PhD, University of Cincinnati, 2023, Engineering and Applied Science: Civil Engineering

The constant growth of population and urbanization in conjunction with the demand for enhanced building services and comfort have led to a substantial increase in energy consumption in the building sector accounting for up to 40% in developed countries. Among the several end uses in buildings, heating, ventilation, and air conditioning (HVAC) systems consume the largest quantity of energy. Therefore, energy efficiency in these systems has become a prime objective for building standards and energy policies. The variable air volume (VAV) systems that are the most commonly used HVAC systems in the USA offer some advantages to achieve energy efficiency in buildings but also have some inherent limitations that can increase the energy consumption and cost of the systems. In response, this dissertation proposes an optimal design and control of the VAV systems that aims to achieve both energy and cost benefits in buildings, taking the existing systems' attributes into account. In this research, (i) a new configuration of dual duct systems named the ‘Dual VAV' system is proposed that has the characteristics of existing single and dual duct VAV systems to utilize their benefits while eliminating the shortcomings, (ii) a new sequence of control is designed for the dual VAV system after several iterations that largely varies from the standard control sequence of the VAV systems for the effective control and operation of the proposed system, (iii) a modeling strategy is developed for dual VAV and three other existing systems for the building simulation purposes as the specific AHU arrangement is not available in any simulation platforms, (iv) a small multizone office building is simulated with the model and control sequence at first and later a large four story multizone office building is simulated for the evaluation of annual heating, cooling and fan power consumption for the proposed system, (v) two optimization strategies for supply air temperature reset and outdoor (open full item for complete abstract)

Committee: Nabil Nassif (Committee Chair); Munir Nazzal Ph.D. (Committee Member); Hazem Elzarka Ph.D. (Committee Member); Pravin Bhiwapurkar Ph.D. (Committee Member) Subjects: Civil Engineering

PhD, University of Cincinnati, 2023, Engineering and Applied Science: Civil Engineering

A substantial portion of buildings' total energy use is caused by heating, ventilation, and air conditioning (HVAC) systems. Data from the U.S. Energy Information Administration reports that buildings in the U.S. currently exhaust 72% of electricity produced and 55% of U.S. natural gas. In the U.S. the energy consumption of buildings exceeds that of transportation and other demand sectors. Of this energy, approximately half is used by heating and cooling systems. If energy usage trends continue at this pace, by 2025 buildings will turn into the largest users of energy worldwide. Developing methods and models that contribute to building energy savings is increasingly imperative for a sustainable future. Although most modern buildings today are equipped with advanced building automation systems (BAS) giving them the ability to collect a large amount of data, they still lack the embedded computational means and centralized solutions to operate in an optimal way. They face long term challenges too as it is estimated that around 30% of the total energy consumption in buildings is wasted due to lack of proper maintenance, aging equipment, and/or control issues. There is a significant need to explore how the latest computational methods can draw from available building data sources to perform modeling for optimization and energy efficiency. Fault detection requires model accuracy and appropriate thresholds. Machine learning-based energy models have proved to be efficient and accurate at this. This multi-level study introduces a comprehensive method to model, optimize and assess the performance of different components of HVAC systems. The proposed methods use performance data collected from real building components and are applicable to any existing system regardless of its complexity, configuration, or age. Development of accurate performance models was achieved by implementing various data driven modeling algorithms to datasets obtained from components performa (open full item for complete abstract)

Committee: Nabil Nassif (Committee Chair); Munir Nazzal Ph.D. (Committee Member); Hazem Elzarka Ph.D. (Committee Member); Pravin Bhiwapurkar (Committee Member) Subjects: Engineering

Master of Sciences (Engineering), Case Western Reserve University, 2022, EMC - Mechanical Engineering

In this study, we refined and validated a method of determining the average refrigeration load in US commercial buildings using a virtual energy audit software called EDIFES. Crucial assumptions made in the analysis were investigated and validated, using variants of the preexisting code. Validation occurred through two methods: a statistics-based population study and a case study. The population study compared a sample of 32 buildings EDIFES analyzed against a group of 129 CBECS buildings based on energy use intensity (EUI). The Wilcoxon rank-sum test was used to determine if there was a statistically significant difference between the two populations. This statistics-based method of validation using the CBECS dataset revealed two key findings. First, it was determined that this analysis of determining refrigeration load should only be performed on food sales buildings. Second, the analysis revealed that the 3 Hour Variant of the refrigeration marker with 60% run time correction factor performed the best, with a p-value of 0.977, meaning there is very little evidence to reject the statement that the two populations have equal medians. The case study approach to validation entailed selecting a food sales building on the campus of CWRU, submetering plug-in refrigeration loads, and using an engineering manual (physics-based analysis) to estimate the load of walk-in units. This analysis demonstrated that, on average, EDIFES effectively captured 94% of the refrigeration load of the building.

Committee: Brian Maxwell PhD (Committee Chair); Stephen Hostler PhD (Committee Member); Alexis Abramson PhD (Committee Member); Roger French PhD (Committee Member) Subjects: Energy; Engineering

Doctor of Philosophy, The Ohio State University, 2019, Food, Agricultural and Biological Engineering

Thermal comfort is an important factor in designing high-quality buildings. The well-conditioned environment can keep occupants healthy and productive and ensure workplace safety. The heating, ventilation and air conditioning (HVAC) system plays an important role in providing and maintaining indoor thermal comfort for buildings. The faults in an HVAC system not only waste energy but also cause poor thermal comfort, building-related illnesses, or even safety accidents. This research adopted the model-based method to detect and diagnose the faults in a selected HVAC system. First, a simulation model of the case study building was created and validated based on both energy and thermal performance. Then, by comparing the indoor air temperatures between the simulation model and the real situation, three common types of faults in the HVAC system were detected for summer and winter, including: 1) control fault, 2) facility fault, and 3) design fault. In addition, the simulation fault was identified in the winter time. For each type of faults, the corresponding solutions were proposed, which will help building operators to locate and solve the faults quickly and accurately. As another important factor to designing high-quality buildings, building energy efficiency could reduce building's energy consumption and their environmental footprint. To lower buildings' significant energy consumption and high impacts on environmental sustainability, recent years have witnessed rapidly growing interests in efficient HVAC precooling control and optimization. However, due to the complex analytical modeling of building thermal transfer, rigorous mathematical optimization for HVAC precooling is highly challenging. As a result, progress on HVAC precooling optimization remains limited in the literature. One of the main contributions of this research is to overcome the aforementioned challenge and propose an accurate and tractable HVAC precooling optimization framework. The main results are (open full item for complete abstract)

Committee: Qian Chen (Advisor); Jia Liu (Committee Member); Sandra Metzler (Committee Member); Lingying Zhao (Committee Member) Subjects: Agricultural Engineering; Civil Engineering; Environmental Engineering; Sustainability

Doctor of Philosophy, Case Western Reserve University, 2018, EMC - Mechanical Engineering

Energy Diagnostics Investigator for Efficiency Savings (EDIFES) is a scalable data an- alytics tool that uses big data, and rigorous statistical studies to uncover building en- ergy characteristics. To create EDIFES, building energy markers were developed using R and Python functions that compute various types of building identifiers when applied to whole building, 15-minute electricity data, as is that typically collected by the util- ity company. Requisite weather datasets also were analyzed in conjunction with the electricity consumption data. In this study, we developed nine building markers and applied them to 19 commercial buildings located in four different climate zones to com- pare their characteristics. The building markers are: correlation with weather variables, weekday-weekend operational pattern, weekday operational pattern, heating type, sys- tem oversize (heating), system oversize (cooling), HVAC scheduling, HVAC sizing, and baseload. Using the findings from this analysis, we developed a building energy disag- gregation model to further quantify a buildings' energy usage. Building energy disaggre- gation can identify and estimate equipment-level energy scheduling and consumption which can provide real-time feedback to the customer. The disaggregation tool is unsu- pervised and non-intrusive, and again, uses only whole building electricity and weather datasets for the analysis. Therefore, the disaggregation model can perform the analysis virtually, without installing any sensors/meters in the building. The disaggregation tool is derived from the building markers and by utilizing Bayesian frameworks: the Hidden Markov model and the Factorial Hidden Markov model. The disaggregation tool esti- mates the equipment state with an accuracy of approximately 75% for a scheduled office building. The state of the HVAC can be estimated with the disaggregation tool with an accuracy of approximately 81%. We can conclude that the EDIFES analysis developed to-date an (open full item for complete abstract)

Committee: Alexis Abramson (Committee Chair); Roger French (Committee Member); Joseph Prahl (Committee Member); Mehmet Koyuturk (Committee Member); Rojiar Haddadian (Committee Member) Subjects: Mechanical Engineering

Master of Science (M.S.), University of Dayton, 2017, Renewable and Clean Energy

Photovoltaic-thermal (PVT) technology is a relatively new technology that comprises a photovoltaic (PV) panel coupled with a thermal collector to convert solar radiation into electricity and thermal energy simultaneously. Since cell temperature affects the electrical performance of PV panels, coupling a thermal collector with a PV panel contributes to extracting the heat from the latter to improve its performance. In order to ensure a sufficient temperature difference between the PV cells and the working fluid temperature entering the thermal collector, the circulated water has to reject the heat that has been removed from the PV cells into a relatively colder environment. Borehole thermal energy storage (BTES), which is located underground, often serves as this relatively colder environment due to the stability of underground temperatures, which are usually lower than the working cell temperature. Use of BTES is especially beneficial in summer, when the degradation in cells efficiency is highest. In this thesis, the electrical, thermal, and economic performances of a PVT system are evaluated for three types of buildings -- residential, small office, and secondary school -- in two different climates in the United States, one of which is hot and the other is cold. For each case, two different scenarios are considered. In the first, a PVT system is coupled with BTES, and a ground-coupled heat pump (GCHP) is in use. In the second, a PVT system is coupled with BTES and no GCHP is in use. Each scenarios' GCHP performance is assessed as well. Both the PVT collectors and GCHP performances are evaluated over short and long-term to study the effect of continued ground heat imbalance on both technologies.

Committee: Andrew Chiasson Ph.D. (Committee Chair); Youssef Raffoul Ph.D. (Committee Member); Robert Gilbert Ph.D. (Committee Member) Subjects: Energy; Engineering; Mechanical Engineering

Master of Sciences (Engineering), Case Western Reserve University, 2016, EMC - Mechanical Engineering

Energy Diagnostics Investigator for Efficiency Savings (EDIFES) has been developed for scalable data analytics to conduct virtual energy audits on commercial buildings. Built as a software package in R, EDIFES ingests building electricity data and readily available weather data, applying various data analytics to determine building markers, characteristics, and operational tendencies. Through these analyses building systems are identified, including Heating Ventilation and Air Conditioning (HVAC), lighting, and plug load or other equipment, with characteristics such as load and system scheduling. Once building systems have been identified, EDIFES conducts virtual energy audits to diagnose efficiency issues, determines the impact (i.e. return-on-investment or payback) of potential retrofit actions (e.g. rescheduling HVAC to occupied hours or conducting a lighting retrofit). After this stage, it can be used for measurement and verification (M\&V) or continuous commissioning. Six buildings are presented in this thesis.

Committee: Alexis Abramson PhD (Committee Chair); Roger French PhD (Committee Co-Chair); Joseph Prahl PhD (Committee Member) Subjects: Civil Engineering; Computer Science; Energy; Engineering; Mechanical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2016, Engineering

Variable Air Volume (VAV) systems are the most popular HVAC systems in commercial buildings. VAV systems are designed to deliver airflows at design conditions which only occur for a few hours in a year. Minimizing energy use in VAV systems requires reducing the amount of airflow delivered through the system at part load conditions. Air Handling Unit (AHU) fans are the major drivers of airflow in VAV systems and installing a Variable Frequency Drive (VFD) is the most common method of regulating airflow in VAV systems. A VFD drive does not necessarily save energy without use of an appropriate control strategy. Static pressure reset (SPR) is considered to be the most energy efficient control strategy for AHU fans with VFDs installed. The implementation of SPR however has many challenges; for example, rogue zones—zones which have faulty sensors or failed controls and actuators, system dynamics like hunting and system diversity. By investigating the parameters associated with the implementation of SPR in VAV systems, a new, improved, more stable SPR algorithm was developed and validated. This approach was further improved using Fault Detection and Diagnostics (FDD) to eliminate rogue zones. Additionally, a CO2-Demand Control Ventilation (DCV) based minimum airflow control was used to further reduce ventilation airflow and save more energy from SPR. Energy savings ranging from 25% to 51% were recorded in actual buildings with the new SPR algorithm. Finally, a methodology that utilizes historical VAV data was developed to estimate the potential savings that could be realized using SPR. The approach employed first determines an effective system loss coefficient as a function of mean damper position using the historical duct static pressure, VAV damper positions and airflows. Additionally, the historical data is used to identify the maximum mean duct damper position realizable as a result of insuring a sufficient number of VAVs are fully open at any time. Savings ar (open full item for complete abstract)

Committee: Kevin Hallinan (Committee Chair); Kelly Kissock (Committee Co-Chair); Andrew Chiasson (Committee Member); Zhenhua Jiang (Committee Member) Subjects: Energy; Engineering; Mechanical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2015, Engineering

Currently, there is no integrated dynamic simulation program for an energy efficient greenhouse coupled with an aquaponic system. This research is intended to promote the thermal management of greenhouses in order to provide sustainable food production with the lowest possible energy use and material waste. A brief introduction of greenhouses, passive houses, energy efficiency, renewable energy systems, and their applications are included for ready reference. An experimental working scaled-down energy-efficient greenhouse was built to verify and calibrate the results of a dynamic simulation model made using TRNSYS software. However, TRNSYS requires the aid of Google SketchUp to develop 3D building geometry. The simulation model was built following the passive house standard as closely as possible. The new simulation model was then utilized to design an actual greenhouse with Aquaponics. It was demonstrated that the passive house standard can be applied to improve upon conventional greenhouse performance, and that it is adaptable to different climates. The energy-efficient greenhouse provides the required thermal environment for fish and plant growth, while eliminating the need for conventional cooling and heating systems.

Committee: John Kissock (Advisor) Subjects: Agricultural Engineering; Agriculture; Alternative Energy; Energy; Engineering; Environmental Science; Mechanical Engineering

PhD, University of Cincinnati, 2019, Design, Architecture, Art and Planning: Architecture

Saudi Arabia has gone through a major transformation in architecture and lifestyle. The drastic shift in the country's economy during the 1970s exposed all Saudis to the western culture and lifestyle which in turns changed almost all the architectural features that once were locally suitable. This transformation has created a conflict which generates architectural designs that are not in harmony with the surrounding nature and consequently not energy efficient. The country has been giving significant attention to energy conservation due to extensive energy consumption in the building sector. Residential buildings, in particular, occupy two-thirds of the total consumption of electricity. The increasing level of energy consumption burdens the national economy; therefore, the government omitted the energy subsidies to protect the national economy. While this mitigated the economic challenges, it created another problem for homeowners. Energy bills have drastically increased because most of the existing buildings lack of proper thermal insulation. Due to the high energy consumption associated with building use in the country, a study in this regard is needed now more than ever. The current situation of Saudi Arabia requires retrofitting the existing buildings to achieve high energy efficiency standards. The goal of this research is to provide energy efficient design guide and economical retrofitting strategies that would help the public as well as designers in the process of decision-making. The aim is to help decision makers with the most energy efficient and cost-effective alternative available. Additionally, this research aims to revive the traditional architecture which enhance the local identity and ultimately improve the energy efficiency of buildings. So, this dissertation focuses on retrofitting the existing residential buildings to meet the highest possible energy performance they could reach without neglecting other aspects such as cost viability and cultur (open full item for complete abstract)

Committee: Joori Suh Ph.D. (Committee Chair); Julian Wang Ph.D. (Committee Chair); Hazem Elzarka Ph.D. (Committee Member) Subjects: Architecture

Doctor of Philosophy (Ph.D.), University of Dayton, 2019, Mechanical Engineering

Cost effective energy efficiency improvements in residential buildings could yield annual electricity savings of approximately 30 percent within this sector for the United States. Furthermore, such investment can create millions of direct and indirect jobs throughout the economy. Unfortunately, realizing these savings is difficult. One of the impediments for realization is the means by which savings can be estimated. The prevalent approach is to use energy models to estimate. However, actual energy savings are more often than not over-predicted by energy models, leading to wariness on the part of potential investors which include the residents themselves. A driver for this research is 500 residential buildings with known geometrical and historical energy data owned by the University of Dayton. Further, the energy characteristics of these buildings are knowable. This housing stock offers significant diversity in size (ranging from a floor area of 715to 2800 square feet), age (from the early 1900s to new construction) and energy effectiveness, the latter occurring as a result of gradual improvements made to residences over the past 15 years. In the summer of 2015 energy and building data audits were conducted on a subset of 139 homes. The audit documented the areas of the walls and attic, the amount and type of insulation in the walls and attic, areas and types of windows, floor heights, maximum occupancy, appliance (refrigerator, range, oven) specifications, heating ventilation air-conditioning system specifications domestic hot water equipment specifications, interior attic penetration area, and the presence of a basement. A data mining approach was used for developing the Random Forest (RF) model to predict energy consumption in a group of single family houses based upon knowledge of residential energy characteristics, historical energy consumption, occupancy and building geometrical data, as well as inferred energy characteristics from energy consumption dat (open full item for complete abstract)

Committee: Kevin Hallinan Ph.D (Advisor); Robert Brecha Ph.D (Committee Member); Andrew Chiasson Ph.D (Committee Member); Jun- Ki Choi Ph.D (Committee Member) Subjects: Economics; Energy; Mechanical Engineering

Master of Sciences (Engineering), Case Western Reserve University, 2019, EECS - Electrical Engineering

This thesis discusses an integrated tripartite method of building performance evaluation, analysis, and improvement. This method is described and explained through the context of studies involving the Olin Building on the campus of Case Western Reserve University. The three methods of design analysis, data collection, and fieldwork are introduced, and performance is defined as an optimization of comfort, energy efficiency, and reliability. Relevant history of the building's construction and renovation are discussed, including insights developed by the author regarding the consequences of various design features and modifications. Olin's major renovation in 1996 is a major focus, and the current controls installed in the building are discussed in detail. Data collection and analytical methods used and devised by the author are reviewed, and effective fieldwork techniques are outlined. The author concludes by summarizing major themes, illustrating accomplishments in Olin, and enumerating future work that could be done.

Committee: Kenneth Loparo (Advisor); Frank Merat (Committee Member); Sunniva Collins (Committee Member) Subjects: Electrical Engineering; Engineering; Mechanical Engineering

Master of Science (MS), Ohio University, 2006, Environmental Studies (Arts and Sciences)

This thesis aims to evaluate the lighting efficiency of three Ohio University campus buildings. The primary research question is: What are the short- versus long-term costs and benefits to Ohio University of renovating the lighting systems of these older buildings? The research was conducted as a case study with examination of two subquestions: What types of lighting fixtures are currently being used and how efficient are they? How efficient can proposed lighting fixtures be? Results indicate that the cost of installing more energy-efficient lighting fixtures can be quickly recaptured in older buildings. With replacing the present lighting fixtures Ohio University would pay approximately 2.5 times less than it pays currently for the lighting utilities cost of the case study buildings. With these energy savings it would take 3 to 4 years to reclaim the money spent for reinstallation of the energy-efficient lighting fixtures.

Committee: Dorothy Sack (Advisor) Subjects: Environmental Sciences