[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122085-en":3,"doc-seo-122085-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122085,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","INTEGRATING TRANSACTIVE ENERGY AND MACHINE LEARNING FOR RE - ENERGIZING WASTEWATER TREATMENT PLANTS","This thesis integrates transactive energy concepts with machine learning to support the re-energizing of wastewater treatment plants (WWTPs). It frames renewable energy and power demand within WWTP and water treatment contexts, then develops short-term wind speed forecasting using machine learning to improve energy predictability. The study characterizes wind generation, evaluates regression approaches including recurrent neural networks, long short-term memory, and ensemble models, and compares forecasting performance with defined metrics. It further examines transactive energy operations for energy trading in WWTP facilities.","INTEGRATING TRANSACTIVE ENERGY AND MACHINE LEARNING FOR RE  \nENERGIZING WASTEWATER TREATMENT PLANTS  \nby  \nShriyank Somvanshi, B. Tech.  \nA thesis submitted to the Graduate Council of  \nTexas State University in partial fulfillment  \nof the requirements for the degree of  \nMaster of Science  \nwith a Major in Engineering  \nMay 2023  \nCommittee Members:  \nTongdan Jin, Chair  \nKeisuke Ikehata  \nEmily Zhu Fainman  \nCOPYRIGHT  \nby Shriyank Somvanshi  \n2023  \nDEDICATION  \nDedicated to my father, family, friends, and all those who have supported me throughout this journey. Their unwavering encouragement and belief in me has been the driving force behind my success. I am grateful for their constant love and support.  \nACKNOWLEDGMENTS  \nI would like to express my sincere gratitude to my supervisor Dr. Tongdan Jin, for his guidance, encouragement, and support throughout my research work. His expertise, insightful feedback, and patience were instrumental in shaping my research ideas and in guiding me through the challenging phases ofthis project.  \nI would also like to thank the members of my thesis committee Dr. Keisuke Ikehata and Dr. Emily Zhu Fainman for their valuable guidance and expertise on domain knowledge of wastewater treatment and machine learning algorithms, respectively, which have greatly contributed to the quality and rigor of this thesis.  \nI am grateful to Industrial Engineering and Civil Engineering programs at Texas State University, for providing me with the financial support to conduct the cutting-edge research, and for providing the necessary resources and facilities to carry out numerical experiments. I would like to thank my colleagues for their stimulating discussions and for creating a supportive work environment that made this research experience enjoyable and fulfilling.  \nFinally, I want to express my heartfelt appreciation to my family, for their love, encouragement, and unwavering support throughout my academic journey. Without their constant encouragement and sacrifices, this achievement would not have been possible.  \nThank you all for being a part of my journey and for contributing to the successful completion of my master's thesis.  \nTABLE OF CONTENTS  \nPage  \nLIST OF TABLES ........................................................................................................................ vii  \nLIST OF FIGURES ..................................................................................................................... viii  \nLIST OF ABBREVIATIONS..........................................................................................................x  \nABSTRACT.................................................................................................................................. xii  \nCHAPTER  \n1. INTRODUCTION AND LITERATURE REVIEW .......................................................1  \n1.1 Background and Motivation ..............................................................................1  \n1.1.1 Wastewater Properties .............................................................................. 1  \n1.1.2 Types of Wastewater Treatment ...............................................................2  \n1.2 The State of the Art ............................................................................................3  \n1.2.1 Wastewater Treatment Technologies........................................................3  \n1.2.2 Energy Sources in Wastewater Treatment ................................................9  \n1.2.3 Energy Consumption in Wastewater Treatment .....................................12  \n1.2.4 Machine Learning in Wind Speed Forecasting.......................................16  \n1.3 Research Objectives and Contributions ...........................................................17  \n2. RENEWABLE ENERGY AND POWER DEMAND OF WWTP ...............................20  \n2.1 Renewable Energy Enabled Potable Water Reuse System..............................20  \n2.1.1 S","cbCaifmZQ9pFcgXY","https://ap.wps.com/l/cbCaifmZQ9pFcgXY","pdf",4016712,1,163,"English","en",105,"# List of Tables\n# List of Figures\n# List of Abbreviations\n# Abstract\n# Chapter 1. Introduction and Literature Review\n## Background and Motivation\n## The State of the Art\n## Research Objectives and Contributions\n# Chapter 2. Renewable Energy and Power Demand of WWTP\n## Renewable Energy Enabled Potable Water Reuse System\n## Power Load Profile\n# Chapter 3. Machine Learning Approach to Short-Term Wind Speed Forecasting\n## Characterizing Wind Generation\n## Wind Speed Forecasting using Machine Learning\n## Regression Models\n## Comparison of Regression Models\n# Chapter 4. Transactive Energy Operations in WWTP Facility\n## Transactive Energy Trading as Emerging Market","[{\"question\":\"What problem does this thesis address for wastewater treatment plants?\",\"answer\":\"It addresses how to re-energize wastewater treatment plants by combining transactive energy operations with machine learning-based forecasting to better manage energy inputs and trading decisions.\"},{\"question\":\"How is wind speed forecasting used in the proposed approach?\",\"answer\":\"Wind speed forecasting is used to improve energy predictability; the thesis characterizes wind generation and then builds forecasting models using machine learning.\"},{\"question\":\"Which machine learning model types are evaluated and compared?\",\"answer\":\"The thesis evaluates regression models including recurrent neural networks, long short-term memory, and an ensemble model, and compares them using defined forecasting performance metrics.\"}]","INTEGRATING TRANSACTIVE ENERGY AND MACHINE LEARNING FOR RE - ENERGIZING WASTEWATER TREATMENT PLANTS | PDF",1785808744,411,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"integrating-transactive-energy-and-machine-learning-for-re-energizing-wastewater-treatment-plants","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/integrating-transactive-energy-and-machine-learning-for-re-energizing-wastewater-treatment-plants/122085/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this thesis address for wastewater treatment plants?","Question",{"text":75,"@type":76},"It addresses how to re-energize wastewater treatment plants by combining transactive energy operations with machine learning-based forecasting to better manage energy inputs and trading decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is wind speed forecasting used in the proposed approach?",{"text":80,"@type":76},"Wind speed forecasting is used to improve energy predictability; the thesis characterizes wind generation and then builds forecasting models using machine learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model types are evaluated and compared?",{"text":84,"@type":76},"The thesis evaluates regression models including recurrent neural networks, long short-term memory, and an ensemble model, and compares them using defined forecasting performance metrics.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]