[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119998-en":3,"doc-seo-119998-105":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":4,"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},119998,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","SMART HOME ENERGY SAVING WITH BIG DATA AND MACHINE LEARNING - Paper on HEMS-IoT Energy Control - 2024","Escalating energy consumption, especially in housing, drives the need for smart houses that reduce usage while maintaining comfort and safety. The document presents an IoT-enabled home energy management approach that combines Internet of Things connectivity with big data analytics and machine learning. A HEMS-IoT system is proposed using J48 and Weka to learn user behavior and energy consumption profiles. RuleML and Apache Mahout tailor energy-saving recommendations, and a monitoring demonstration validates security, comfort, and conservation outcomes.","SMART HOME ENERGY SAVING WITH BIG DATA AND MACHINE LEARNING  \nHawar Bahzad Ahmad 1, Renas Rajab Asaad 1,*, Saman M. Almufti 1, Ahmed Alaa Hani 1, Amira  \nBibo Sallow3, Subhi R. M. Zeebaree2  \n1 Department of Computer Science, Nawroz University, Duhok, Iraq  \n2 Energy Engineering Department, Duhok Polytechnic University, Duhok, Iraq  \n3 Department of Information Technology, Duhok Polytechnic University, Duhok, Iraq Corresponding author email: [renas.rekany@nawroz.edu.krd](renas.rekany@nawroz.edu.krd)  \nArticle Info  \nRecieved: Mar 02, 2024  \nRevised: Apr 10, 2024  \nAccepted: May 02, 2024  \nOnlineVersion: May 14, 2024  \nAbstract  \nIn response to escalating energy consumption, particularly within the housing sector, a global imperative to reduce energy usage has emerged, propelling the concept of \"smart houses\" to the forefront of innovation. This paradigm shift owes its genesis to the convergence of advancements in energy conversion, communication networks, and information technology, catalyzing the emergence of the Internet of Things (IoT) . The IoT facilitates seamless connectivity of devices via the World Wide Web, enabling remote management, monitoring, and detection capabilities. Capitalizing on this technological synergy, the integration of IoT, big data, and machine learning with home automation systems holds immense promise for enhancing energy efficiency. This paper introduces HEMS-IoT, a groundbreaking energy control system for intelligent homes, underpinned by big data analyticsand machine learning algorithms, prioritizing security, convenience, and energy conservation. Leveraging J48 neural network technology and the Weka API, the study illuminates user behaviors and energy consumption patterns, enabling household classification based on energy usage profiles. Moreover, to ensure user comfort and safety, RuleML and Apache Mahout are deployed to customize energy-saving recommendations tailored to individual preferences. By presenting a practical demonstration of smart home monitoring, this paper validatesthe effectiveness of the proposed approach in enhancing security, comfort, and energy conservation. This pioneering research not only showcases the transformative potential of IoT-driven energy management systems but also sets the stage for a sustainable and interconnected future.  \nKeywords: Big Data, Internet of Things, HEMS-IoT, Smart Home  \n© 2024 by the author(s)  \nThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nINTRODUCTION  \nNew developments in information, communication, and power conversion technologies have led to the emergence of the \"smart home,\" a new housing paradigm. These homes improve comfort, security, usability, and entertainment features while enabling homeowners to reduce energy consumption. As the market for smart homes continues to grow quickly, Home the management of energy Systems (HEMS) are essential to achieving the goals of smart energy houses. Energy-efficient systems, pleasure, lighting, the detection of fires, and other areas are where progress is most noticeable (Elkhorchani, & Grayaa, 2016; Al-Ali et al., 2017; Marinakis et al., 2020; Abdulqadir et al., 2021) .  \nInterior comfort and ecological responsibility are becoming more and more integrated into domestic action plans as measures to conserve energy gain global traction. It is critical to acknowledge the diversity of demands and lifestyles that impact consumer behaviour patterns when it comes to energy use. In order to develop effective resource-saving strategies, it is imperative that one recognises the interplay between ease of use, electrical usage, and occupant needs (Salman et al., 2016; Li et al., 2018; Sadeeq et al., 2021) . This can be achieved by balancing the priority of interior comfort vs power conservation. Because Internet of Things (IoT","cbCaifbrlYG2iwSt","https://ap.wps.com/l/cbCaifbrlYG2iwSt","pdf",323836,1,10,"English","en",105,"# Introduction\n## Smart home and HEMS concept\n## Role of IoT, big data, and machine learning\n# Literature Review\n## IoT for energy monitoring and control\n## Big data and machine learning initiatives\n# Proposed System\n## System architecture and workflow","[{\"question\":\"What problem does the paper address in smart homes?\",\"answer\":\"It addresses the need to reduce energy consumption in the housing sector while improving comfort and security through intelligent energy management.\"},{\"question\":\"How does HEMS-IoT use data and machine learning?\",\"answer\":\"It leverages big data analytics and machine learning to analyze user behaviors and energy consumption patterns, enabling classification of household energy usage profiles.\"},{\"question\":\"How are personalized energy-saving recommendations generated?\",\"answer\":\"RuleML and Apache Mahout are deployed to customize recommendations based on individual preferences while considering comfort and safety.\"}]","SMART HOME ENERGY SAVING WITH BIG DATA AND MACHINE LEARNING - 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