[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118510-en":3,"doc-seo-118510-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118510,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning methods for energy sector in internet of things - Comparative study using Weka","Machine learning methods for IoT applications in the energy sector are analyzed through a comparative study of algorithm behavior, strengths, and limitations. Energy efficiency evaluation targets prediction of energy consumption, using a feature selection step to rank inputs and identify the most influential feature. Experiments are carried out in Weka with linear regression, k-nearest neighbors, decision stump, RBF network, and isotonic regression, and the best-performing approach is determined. Reported minimum errors are 1.546 for cooling load and 1.364 for heating load, with random forest emerging as the most suitable choice within this study.","Machine learning methods for energy sector in internet of  \nthings  \nReyhane Hafezi Fard, Soodeh Hosseini  \nDepartment of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran  \n\n| Article history:\u003Cbr>Received Aug 29, 2023 Revised May 15, 2025 Accepted Jun 10, 2025 |\n| --- |\n| Keywords:\u003Cbr>Deep learning\u003Cbr>Energy sector\u003Cbr>Industrial internet of things Internet of things\u003Cbr>Machine learning algorithms Prediction\u003Cbr>Weka |\n\nCorresponding Author:  \nThis research paper focuses on exploring machine learning studies and conducting a comparative analysis of their advantages, disadvantages, implementation environments, and algorithms. A key aspect of the study involves evaluating the energy efficiency using machine learning algorithms to predict energy consumption. Additionally, a feature selection algorithm is employed to rank the features, with the highest-ranking feature identified as one of the most significant. The experimentation is conducted using the Weka tool, incorporating several machine learning algorithms such as linear regression, k-nearest neighbors, decision stump, radial basis function (RBF) network, and isotonic regression. The RBF algorithm, which relies on RBF, shares similarities with neural network algorithms. Results indicate a minimum error value of 1.546 for cooling load and 1.364 for heating load. The random forest algorithm emerges as the most suitable choice within the context of this study.  \nThis is an open access article under the CC BY-SA license.  \nSoodeh Hosseini  \nDepartment of Computer Science, Faculty of Mathematics and Computer Shahid Bahonar University of Kerman  \nKerman, Iran  \nEmail: [so_hosseini@uk.ac.ir](so_hosseini@uk.ac.ir)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe realm of information technology is constantly evolving, particularly in terms of communication and connectivity across various locations and timeframes. The internet of things (IoT) refers to the interconnectedness and communication between diverse devices [1], [2] . Within the IoT framework, a multitude of valuable information is collected and utilized in smart grid applications [3] . As researchers strive to implement IoT-based networks in practical scenarios [4], an area of focus lies in the prediction and optimization of electricity consumption in residential buildings [5] . The escalating global energy demand resulting from population growth has become a pressing concern for electricity companies, emphasizing the significance of accurate electricity consumption forecasting. Failure to effectively manage energy consumption may lead to energy shortages in the coming years. To address this issue, two approaches are commonly considered: increasing energy production and maximizing the utilization of existing energy resources. While energy production is a costly and resource-intensive solution, employing preventive measures to optimize available energy resources presents a viable alternative [6] .  \nUndoubtedly, the advent of the IoT has had far-reaching implications in our daily lives, reshaping both our environment and society at large. Communication systems researchers often prioritize the communication aspects of the IoT, mistakenly overlooking other crucial factors [7] . The IoT serves as a pivotal source of novel data, and data science plays a crucial role in enhancing the intelligence of IoT applications. Data science integrates various scientific disciplines to uncover patterns and insights through  \ntechniques like data mining and machine learning. Different data mining models such as neural networks, classification, and clustering methods are employed to address diverse problems based on data characteristics [8] . In the context of the IoT, the industrial internet of things (IIoT) emerges as a significant field applicable to power plants [9], while also proving useful in detecting malware within enterprise information systems [10] . By enabling seamless communicat","cbCaifzbYQVF1qGP","https://ap.wps.com/l/cbCaifzbYQVF1qGP","pdf",313726,1,"English","en",105,"# Introduction\n## IoT and smart grid context\n## Energy consumption forecasting motivation\n## Data science and ML/DL in IoT\n# Machine learning focus for IoT and energy sector\n## Feature selection and energy efficiency evaluation\n## Algorithms implemented in Weka\n# Results and discussion\n## Minimum prediction errors for cooling and heating\n## Model suitability (random forest)","[{\"question\":\"What problem does the study address in the energy sector for IoT environments?\",\"answer\":\"The study targets accurate prediction of energy consumption in IoT-based energy applications, emphasizing energy efficiency evaluation to support better energy management in buildings and the broader energy context.\"},{\"question\":\"How are features handled before building prediction models?\",\"answer\":\"A feature selection algorithm ranks the features, and the highest-ranking feature is identified as one of the most significant inputs for the prediction task.\"},{\"question\":\"Which algorithms are tested and what is the main outcome?\",\"answer\":\"Experiments in Weka test linear regression, k-nearest neighbors, decision stump, RBF network, and isotonic regression. The random forest algorithm is reported as the most suitable choice, with minimum errors of 1.546 for cooling load and 1.364 for heating load.\"}]","Machine learning methods for energy sector in internet of things - Comparative study using Weka | PDF",1785683933,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-methods-for-energy-sector-in-internet-of-things-comparative-study-using-weka","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-methods-for-energy-sector-in-internet-of-things-comparative-study-using-weka/118510/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the study address in the energy sector for IoT environments?","Question",{"text":74,"@type":75},"The study targets accurate prediction of energy consumption in IoT-based energy applications, emphasizing energy efficiency evaluation to support better energy management in buildings and the broader energy context.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How are features handled before building prediction models?",{"text":79,"@type":75},"A feature selection algorithm ranks the features, and the highest-ranking feature is identified as one of the most significant inputs for the prediction task.",{"name":81,"@type":72,"acceptedAnswer":82},"Which algorithms are tested and what is the main outcome?",{"text":83,"@type":75},"Experiments in Weka test linear regression, k-nearest neighbors, decision stump, RBF network, and isotonic regression. 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