Environmental Effects of Renewable Energy a Machine Learning Approach

dc.contributor.advisorJamshidi, Mo
dc.contributor.authorYetis, Yunus
dc.contributor.committeeMemberPrevost, Jeff
dc.contributor.committeeMemberAhmed, Sara
dc.contributor.committeeMemberKambiz, Tehrani
dc.creator.orcidhttps://orcid.org/0000-0003-2296-8314
dc.date.accessioned2024-03-08T17:34:41Z
dc.date.available2022-08-27
dc.date.available2024-03-08T17:34:41Z
dc.date.issued2020
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThis research study attempts to evaluate the impact of sustainable energy (particularly the increase in the total energy consumption of alternative energy sources) on air pollutant emissions V OCs, PM10, PM2:5, NOx, and SO2. The study describes data and sources of information and recommends a framework for carrying out this approximation based on surveys from the Texas Environmental Quality Commission (TCEQ). The primary goal is to allow a broader awareness of the benefits as well as trade-offs of sustainable energy use in terms of air pollutant emissions relative to its competitors’ non-renewable energy use. Since electricity output from natural gas and renewable energy increases, the shutdown of several Texas coal-fired power plants is becoming highly possible. Throughout this research, the possible effects of these closures on human health, as well as air quality, were examined by connecting a local photochemical framework with a tool for evaluating health implications. The effects ranged widely across thirteen of the major coal-fired power plants in Texas, often by more than one scale factor, even after generation-by-generation normalization. Although a number power plants had marginal effects on emissions at essential sensors, an average result for PM2:5 as well as ozone was observed up to and 0.2 g=m3,and 0.5 parts per billion (ppb) as well as maximum impacts up to 0.9 g=m3 and 3.3 ppb, respectively. Health implications derived mainly from fine particular matter and were more significant in order of magnitude for plants lacking SO2 air filters. Health consequences levels between the two reduced-form models and baseline models were reasonably consistent. This thesis deals with the visibility forecasting task through machine learning techniques using weather station evidence. Visibility is among the most significant consequences of conditions on coal power plant . Low visibility circumstances may have a substantial effect on the safety, resulting in adverse situations, IV triggering incidents, and endangering human health. Realistic visibility modeling plays a crucial role in coal power plant networks based on decision making as well as management. Nevertheless, visibility prediction remains a critical, strenuous activity given the uncertainty and volatility of meteorological parameters, and a subject of considerable interest in coal power plant statewide. This study aims to explore the use of deep learning models for the task of visibility forecasting in one step (i.e., visibility distance estimate for the next hour) utilizing time-series data from ground meteorological stations. The study cites 3 architectures in the learning algorithm: long short-term memory (LSTM), Recurrent Neural Network (RNN), network, and Artificial Neural Network (ANN). Two datasets from Texas were used to evaluate the models.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent101 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/6070
dc.languageen
dc.subjectAir Pollution
dc.subjectData Modelling
dc.subjectDeep Learning
dc.subjectForecasting
dc.subjectMachine Learning
dc.subjectRenewable Energy
dc.subject.classificationElectrical engineering
dc.subject.classificationEnergy
dc.titleEnvironmental Effects of Renewable Energy a Machine Learning Approach
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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