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The work proposes a new optimized variant of machine learning algorithms and trains and tests it on reference datasets for IIT, India, provided by Smart Energy Informatics Lab. Performance is verified on another campus using a Pakistan dataset, with comparison, optimization, and evaluation steps to improve forecast load accuracy.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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is the main purpose of the thesis?","Question",{"text":63,"@type":64},"To develop an optimized variant of machine learning algorithms for data-driven electrical energy efficiency management, focusing on improving energy load prediction for university campuses.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which datasets were used for training and testing?",{"text":68,"@type":64},"A reference dataset for IIT, India was used for training and testing, and performance was further verified using datasets from another campus environment in Pakistan.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the thesis structure its study work?",{"text":72,"@type":64},"It includes a comprehensive comparative phase across different machine learning algorithms for energy load forecasting, followed by optimization of the best candidate algorithm, and then evaluation of the enhanced forecasting 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MANAGEMENT (D2EEM)  \nSHAMIM AKHTAR  \nDOCTOR OF PHILOSOPHY  \nUNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH  \nUNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH  \nDECLARATION OF THESIS AND COPYRIGHT  \nAuthor’s Full Name Date ofBirth  \nTitle  \nAcademic Session  \n: SHAMIM AKHTAR  \n: 01 APRIL 1974  \n: AN OPTIMIZED VARIANT OF MACHINE LEARNING ALGORITHM FOR DATA-DRIVEN ELECTRICAL ENERGY EFFICIENCY MANAGEMENT (D2EEM)  \n: Semester II 2023/24  \nI declare that this thesis is classified as:  \n􀂅 CONFIDENTIAL  \n􀂅 RESTRICTED  \n(Contains confidential information under the Official Secret Act 1997)*  \n(Contains restricted information as specified by the  \norganization where research was done)*  \n􀀻 OPEN ACCESS I agree that my thesis to be published as online open access  \n(Full Text)  \nI acknowledge that Universiti Malaysia Pahang Al-Sultan Abdullah reserves the following rights:  \n1. The Thesis is the Property of Universiti Malaysia Pahang Al-Sultan Abdullah  \n2. The Library of Universiti Malaysia Pahang Al-Sultan Abdullah has the right to make copies of the thesis for the purpose of research only.  \n3. The Library has the right to make copies of the thesis for academic exchange.  \nCertified by:  \n_____________________  \n(Student’s Signature)  \nPassport Number Date: 26/06/2024  \n______________________  \n(Supervisor’s Signature)  \nName of Supervisor  \n_  \nDate: 26/06/2024  \nNOTE : * If the thesis is CONFIDENTIAL or RESTRICTED, please attach a thesis declaration letter.  \nSUPERVISOR’S DECLARATION  \nWe hereby declare that We have checked this thesis, and, in our opinion, this thesis is adequate in terms of scope and quality for the award of the degree of Doctor of Philosophy.  \n(Supervisor’s Signature)  \nFull Name : \"TTPD􀀏 1SPG􀀏 *S􀀏%S .VIBNBE ;BIJN CJO 4BKPE  \nPosition : \"TTPD􀀏1SPG􀀏  \nDate : 􀀓􀀗 +VOF 􀀓􀀑􀀓􀀕  \n_______________________________  \n(Co-supervisor’s Signature)  \nFull Name : 'U􀀑 6D\\HG 6DMMDG +XVVDLQ 5L]ZL  \nPosition : $VVRFLDWH 3URIHVVRU  \nDate : 􀀕􀀙 -XQH 􀀕􀀓􀀕􀀗  \nSTUDENT’S DECLARATION  \nI hereby declare that the work in this thesis is based on my original work except for quotations and citations which have been duly acknowledged. I also declare that it has not been previously or concurrently submitted for any other degree at Universiti Malaysia Pahang Al-Sultan Abdullah or any other institution.  \n_______________________________  \n(Student’s Signature)  \nFull Name : SHAMIM AKHTAR  \nID Number : PES19002  \nDate : 26 June 2024  \nAN OPTIMIZED VARIANT OF MACHINE LEARNING ALGORITHM FOR DATA-DRIVEN ELECTRICAL ENERGY EFFICIENCY MANAGEMENT  \n(D2EEM)  \nSHAMIM AKHTAR  \nThesis submitted in fulfillment of the requirements for the award of the degree of Doctor of Philosophy  \nFaculty of Electrical and Electronics Engineering Technology  \nUNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH  \nACKNOWLEDGEMENTS  \nI would like to express my gratitude to my thesis guide Dr. Muhamad Zahim Bin Sujod for their guidance, advice, and constant support throughout my research work. I would like to thank her for being my advisor here at Universiti Malaysia Pahang. I consider it my good fortune to have got an opportunity to work with such a great researcher and mentor.  \nI am heartily grateful to our department head \"Prof. Ir. Ts. Dr. Hamdan Bin Daniyal \", for his moral support and all the facilities I provided for doing my research work under such a great team. In addition to my official supervisors, I have also had a field supervisor, the most important one being Dr. Syed Sajjad Hussain Rizvi thank you for allowing me to participate in research and always taking on time to discuss knowledge and life in usual with me. Your enthusiasm for research is extremely inspiring, and you were the first person to enthuse me sufficiently to be a decent researcher.  \nI am especially obligated to my parents, wife and children because of their love and support I can get my PhD.  \nFinally, I would also l","cbCaim1dhkQEZuVX","https://ap.wps.com/l/cbCaim1dhkQEZuVX","pdf",5286026,147,"English","# Thesis Declarations and Copyright\n## Classification (Confidential/Restricted/Open Access)\n# Acknowledgements\n# Abstrak","[{\"question\":\"What is the main purpose of the thesis?\",\"answer\":\"To develop an optimized variant of machine learning algorithms for data-driven electrical energy efficiency management, focusing on improving energy load prediction for university campuses.\"},{\"question\":\"Which datasets were used for training and testing?\",\"answer\":\"A reference dataset for IIT, India was used for training and testing, and performance was further verified using datasets from another campus environment in Pakistan.\"},{\"question\":\"How does the thesis structure its study work?\",\"answer\":\"It includes a comprehensive comparative phase across different machine learning algorithms for energy load forecasting, followed by optimization of the best candidate algorithm, and then evaluation of the enhanced forecasting performance.\"}]","AN OPTIMIZED VARIANT OF MACHINE LEARNING ALGORITHM FOR DATA-DRIVEN ELECTRICAL ENERGY EFFICIENCY MANAGEMENT (D2EEM) | PDF",1789642432,370]