Operations Research, Systems Engineering and Industrial Engineering Commons^™

Optimal Demand Response Models With Energy Storage Systems In Smart Grids, Mohemmed Masooud Alhaider

USF Tampa Graduate Theses and Dissertations

This research aims to develop solutions to relieve system stress conditions in electric grids. The approach adopted in this research is based on a new concept in the Smart Grid, namely, demand response optimization. A number of demand response programs with energy storage systems are designed to enable a community to achieve optimal demand side energy management.

The proposed models aim to improve the utilization of the demand side energy through load management programs including peak shaving, load shifting, and valley lling. First, a model is proposed to nd the optimal capacity of the battery energy storage system (BESS) to …

Oversampling Methods For Imbalanced Dataset Classification And Their Application To Gynecological Disorder Diagnosis, Iman Nekooeimehr

USF Tampa Graduate Theses and Dissertations

In many applications, the dataset for classification may be highly imbalanced where most of the instances in the training set may belong to some of the classes (majority classes), while only a few instances are from the other classes (minority classes). Conventional classifiers will strongly favor the majority class and ignore the minority instances. The imbalance problem can occur in both binary data classification and also in ordinal regression. Ordinal regression is a supervised approach for learning the ordinal relationship between classes. Extensive research has been performed for addressing imbalanced datasets for binary classification; however, current methods do not address …

Rule-Based Risk Monitoring Systems For Complex Datasets, Mona Haghighi

USF Tampa Graduate Theses and Dissertations

In this dissertation we present rule-based machine learning methods for solving problems with high-dimensional or complex datasets. We are applying decision tree methods on blood-based biomarkers and neuropsychological tests to predict Alzheimer’s disease in its early stages. We are also using tree-based methods to identify disparity in dementia related biomarkers among three female ethnic groups. In another part of this research, we tried to use rule-based methods to identify homogeneous subgroups of subjects who share the same risk patterns out of a heterogeneous population. Finally, we applied a network-based method to reduce the dimensionality of a clinical dataset, while capturing …

Commuting By Customized Bus: A Comparative Analysis With Private Car And Conventional Public Transport In Two Cities, Tao Liu, Avishai Ceder, Romain Bologna, Benjamin Cabantous

Journal of Public Transportation

Commuting is a major component in the creation of traffic and travel problems. Thus, more attention should be given to its practice. Private car (PC) transport, the dominant mode of commuting in most of the world’s major cities, creates traffic-related social problems such as traffic congestion, traffic fatalities and injuries, and adverse environmental impacts. This study proposes a novel commuting travel mode—a customized bus (CB) transit system that provides advanced, personalized, and flexible demand-interactive minibus service using Internet, telephone, and smartphone apps. The aim was to assess and compare the performance of CB with PC and with conventional public transport …

Renewable Energy Investment Planning And Policy Design, Alireza Ghalebani

USF Tampa Graduate Theses and Dissertations

In this dissertation, we leverage predictive and prescriptive analytics to develop decision support systems to promote the use of renewable energy in society. Since electricity from renewable energy sources is still relatively expensive, there are variety of financial incentive programs available in different regions. Our research focuses on financial incentive programs and tackles two main problem: 1) how to optimally design and control hybrid renewable energy systems for residential and commercial buildings given the capacity based and performance based incentives, and 2) how to develop a model-based system for policy makers for designing optimal financial incentive programs to promote investment …

Mining Dynamic Recurrences In Nonlinear And Nonstationary Systems For Feature Extraction, Process Monitoring And Fault Diagnosis, Yun Chen

USF Tampa Graduate Theses and Dissertations

Real-time sensing brings the proliferation of big data that contains rich information of complex systems. It is well known that real-world systems show high levels of nonlinear and nonstationary behaviors in the presence of extraneous noise. This brings significant challenges for human experts to visually inspect the integrity and performance of complex systems from the collected data. My research goal is to develop innovative methodologies for modeling and optimizing complex systems, and create enabling technologies for real-world applications. Specifically, my research focuses on Mining Dynamic Recurrences in Nonlinear and Nonstationary Systems for Feature Extraction, Process Monitoring and Fault Diagnosis. …