Integrating Transactive Energy and Machine Learning For Re-Energizing Wastewater Treatment Plants

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dc.contributor.advisorJin, Tongdan
dc.contributor.authorSomvanshi, Shriyank
dc.contributor.committeeMemberIkehata, Keisuke
dc.contributor.committeeMemberFainman, Emily Zhu
dc.date.accessioned2023-05-01T21:09:28Z
dc.date.available2023-05-01T21:09:28Z
dc.date.issued2023-05
dc.description.abstractA large wastewater treatment plant (WWTP) typically consumes 300-500 MWh of electricity per day and often relies on the grid power generated by burning fossil fuels. Wind- and solar-based distributed generation emerged as a clean energy solution to achieve environmental sustainability and net-zero performance. This study investigates power consumption trends in WWTP facilities using six and nine years of data, respectively. The study leverages machine learning algorithms for wind speed and power load forecasting. Particularly, recurrent neural network (RNN), long short-term memory (LSTM), and ensemble models are adopted as intelligent computing tool for generation forecasting. A regression model was developed to forecast the power output of onsite wind turbines. Managerial insights were obtained regarding the most effective model for wind power forecasting and load prediction of the WWTP in Melbourne, Australia, and the water treatment plant in San Marcos, Texas. The following research findings are obtained. First, when multiple criteria along with forecasting wind speed are considered, the RNN model provides much better prediction than the LSTM and ensemble models. Second, when integrated with two or more low performance neural network models, the ensemble model can yield more accurate results by collectively increasing their predicting accuracy. Third, the integration of renewable transactive energy and blockchain technology has the potential to realize peer-to-peer energy trading, in which electricity is sold directly between prosumers and consumers without the intermediaries. Future research could investigate other machine learning algorithms, such as convolutional neural networks, for improving wind speed or solar irradiance forecasting, and extend the machine learning based computing tools to residential, commercial, and other industrial prosumers.
dc.description.departmentEngineering
dc.formatText
dc.format.extent163 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationSomvanshi, S. (2023). Integrating transactive energy and machine learning for re-energizing wastewater treatment plants (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/16695
dc.language.isoen
dc.subjectrenewable energy
dc.subjectmachine learning
dc.titleIntegrating Transactive Energy and Machine Learning For Re-Energizing Wastewater Treatment Plants
dc.typeThesis
thesis.degree.departmentEngineering
thesis.degree.disciplineEngineering
thesis.degree.grantorTexas State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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