University of Alabama Repository :: Browsing by Author "O'Neill, Zheng"

Browsing by Author "O'Neill, Zheng"

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Construction equipment travel path visualization and productivity evaluation
(University of Alabama Libraries, 2017) Song, Siyuan; Moynihan, Gary P.; Marks, Eric D.; University of Alabama Tuscaloosa
The U.S. construction industry represents approximately 4% of the U.S. gross domestic product (BEA 2015) and currently involves over 6 million workers employed by an estimated 750,000 construction firms (BLS 2015). Within this industry, productivity is a key driver for economic growth and strongly affects prosperity for the country (Vogl and Abdel-Wahab 2014). More specifically, higher construction productivity and more reliable installation (quality) translates into higher wages and increased profits (Vogl and Abdel-Wahab 2014). On many construction projects, productivity is defined or greatly impacted by equipment cycle time. Furthermore, the U.S. construction industry continues to be one of the more dangerous work environments for employees (BLS 2015). Construction workers in the U.S. experience a disproportionate number of fatalities when compared other major industrial sectors in the U.S. (BLS 2013). Visibility has proven to be a major cause of accidents on construction sites (Hinze and Teizer 2011). This research seeks to prove the hypothesis that visibility and location-based data can be automatically collected and analyzed for construction equipment operators to assess a construction equipment cycle. As one of the more promising recent implementations in the construction industry, sensing and design technology provide unique opportunities to capture and analyze location-based information on construction sites. These technologies can enable productivity managers to identify, assess, and decrease the overall cycle time of a specific operation. This research implements Building Information Modeling (BIM), Global Positioning System (GPS) location identification, and laser scanning to enable automated data collection and analysis. The overall objective of the research is to automatically capture and analyze elements of a construction equipment cycle. The outcomes of this research addresses the following key components of an equipment cycle time: 1) automated cycle time path planning, 2) location-based data capture and analysis of real-time equipment cycles, and 3) equipment path environment visualization. The research framework was tested with active construction site data, and feedback from the workforce and management was assessed and integrated into the research approach. The research has the potential to improve productivity on construction sites and enhance construction employee safety performance. It will also assist in adding a link between productivity planning and management and existing project BIMs.
Development and modeling of a solar powered ground source heat pump system
(University of Alabama Libraries, 2017) Qian, Defeng; O'Neill, Zheng; University of Alabama Tuscaloosa
Buildings consumed 40% of the energy and represented 40% of the carbon emissions in the United States. This is more than any other sector of the U.S. economy, including transportation and industry. Most building energy consumption is for space heating, cooling and water heating in buildings. Enhancing building efficiency represents one of the easiest, most immediate and most cost-effective ways to reduce carbon emissions. One of energy efficient and environment friendly technologies with potentials for savings is Ground Source Heat Pump (GSHP) system. On the other hand, solar energy is considered as an unlimited an environment friendly energy source, which has been widely used for solar thermal and solar power applications. This study presents a laboratory test facility for a solar powered ground source heat pump system. The ultimate technical goal is to apply the solar powered ground source heat pump into a net-zero energy building (NZEB), where all the electricity consumption will be covered by an integrated on-site solar Photovoltaics (PV) panels and battery system. In addition, an equation based object-oriented modeling language, i.e., Modelica [1] is being investigated for the integrated system modeling. Such dynamic model will be used to explore advanced control of a solar powered GSHP system to facilitate better building to grid integrations. The detail for the design and layout of this solar powered GSHP system, together with the monitoring and data acquisition system and its Modelica-based dynamic model are introduced in this thesis. In addition, the feasibility of the application of the system are discussed. Finally yet importantly, the future work are presented.
Energy analytics and machine learning modeling in milling of difficult-to-machine alloys
(University of Alabama Libraries, 2019) Liu, Ziye; Guo, Yuebin B.; University of Alabama Tuscaloosa
Manufacturing consumes approximately 20% of total energy consumption in the U.S. Energy consumption in machining, an important manufacturing cluster, is substantial, contributes to a large fraction of manufacturing cost, and has a significant environmental impact. On the other hand, energy consumption at the process level is directly related to surface integrity of machined part. Therefore, it is critical to investigate energy consumption in machining for process sustainability and surface integrity. This dissertation consists of seven chapters: (1) The first chapter gave a comprehensive literature review on energy consumption in machining; (2) The second chapter compared the characteristics of energy consumption in dry milling vs. flood milling of Inconel 718 superalloy at machine, spindle, and process levels; (3) The third chapter has developed an innovative holistic concept of cumulative energy demand to evaluate the energy demand in milling Inconel 718 from the life cycle perspective; (4) The forth chapter has proposed a hybrid machine learning model by integrating cutting mechanics to predict net cutting specific energy; (5) The fifth chapter has coined a new concept of energy-based process signature to establish the relationship between net cutting specific energy and surface integrity; (6) The sixth chapter has proposed a real-time energy consumption monitoring framework at machine and enterprise levels; and (7) The last chapter has summarized the conclusions and proposed the future work.
Energy modeling and calibration of a mixed-use building with laboratories, offices, and classrooms
(University of Alabama Libraries, 2016) Liu, Liu; O'Neill, Zheng; University of Alabama Tuscaloosa
In general, mixed-use building with laboratories, office, and classrooms consume a significant amount of energy and very energy intensive. These buildings provide great opportunities for energy efficiency improvement from mechanical and energy system, including Heating, Ventilation and Air-Conditioning (HVAC) plugs, and lighting subsystems. Building Energy Modeling (BEM) recently has received more and more attention as a tool to reduce building energy consumption. However, the interconnected complexity of system and equipment in buildings with laboratories makes modeling of these buildings a unique and challenging task. This study presents a development and calibration of a university mixed-use building using the EnergyPlus simulation program. The building under study is the South Engineering Research Center (SERC) building. This building was built in 2012 and has a total area of 175,000 ft2 with three floors. SERC mainly consists of research and teaching laboratories, classrooms, conference rooms and offices. Air Hander Units (AHU) equipped with Energy Recovery Units (ERU) supply 100 percent outside air to the laboratory spaces through terminal Variable Air Volume (VAV) Boxes. Chilled and hot water are delivered from the campus central energy plant. Building geometry was created using DesignBuilder. Meter and sensors data from Building Automation System (BAS) are being collected and used for calibration. The modeling process, preliminary calibration and verification results, as well as implementation issues encountered throughout the modeling and calibration processed from a user’s perspective, are presented and discussed.
Energy performance estimation of cooling towers
(University of Alabama Libraries, 2016) Zhao, Zilai; Woodbury, Keith A.; University of Alabama Tuscaloosa
The goal of this project is to investigate and compare the performance of cooling towers using Effectiveness-NTU model and the empirical model of cooling towers. The process of achieving the goals includes: Developed both models of the cooling tower using the Effectiveness-NTU method, and empirical method for predicting the performance at the design and off-design conditions; Stated experimental protocols and gathered data on HVAC cooling towers on campus of the University of Alabama; Used collected data to validate the models; Compared results from models with real measurements and find the limitations of models; Applied known annual weather data to estimate the performance and energy consumption of cooling tower for a whole year; Recommended the approach for the best energy and heat performance of cooling towers. As a result, the Effectiveness-NTU model provided closer results than the empirical model. All data and specifications were measured and gathered from the experiment on the cooling tower on campus. However, the air mass flow rate and the temperature of the leaving air were not always possible to gather in different cooling towers, especially industry cooling towers. Therefore, both models were designed to predict mass flow rate, and temperature of the leaving air by applying air temperature and relative humidity. Further testing is required to validate the accuracy of the models because there was a limited control over the running status of fans and the experiments were not done in wintertime with lower entering air temperature. Validation of the models on another cooling tower is also essential. Additionally, the empirical model can be improved if there is a way to reset all 27 coefficients based on different cooling towers.
General equation for predicting cooling tower approach temperatures at lower wetbulbs
(University of Alabama Libraries, 2020) Cottrell, Benjamin Joseph; Woodbury, Keith A.; University of Alabama Tuscaloosa
Cooling tower approach temperature is the difference in leaving water temperature and entering air wet-bulb temperature. Cooling tower manufacturers describe the design capacity with water volumetric flow, entering water temperature, leaving water temperature, and entering air wet-bulb temperature. Cooling tower performance can be measured by comparing the design criteria to live operation, but only at design conditions. It is necessary to describe cooling tower approach temperatures when tonnage, flow, and wet bulb temperatures are not at design conditions. Gradient descent regression analysis was performed on data including 8760 hours from seven cooling tower installations to produce a generalized description of approach temperature. Fault detection diagnostics can be performed on such expected approach temperatures against live data.
HVAC control loop performance assessment: A critical review (1587-RP)
(Taylor & Francis, 2016) O'Neill, Zheng; Li, Yanfei; Williams, Keith; University of Alabama Tuscaloosa
This article presents a comprehensive review of control loop performance assessments in the context of building HVAC controls. Few studies are available for assessing HVAC control loop performance using a single control quality factor. A control quality factor should be an objective and quantitative metric with simple-to-interpret criteria and should only use data available from the actual control system, such as the control output. The authors systematically reviewed 34 indices and the associated methods of evaluating control loop performance and cataloged the drawbacks and merits of the different indices. Most of these performance assessment indices are currently used in process control industry applications. There were 14 of the 34 indices selected for further review, due to their particular suitability for implementation in HVAC control loop performance assessment. Finally, the selected 14 indices are implemented for assessments of three regulatory control loops with proportional-integral controllers: a heating coil outlet air temperature control loop and variable air volume room air temperature control loop using simulated data from a dynamic Modelica model, and variable air volume room air temperature control loop in a heating mode from real field data. Based on the review and preliminary results, the Normalized Harris Index and exponentially weighted moving averages based index are proposed as potential candidates for control quality factor, and further investigation of the use of them in HVAC control loop performance assessment is recommended.
An innovative fault analysis framework to enhance building operations
(University of Alabama Libraries, 2018) Li, Yanfei; O'Neill, Zheng; University of Alabama Tuscaloosa
This study proposes a failure analysis framework to enhance the building operations. A literature review was conducted for fault modeling and the fault mode and effect analysis (FMEA) applications to the building technology with a conclusion that few FMEA studies were applied in the building Heating, Ventilation, and Air-Conditioning (HVAC) systems. This study aims to fill this gap by investigating fault impacts through an FMEA analysis integrated with the whole building energy performance simulation (i.e., EnergyPlus). The primary objective of this study is to rank the fault based on impacts in terms of building energy consumptions and/or occupant thermal comfort under multiple faults. To achieve this goal, an extensible fault model library was established, including the building envelope, HVAC systems, lightings, etc. An FMEA framework was built to inject the possible fault models into EnergyPlus to evaluate their impacts on building energy consumption and thermal comfort. A parametric sensitivity analysis was used to determine and rank the criticality of the faults considering the fault concurrence frequency. Benefits and drawbacks of the response surface model through a deep learning algorithm (i.e. the multilayer perceptron regression) were explored during the sensitivity analysis for the fault rankings. The proposed fault analysis framework with rankings were tested and demonstrated for two DOE reference buildings (i.e., the medium office and the secondary school) in four different climate zones (i.e., Atlanta, Chicago, Miami, and San Francisco) with 24000 EnergyPlus fault simulations. Each fault mode is one fault model, or combination of multiple fault models, depending on the building energy systems. A total of 129 fault modes from 41 groups of fault models were implemented and simulated for the medium office case. The 129 fault modes are corresponding with 129 faults’ associated input parameters. For the secondary school, a total of 553 fault modes from 64 fault models were implemented during this study. The 553 fault modes are mapped into 553 fault associated input parameters. Those fault modes were injected into EnergyPlus simultaneously to study the fault impacts under multiple faults. The results demonstrate the proposed FMEA framework is robust and scalable for the fault impact analysis. The top critical faults for the medium office is the HVAC-Left-ON for the packaged rooftop unit, for the site energy, source energy, HVAC energy. Excluding the HVAC-Left-ON, the top critical faults vary greatly among the 4 climate zones. For the secondary school, the top critical faults are the Chiller-Fouling and Boiler-Fouling, for the air-cooled chiller system plus the gas-fired boiler.
Location-based leading indicators in BIM for construction safety
(University of Alabama Libraries, 2017) Shen, Xu; Marks, Eric D.; University of Alabama Tuscaloosa
The US construction industry continues to experience a high number of injuries and fatalities in comparison to other US industrial sectors (BLS 2013). Although the U.S. construction accounts for only 4% of total employment, the industry experiences a disproportionate 19% of the total fatalities experienced by the U.S. workforce (BLS 2014). An enhanced understanding of safety leading indicators for construction sites can be an influential factor in mitigating existing hazards and predicting future hazards (Hinze et al. 2013, Hinze 2006). Although construction companies in the U.S. are required by OSHA regulation to report all fatalities, injuries, and illnesses that occur on construction sites, a more concerted effort including research and implementation is required for safety leading indicators including near miss reporting and hazard identification. This research seeks to test the hypothesis that it is feasible to collect, analyze, and disseminate safety leading indicators through location-based information and visualization. Because one of the most impactful transitions in the construction industry in the past decade has been a transition to digitized construction documents with visualization of construction processes through Building Information Modeling (BIM), BIM provides a real-time visualization and communication platform for construction stakeholders (Azhar 2011, Eastman et al. 2011). Furthermore, the construction industry is transitioning from lagging or reactive safety data collection (i.e., injuries, illnesses and fatalities) to pro-active or leading indicator safety data collection (i.e., near misses and hazard identification) (Hallowell et al. 2013). This research advocates for the effective retrieval, analysis, visualization and dissemination of safety leading indicator data through created databases, algorithms and BIM functionality. Since a large majority of function components in a BIM are location-based, the outcome of this research is limited to location-based safety leading indicators (i.e., leading indicator safety data that can be assigned to a specific location). The research approach is divided into three major components: 1) near miss reporting, 2) automatic hazardous proximity zone generation, and 3) site location optimization. The framework will be evaluation in controlled laboratory settings as well as active construction sites. Throughout the research methodology, feedback and mentorship from construction engineering and management employees will be collected and integrated. This research connects the capabilities of BIM to safety data collection, storage, analysis and visualization.
Model-based estimation on building envelope infiltration
(University of Alabama Libraries, 2019) Hao, Zhengwen; O'Neill, Zheng; University of Alabama Tuscaloosa
Buildings consumed nearly 40% of total energy in the U.S. Air leaks through the building envelope are one of the factors increasing building energy consumption. The estimated energy use associated with infiltration loss through the building envelope within residential and commercial buildings in the United States for the year 2010 is 4 quads annually, which accounts for nearly 10% of the total energy use in buildings. The U.S Department of Energy published a building technologies program air leakage guide. In this air leakage guide, the DOE proposed five requirements for infiltration measurement method. However, two commonly used infiltration diagnostic approaches, blower door test and tracer gas method, are unable to meet the DOE requirement. Since existing infiltration diagnostic approaches do not meet with the DOE requirements, the building infiltration measurement is a challenge. To address the current challenges, a scalable and low-cost Building Infiltration Estimator with Ultrasonic Thermometry (BLAST) is proposed. The proposed method contains the physical measurements and a model-based estimation. This study is focusing on the model-based estimation part. To estimate building infiltration, a building envelope heat transfer model has to be developed. Recent study shows that a low-ordered three resistance-two capacitance (3R2C) thermal network model is sufficient to describe the building envelope heat transfer. A customized 3R2C thermal network is developed to represent building envelope heat transfer. Based on the 3R2C thermal network, the energy balance equation for building envelope has been applied to get the state-space model. The state-space differential equation is one of the key points to determine the suitable estimation method. This study uses an Extended Kalman filter (EKF) to inversely estimate the building infiltration using measurements of surface temperature and total heat flux, and a low-order state-space model. An EnergyPlus-based emulator is used to generate a virtual building and measurements to test the proposed estimation method. Nearly 80% estimated infiltration resistances are within the 20% error band compared to the calculated infiltration resistance from EnergyPlus. This preliminary study shows the EKF based estimator with the proposed measurements is promising for building infiltration estimation.
Non-technical loss fraud detection in smart grid
(University of Alabama Libraries, 2017) Han, Wenlin; Xiao, Yang; University of Alabama Tuscaloosa
Utility companies consistently suffer from the harassing of Non-Technical Loss (NTL) frauds globally. In the traditional power grid, electricity theft is the main form of NTL frauds. In Smart Grid, smart meter thwarts electricity theft in some ways but cause more problems, e.g., intrusions, hacking, and malicious manipulation. Various detectors have been proposed to detect NTL frauds including physical methods, intrusion-detection based methods, profile-based methods, statistic methods, and comparison-based methods. However, these methods either rely on user behavior analysis which requires a large amount of detailed energy consumption data causing privacy concerns or need a lot of extra devices which are expensive. Or they have some other problems. In this dissertation, we thoroughly study NTL frauds in Smart Grid. We thoroughly survey the existing solutions and divided them into five categories. After studying the problems of the existing solutions, We propose three novel detectors to detect NTL frauds in Smart Grid which can address the problems of all the existing solutions. These detectors model an adversary's behavior and detect NTL frauds based on several numerical analysis methods which are lightweight and non-traditional. The first detector is named NTL Fraud Detection (NFD) which is based on Lagrange polynomial. NFD can detect a single tampered meter as well as multiple tampered meters in a group. The second detector is based on Recursive Least Square (RLS), which is named Fast NTL Fraud Detection (FNFD). FNFD is proposed to improve the detection speed of NFD. Colluded NTL Fraud Detection (CNFD) is the third detector that we propose to detect colluded NTL frauds. We have also studied the parameter selection and performance of these detectors.
A novel demand response mechanism in smart community with learning-based neural network modeling
(University of Alabama Libraries, 2017) Lin, Bo; Li, Shuhui; University of Alabama Tuscaloosa
This thesis investigates how to develop a learning-based demand response mechanism for a novel smart community that can minimize the energy cost both for the smart community system operator and for the residents while meeting all requirements for all entities. The thesis first proposes a demand response centralized energy management strategy for the grid-connected smart community with distributed energy resources. The results show that the energy management system can make proper day ahead schedule based on forecasted load and weather information, utility curtailment condition and user priority settings. Then the thesis proposed a novel learning-based NARX (nonlinear autoregressive network with exogenous inputs) energy consumption model for the Smart Community. The mode consists of a certain size of smart homes all equipped with smart meters and typical controllable electric appliances. Different modeling methods for HVAC, electric water heater, cloth dyer, electric vehicle and energy storage system are analyzed. The performance of the NARX model shows the accuracy of applying NARX to Smart Community modeling. After that, the thesis proposed a genetic algorithm based optimal method to solve the energy cost function. After simulation under different load curtailment conditions, the validation of the proposed method is proved as it can greatly lower the total energy cost from the grid.
Uncertainty quantifications and operation optimization of buildings as virtual batteries for the grid with high penetrations of renewables
(University of Alabama Libraries, 2017) Niu, Fuxin; O'Neill, Zheng; University of Alabama Tuscaloosa
In the United States, 40% of the nation’s total energy consumption and 75% of the total electricity consumption are consumed by commercial and residential buildings, and those numbers are projected to be 46% and 80% by year 2035. Primary energy source is limited, and quickly consumed in the modern world. Renewable energy sources such as solar, wind, and biogas have been fully recognized as reliable, environment friendly, and efficient options for low carbon society and sustainable development. A sustainable energy future and net zero energy buildings require high penetration of Renewable Energy Source (RES) such as wind and solar. However, the constrained availability and variability/intermittency of RES present a great challenge to integrate wind and solar energy into the power grid at a large scale. There is a huge potential for building energy cost savings through a proactive integration with the power grid. The key to improve building operation efficiency is to coordinate and optimize the operation of various energy sources with time-sensitive electricity price. In addition, Energy storage systems (e.g., electric battery, thermal storage from chilled water storage tank, etc.) increase the complexity of the whole system integration. Besides the external energy storage equipment such as battery and thermal tank, the building itself also has a hug thermal storage capacity. The thermal mass of buildings has been proved to be able to reduce energy cost and peak demand through precooling strategy. Currently the majority analysis on the building to grid integration system focused on the building demand side management such as how much energy can be saved, and how much operation cost can be reduced. However, the stability of the power grid itself is very important. Currently, the ancillary equipment including flywheels and batteries has high initial cost and operation maintenance fee. Effectively utilizing the existing HVAC equipment and system to provide grid frequency regulation service will help achieve a better building-to-grid integration In order to fully understand the operation of building-to-grid integration system with renewable energy. This dissertation conducted the following research. First, the operation optimization of building-to-grid integration system with renewable energy of solar PV was conducted. Second, the optimal operation schedule of the building with the precooling control strategy using the building thermal mass was investigated. Third, the uncertainty and sensitivity analysis on the operation optimization of the building-to-grid integration system and building with precooling control strategy was performed. . Furthermore, the research on the frequency regulation service from building HVAC system was explored. To solve the proposed optimization problem, there is a need to have accurate forecasting of building energy consumption and solar radiation. Data-driven models such as AutoregRessive model with eXternal inputs (ARX), State Space (SS), Subspace State Space (N4S), and Bayesian Network model (BN) for building energy consumption prediction was evaluated. And recurrent neural network (RNN) based deep learning algorithm for solar radiation prediction was conducted. This dissertation research’s goals are (1) to improve building operation efficiency of building-to-grid integration system by optimizing the operation of various energy sources with time-sensitive electricity price; (2) to improve building operation efficiency with precooling control strategy through building thermal mass; (3) to improve power grid stability by providing frequency regulation service through building HVAC system intelligent operations. Through the dissertation work, the following conclusions were achieved. BN model is more accurate for building energy consumption prediction of all selected data-driven prediction models. The deep learning based RNN model improved the solar radiation prediction performance comparing with the artificial neural network (ANN) model. The accuracy for the ANN and RNN model prediction can be improved with short data sampling frequency and moving window algorithms. Optimization based scheduling for the building-to-grid integration system and the building with a precooling control strategy significantly reduced the building operation cost. Through the uncertainty and sensitivity analysis, it was found that the solar PV area was the most important parameter for the building-to-grid integration system to reduce the operation cost. For the building operation with precooling control strategy, the operation cost is more sensitive to building envelop thermal resistance than the building envelop thermal capacity. Finally, the building intelligent operation also has the contribution to the power stability by providing frequency regulation service.
Using inverse regression models to create gray box models for industrial facilities
(University of Alabama Libraries, 2018) Carpenter, Joseph; Woodbury, Keith A.; O'Neill, Zheng; University of Alabama Tuscaloosa
Industrial facilities account for approximately one third of energy usage in the world, and effective energy assessments of these facilities require a reliable baseline energy model. Commercial and residential buildings are baselined with both simple change-point models and models that are more complex, such as Gaussian process and artificial neural networks, and these models are developed and tested with dense high-frequency data. However, industrial facilities are only baselined using change-point models, and data for the models are typically restricted to monthly utility bills and, therefore, generally sparse data. This investigation first compares the effectiveness of change-point models with that of Gaussian process models for baselining industrial facilities using only monthly utility billing information as data. Two case studies are presented to predict electricity usage and two case studies are presented to predict natural gas usage. Both change-point and Gaussian process models provided similar results, and both models meet the recommended NMBE and CV-RMSE from ASHRAE Guideline 14. Due to the simplicity and straight-forward equations of change-point models, they are better for regression analysis unless uncertainty is required. This study then investigates using three parameter cooling change-point regression models to determine the physical parameters, specifically overall heat transfer coefficient, surface area, and outdoor air mass flow rate of an industrial building through a simulation-based emulator. A simplified industrial building similar in size, energy usage, and physical parameters as a typical industrial facility was simulated in fifteen of the climate zones defined by ASHRAE using a whole building simulation program (i.e., EnergyPlus) to produce hourly data to illustrate and demonstrate the proposed approach. The change-point models showed poor results for finding the physical parameters using ambient air temperature as the independent variable. When using sol-air temperatures as the independent variable the change-point models were able to predict a lumped capacity of building envelope and outdoor air infiltration/ventilation within +/– 25 % error of actual (UA+ṀCP)/COP for most of the climate zones in the U.S.

Browsing by Author "O'Neill, Zheng"

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