[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120571-en":3,"doc-seo-120571-105":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},120571,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Predicting Energy Consumption Using Machine Learning - Dissertation","Electricity is the primary source of energy worldwide, and forecasting power consumption is a critical analytical task. Prior studies report limited accuracy due to the non-linear characteristics of electricity usage patterns. This dissertation applies a data mining approach (KDD) to predict power demand from multiple engineered features. Feature engineering and selection are used to identify key variables, followed by building and comparing statistical and deep learning models, including linear regression, k-nearest neighbour, and random forest. Model performance is assessed using RMSE, MAE, and MAPE, with linear regression achieving the lowest error.","Predicting Energy Consumption Using  \nMachine Learning  \nby  \nA dissertation submitted in partial fulfilment of the requirements for the degree of  \nMSc Business Analytics  \nDublin Business School  \nSupervisor: Submitted By:  \nMr. Yelamisew Abgaz Mayank Chhabra............. 10638489  \nDeclaration  \nI declare that except where specific reference is made to the work of others, the contents of this dissertation are original and have not been submitted in whole or in part for consideration for anyother degree or qualification in this or any other university. This dissertation is my work and contains nothing which is the outcome of work done in collaboration with others except as specified in the text and Acknowledgements.  \nAcknowledgements  \nThis work seemed possible due to a significant amount of support and academic Knowledge provided by the professor Yelamisew Abgaz. He helped me select the right project and helped by giving me ideas for scrapping and writing an effective code to achieve the goal. His guidance and ideas helped me in writing a perfect report in a short span.  \nI would also like to thank my peers and friends for helping me gain insights towards completing this project. I came to know lots of insights from them. They provided a relaxed environment where I never felt any stress during the dissertation period.  \nAbstract  \nElectricity is the primary source of energy all around the world, and predicting the given commodity is a substantial topic. Previous research has shown low accuracy in the given topic because of thenon-linear electricity consumption pattern. Our research tries to incorporate data mining methodology-KDD to predict the power consumption based on various features. The study has used various feature Engineering techniques like F regression to find the most critical variables and build statistical and deep learning models like linear regression k-nearest neighbour and Random forest. The study has incorporated statistical models to understand the relationship between the dependent and independent variables and justify the results of the machine learning models, building a comparative study. These models are evaluated on different metrics like RMSE, MAE and MAPE, and linear regression has achieved the Minimum error and can be used for the power consumption.  \nContents  \nAbstract ........................................................................................................................................... 4  \nCHAPTER I – INTRODUCTION.................................................................................................. 7  \n1.1 Background ............................................................................................................................... 7  \n1.2 Business Problem ...................................................................................................................... 7  \n1.3 Research Questions ................................................................................................................... 8  \n1.4 Research aim ............................................................................................................................. 8  \n1.5 Objectives ................................................................................................................................. 9  \n1.6 Chapter Overview ..................................................................................................................... 9  \nCHAPTER II– LITERATURE SURVEY .................................................................................... 10  \nCHAPTER III– METHODOLOGY ............................................................................................. 20  \n3.1 Introduction ............................................................................................................................. 20  \n3.2 Data Source and Collection ........................................................................................","cbCaiiIqZLDWNn7V","https://ap.wps.com/l/cbCaiiIqZLDWNn7V","pdf",1279877,1,34,"English","en",105,"# Abstract\n# Chapter I - Introduction\n## Background\n## Business Problem\n## Research Questions\n## Research Aim\n## Objectives\n## Chapter Overview\n# Chapter II - Literature Survey\n# Chapter III - Methodology\n## Data Source and Collection\n## Cleansing the Electric Power Consumption Data\n## Exploratory Data Analysis\n## Feature Selection and Engineering\n## Model Selection\n## Model Interpretation and Evaluation\n# Chapter IV - Results and Evaluation\n## Results\n## Evaluation\n# Chapter V - Conclusion and Future Work\n# References","[{\"question\":\"What data mining approach is used to predict power consumption?\",\"answer\":\"The study uses a KDD (Knowledge Discovery in Databases) methodology to predict power consumption based on multiple features.\"},{\"question\":\"Which models are built and compared in the dissertation?\",\"answer\":\"It builds statistical and deep learning models, including linear regression, k-nearest neighbour, and random forest, and then conducts a comparative evaluation.\"},{\"question\":\"How is model performance evaluated and which model performs best?\",\"answer\":\"Models are evaluated using RMSE, MAE, and MAPE. Linear regression achieves the minimum error and is suitable for power consumption prediction.\"}]","Predicting Energy Consumption Using Machine Learning - Dissertation | PDF",1785730711,86,{"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},"predicting-energy-consumption-using-machine-learning-dissertation","",{"@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/predicting-energy-consumption-using-machine-learning-dissertation/120571/",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","2026-08-03",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 data mining approach is used to predict power consumption?","Question",{"text":75,"@type":76},"The study uses a KDD (Knowledge Discovery in Databases) methodology to predict power consumption based on multiple features.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models are built and compared in the dissertation?",{"text":80,"@type":76},"It builds statistical and deep learning models, including linear regression, k-nearest neighbour, and random forest, and then conducts a comparative evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated and which model performs best?",{"text":84,"@type":76},"Models are evaluated using RMSE, MAE, and MAPE. Linear regression achieves the minimum error and is suitable for power consumption prediction.","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"]