 weather data revealed a high degree of variation in the heating and cooling energy consumption. Overall results reinforced the hypothesis that building surface level coupling is not essential if the energy model accounts for the microclimate effects. A Design Decision Support (DDS) method was also developed as a tool for project stakeholders to communicate high-fidelity simulated energy data.