[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126427-en":3,"doc-seo-126427-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126427,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Comparing Machine Learning Models For Predicting Fuel Consumption In Energy Generation For The Food Processing Industry In Nigeria - Applied Research Project (MSc Data Analytics)","Accurate prediction of fuel consumption is essential for energy management, cost optimization, and environmental pollution control in Nigeria’s food processing industry. This applied research, structured using the CRISP-DM methodology, compares Artificial Neural Networks, Support Vector Machines, and Random Forest models for forecasting fuel consumption in energy generation. The study uses two years of historical data from Flour Mills of Nigeria Plc, including energy generated, gas consumption, gas pressure, and related variables. Model performance is assessed with RMSE, MSE, and R-squared metrics, showing Random Forest as the strongest overall performer.","COMPARING MACHINE LEARNING MODELS FOR PREDICTING FUEL CONSUMPTION IN ENERGY GENERATION FOR THE FOOD PROCESSING INDUSTRY IN NIGERIA  \nChiamaka Praise Ofili  \nApplied Research Project submitted in partial fulfilment of the requirements for the degree of  \nMSc Data Analytics  \nat Dublin Business School  \nSupervised by  \nSamuel Ogwu  \nDECLARATION  \n‘I declare that this Applied Research Project that I have submitted to Dublin Business School for the award of master’s in data Analytics is the result of my own investigations, except where otherwise stated, where it is clearly acknowledged by references. Furthermore, this work has not been submitted for any other degree.’  \nSigned: Chiamaka Praise Ofili  \nStudent Number: 10611520  \nDate: 28th August 2023  \nACKNOWLEDGEMENTS  \nI would like to express my sincere gratitude to my supervisor, Samuel Ogwu, for his invaluable guidance, support, and encouragement throughout the course of my dissertation. His expertise, feedback, and continuous motivation have been instrumental in shaping the direction of my research and enhancing the quality of my work.  \nI am also deeply grateful to my family and friends for their unwavering love and support during this journey. I couldn't have achieved this milestone without their support.  \nI also want to acknowledge the academic community and all the resources that have contributed to the success of this dissertation.  \nABSTRACT  \nAccurate prediction of fuel consumption is crucial for effective energy management, cost optimization and environmental pollution management in the food processing industry in Nigeria. Machine learning models have shown promise in various domains for predicting and optimizing fuel and energy consumption. This research under the CRISP-DM methodology, compared the performance of three machine learning models which include Artificial Neural Networks, Support Vector Machines and Random Forest in predicting fuel consumption in energy generation in a food processing industry in Nigeria.  \nThis research was conducted using historical data on fuel consumption and independent variables such as energy generated, gas consumption, gas pressure, etc. collected over 2 years from Flour Mills of Nigeria Plc.  \nThe performance of the predicted models was evaluated based on Root Mean Square Error (RMSE), Mean Square Error (MSE) and R Square evaluation metrics. The Random Forest Model performed best across most metrics, with the lowest RMSE (8124 .62 & 26061.43) and MSE (6 .6E+07 & 6.8E+08) on both training and testing data, and a relatively high R-squared value (0 .93 & 0.52) . The ANN Model performed reasonably well, but the SVM Model had a comparatively poorer performance.  \nTABLE OF CONTENTS  \nDECLARATION 1  \nACKNOWLEDGEMENTS 2  \nABSTRACT 3  \nCHAPTER ONE: INTRODUCTION 8  \n1.1 BACKGROUND 8  \n1.2 MOTIVATION 9  \n1.3 RESEARCH QUESTIONS 9  \n1.4 RESEARCH AIM AND OBJECTIVES 10  \n1.5 RESEARCH DESIGN 10  \nCHAPTER TWO: OVERVIEW 12  \n2.1 LITERATURE REVIEW 13  \n2.1.1 Artificial Neural Networks 14  \n2.1.2 Support Vector Machine 15  \n2.1.3 Random Forest 17  \n2.2 COMPARISON OF MODELS 19  \nCHAPTER THREE: MAIN BODY 22  \n3.1 BUSINESS UNDERSTANDING 22  \n3.2 DATA UNDESTANDING 23  \n3.3 DATA PREPARATION 24  \n3.4 DATA MODELLING 30  \n3.4.1 Artificial Neutral Networks 30  \n3.4.2 Support Vector Machines 31  \n3.4.3 Random Forest 32  \n3.5 EVALUATION 34  \n3.5.1 Mean Squared Error (MSE) 34  \n3.5.2 Root Mean Squared Error (RMSE) 34  \n3.5.3 R-squared (Coefficient of Determination) 35  \n3.5.4 Results 36  \n3.6 DEPLOYMENT 38  \nCHAPTER FOUR: CONCLUSIONS 40  \n4.1 LIMITATIONS 41  \n4.2 RECOMMENDATIONS 41  \nREFERENCES 43  \nLIST OF FIGURES  \nFigure 1: CRISP DM Model 11  \nFigure 2: Research Design 22  \nFigure 3: Statistical Qualities of Data 25  \nFigure 4: Handling Null Values 26  \nFigure 5: Distribution of Dataset Features 26  \nFigure 6: Probability Distribution of Target Variable 27  \nFigure 7: Correlation Heat Map 27  \nFigure 8: Feature Selection and Scaling 28  \nFigure 9: Scaled Da","cbCaihJpvluNleQb","https://ap.wps.com/l/cbCaihJpvluNleQb","pdf",917593,5,1,48,"English","en",105,"# Abstract\n# Chapter One: Introduction\n## Background\n## Motivation\n## Research Questions\n## Research Aim and Objectives\n## Research Design\n# Chapter Two: Overview\n## Literature Review\n## Comparison of Models\n# Chapter Three: Main Body\n## Business Understanding\n## Data Unsonderstanding\n## Data Preparation\n## Data Modelling\n## Evaluation\n## Deployment\n# Chapter Four: Conclusions\n## Limitations\n## Recommendations\n# References\n# List of Figures\n# List of Tables","[{\"question\":\"Which machine learning models are compared for predicting fuel consumption?\",\"answer\":\"The study compares Artificial Neural Networks, Support Vector Machines, and Random Forest models under the CRISP-DM workflow.\"},{\"question\":\"What data and variables are used in the research?\",\"answer\":\"Historical fuel consumption data and independent variables such as energy generated, gas consumption, and gas pressure are used, collected over two years from Flour Mills of Nigeria Plc.\"},{\"question\":\"How is model performance evaluated and which model performs best?\",\"answer\":\"Performance is measured using RMSE, MSE, and R-squared. Random Forest achieves the best results across most metrics, with the lowest RMSE and MSE on both training and testing data and relatively high R-squared values.\"}]","Comparing Machine Learning Models For Predicting Fuel Consumption In Energy Generation For The Food Processing Industry In Nigeria - Applied Research Project (MSc Data Analytics) | PDF",1785905002,121,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"comparing-machine-learning-models-for-predicting-fuel-consumption-in-energy-generation-for-the-food-processing-industry-in-nigeria-applied-research-project-msc-data-analytics","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/comparing-machine-learning-models-for-predicting-fuel-consumption-in-energy-generation-for-the-food-processing-industry-in-nigeria-applied-research-project-msc-data-analytics/126427/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Which machine learning models are compared for predicting fuel consumption?","Question",{"text":77,"@type":78},"The study compares Artificial Neural Networks, Support Vector Machines, and Random Forest models under the CRISP-DM workflow.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What data and variables are used in the research?",{"text":82,"@type":78},"Historical fuel consumption data and independent variables such as energy generated, gas consumption, and gas pressure are used, collected over two years from Flour Mills of Nigeria Plc.",{"name":84,"@type":75,"acceptedAnswer":85},"How is model performance evaluated and which model performs best?",{"text":86,"@type":78},"Performance is measured using RMSE, MSE, and R-squared. Random Forest achieves the best results across most metrics, with the lowest RMSE and MSE on both training and testing data and relatively high R-squared values.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]