https://m2.mtmt.hu/ hun 2026-10-24 12:00 BookChapter 36088511 ADMIN_APPROVED true 0 false 2025-08-22T08:57:38.820+0000 2025-08-22T08:55:34.457+0000 2025-04-11T17:23:45.583+0000 true 10095207 SALEH SALEH /api/author/10095207 Author SALEH SALEH (Research of Industrial Internet of Things and Industry 4.0 in Enterprise Resource Planning) true 2025-08-22T08:55:31.128+0000 2025-08-22T08:55:31.128+0000 2025-08-22T08:55:34.462+0000 true 10072271 Andódy Katalin /api/admin/10072271 Admin Andódy Katalin (BME admin4) true true false true 25 25 /api/publicationtype/25 PublicationType Könyvrészlet 2 true 25 BookChapter true 10000312 Conference paper true 25 25 /api/publicationtype/25 PublicationType Könyvrészlet 2 true 25 BookChapter /api/subtype/10000312 Konferenciaközlemény SubType Konferenciaközlemény (Könyvrészlet) 228 true 10000312 true 1 /api/category/1 Category Tudományos true 1 Saleh, Rebin 441 446 441 6 2025 IIoT and Industry 4.0 have enabled, in turn, revolutionary approaches toward energy management for commercial buildings by giving way to more intelligent data-driven decision-making mechanisms. The work presented in this paper aims to discuss the role of electronic sensors and embedded systems as an enabler for real-time forecasting of energy consumption in IIoT-enabled infrastructures. The three most common machine learning algorithms, XGBoost, LightGBM, and Random Forest, were compared in comprehensive detail with all types of sensor data: electricity, chilled water, hot water, and steam. Unlike previous research, which has usually focused on a single model or limited evaluation criteria, the research compared multiple models on seven different performance metrics such as RMSE, R2, MAE, SMAPE, MSLE, F1 Score, and Precision. The results have shown that XGBoost has outperformed other models consistently in terms of accuracy, proving its potential in improving energy efficiency and enabling predictive maintenance schemes in smart buildings. These observations enhance overall understanding in terms of effective incorporation of machine learning models into IIoT-centric energy management systems and provide considerable guidance for further development in energy-efficient infrastructure. true NATIONAL 2025 true true true true true false NONE 2025-08-22 false 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 false false false false 2025-07-10T17:24:51.341+0000 Elektronikai Technológia Tanszék (BME / VIK) 2 3 1 2 0 true 547 true 10002 English /api/language/10002 Angol Language Angol true 2 false true 10939 true 10017 /api/country/10017 Country Amerikai Egyesült Államok true 13 /api/city/10939 City Piscataway (NJ), Amerikai Egyesült Államok true 2054246 true 28266592 /api/publicationidentifier/28266592 https://www.worldcat.org/search?q=isbn%3A9798350379365 9798350379365 PublicationIdentifier ISBN: 9798350379365 true 122 ISBN https://www.worldcat.org/search?q=isbn%3A@@@ /api/publicationsource/122 ISBN PlainSource ISBN true true 10002 /api/publicationsourcetype/10002 PublicationSourceType Egyéb true false 122 true false /api/publication/35705765 Szakál Anikó. IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics SAMI 2025. (2025) ISBN:9798350379365 true true 23 23 /api/publicationtype/23 PublicationType Könyv 3 true 23 Book false true 35705765 true true true true true 10000144 Conference proceedings true 23 23 /api/publicationtype/23 PublicationType Könyv 3 true 23 Book /api/subtype/10000144 Konferenciakötet SubType Konferenciakötet (Könyv) 345 true 10000144 false Book 2025 true 1 /api/category/1 Category Tudományos true 1 false Language 10002 /api/language/10002 Angol Angol English true 2 true PersonAuthorship 126150488 /api/authorship/126150488 Saleh, Rebin [SALEH, SALEH (Research of Indus...), szerző] Elektronikai Technológia Tanszék (BME / VIK) 1 0.5 true false false Author 10095207 /api/author/10095207 SALEH SALEH (Research of Industrial Internet of Things and Industry 4.0 in Enterprise Resource Planning) SALEH SALEH true true Saleh Rebin true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 126150489 /api/authorship/126150489 Villanyi, Balazs [Villányi, Balázs János (elektronikai tech...), szerző] Elektronikai Technológia Tanszék (BME / VIK) 2 0.5 false true false Author 10042347 /api/author/10042347 Villányi Balázs János (elektronikai technológia) Villányi Balázs János true 10042347 true Villanyi Balazs true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PublicationIdentifier 29232105 /api/publicationidentifier/29232105 DOI: 10.1109/SAMI63904.2025.10883313 PlainSource 6 /api/publicationsource/6 DOI PublicationSourceType 10001 /api/publicationsourcetype/10001 DOI true true true DOI DOI https://doi.org/@@@ true true 6 true IDENTICAL 10.1109/SAMI63904.2025.10883313 https://doi.org/10.1109/SAMI63904.2025.10883313 false true PublicationIdentifier 29730914 /api/publicationidentifier/29730914 Scopus: 105013063355 PlainSource 3 /api/publicationsource/3 Scopus PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true Scopus Scopus http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-@@@ true true 3 true 105013063355 http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-105013063355 false true PublicationIdentifier 29232106 /api/publicationidentifier/29232106 Egyéb URL: https://ieeexplore.ieee.org/document/10883313/ PlainSource 40 /api/publicationsource/40 Egyéb URL PublicationSourceType 10006 /api/publicationsourcetype/10006 Link true true true Egyéb URL Other URL @@@ true true 40 true https://ieeexplore.ieee.org/document/10883313/ https://ieeexplore.ieee.org/document/10883313/ false true Keyword 3692 /api/keyword/3692 Learning systems true 3692 true Keyword 6535 /api/keyword/6535 ENERGY true 6535 true Keyword 7802 /api/keyword/7802 Forecasting true 7802 true Keyword 12390 /api/keyword/12390 machine learning true 12390 true Keyword 17169 /api/keyword/17169 Information Management true 17169 true Keyword 1008436 /api/keyword/1008436 decision making true 1008436 true Keyword 1013682 /api/keyword/1013682 Energy management true 1013682 true Keyword 1014077 /api/keyword/1014077 Embedded systems true 1014077 true Keyword 1014520 /api/keyword/1014520 Learning algorithms true 1014520 true Keyword 1072827 /api/keyword/1072827 Machine-learning true 1072827 true Keyword 1141797 /api/keyword/1141797 Intelligent buildings true 1141797 true Keyword 1176158 /api/keyword/1176158 random forest true 1176158 true Keyword 1186319 /api/keyword/1186319 Ensemble learning true 1186319 true Keyword 1195951 /api/keyword/1195951 Predictive models true 1195951 true Keyword 1266704 /api/keyword/1266704 RANDOM FORESTS true 1266704 true Keyword 1293576 /api/keyword/1293576 Office buildings true 1293576 true Keyword 1294170 /api/keyword/1294170 Predictive modeling true 1294170 true Keyword 1672441 /api/keyword/1672441 XGBoost true true Keyword 1698938 /api/keyword/1698938 Energy consumption forecasting true true Keyword 1728717 /api/keyword/1728717 Energy management systems true true Keyword 1729209 /api/keyword/1729209 Energy-consumption true true Keyword 1937980 /api/keyword/1937980 LightGBM true true Keyword 1954803 /api/keyword/1954803 Learning techniques true true Keyword 3680658 /api/keyword/3680658 predictive maintenance true true Reference 65322018 /api/reference/65322018 1. Leo 2019: Machine learning for load forecasting and power grid optimization., J. Energy Syst., 12, p. 221 1 false true Reference 65322019 /api/reference/65322019 2. Charde 2020: Scalability of Machine Learning Models in IoT Networks for Energy Efficiency., International Journal of IoT Applications, 8, p. 34 2 false true Reference 65322020 /api/reference/65322020 3. Moghadasi 2021: Predicting Industrial Energy Consumption Using Gradient Boosting Machines., Energy Management and Systems, 10, p. 99 3 false true Reference 65322021 /api/reference/65322021 4. Wu 2020: Random Forest for Energy Consumption Prediction in Smart Buildings., Smart Buildings Journal, 5, p. 100 4 false true Reference 65322022 /api/reference/65322022 5. DOI: 10.1109/smartgridcomm52983.2022.9961051 5 10.1109/smartgridcomm52983.2022.9961051 false true Reference 65322023 /api/reference/65322023 6. 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Zhang 2020: Improving Building Energy Efficiency Using Predictive Models, IEEE 20 false true Reference 65322038 /api/reference/65322038 21. Nguyen 2021: Assessing Energy Load Forecasting Techniques, IEEE 21 false true Reference 65322039 /api/reference/65322039 22. Lee 2022: Forecasting Power Demand Using SMAPE and Machine Learning, IEEE 22 false true /api/publication/36088511 Saleh Rebin et al. Harnessing IIoT and Industry 4.0 Paradigms for Advanced Energy Consumption Forecasting using Ensemble Learning Techniques. (2025) Megjelent: IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics SAMI 2025 pp. 441-446<div class="BookChapter Publication long-list"> <div class="authors"> <img title="Forrásközlemény" style="float: left" src="/frontend/resources/grid/publication-core-icon.png"> <div class="autype autype0"> <span class="author-name" mtid="10095207"><a href="/gui2/?type=authors&mode=browse&sel=10095207" target="_blank">Saleh Rebin (<span class="authorship-author-name">SALEH SALEH</span> <span class="authorAux-mtmt"> Research of Industrial Internet of Things and I...</span>) </a> </span> <span class="author-affil"><span title="Budapesti Műszaki és Gazdaságtudományi Egyetem">BME</span>/<span title="Villamosmérnöki és Informatikai Kar">VIK</span>/Elektronikai Technológia Tanszék</span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10042347"><a href="/gui2/?type=authors&mode=browse&sel=10042347" target="_blank">Villanyi Balazs (<span class="authorship-author-name">Villányi Balázs János</span> <span class="authorAux-mtmt"> elektronikai technológia</span>) </a> </span> <span class="author-affil"><span title="Budapesti Műszaki és Gazdaságtudományi Egyetem">BME</span>/<span title="Villamosmérnöki és Informatikai Kar">VIK</span>/Elektronikai Technológia Tanszék</span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;36088511" target="_blank">Harnessing IIoT and Industry 4.0 Paradigms for Advanced Energy Consumption Forecasting using Ensemble Learning Techniques</a></div> <div class="InBook"><div class="chapter-in">In:</div> <div class="authors"> <div class="autype autype-1"> <span class="author-name" mtid="10039071"><a href="/gui2/?type=authors&mode=browse&sel=10039071" target="_blank">Szakál Anikó (<span class="authorship-author-name">Szakál Anikó</span> <span class="authorAux-mtmt"> műszaki informatika</span>) </a> </span> <span class="author-affil">Óbudai Egyetem</span> (szerk.) </div> </div> <div class="booktitle"><a href="/gui2/?mode=browse&params=publication;35705765" target="_blank">IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics SAMI 2025 </a></div> <div class="conference"> Konferencia helye, ideje: <span class="location">Stará Lesna, Szlovákia <span class="conference-date">2025.01.23. - 2025.01.25.</span> </div> <span class="publishedAt">Piscataway (NJ): <span class="publishers">Institute of Electrical and Electronics Engineers (IEEE)</span>, <span class="page"> pp 441-446 </span> <span class="year">(2025)</span> ( <span class="seriesTitle">Proceedings - International Symposium on Applied Machine Intelligence and Informatics, SAMI</span> <span class="issn">2836-0834</span> <span class="issn">2767-9438</span> ) </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="10.1109/SAMI63904.2025.10883313" target="_blank" href="https://doi.org/10.1109/SAMI63904.2025.10883313"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:black" title="105013063355" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-105013063355"> Scopus </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:black" title="https://ieeexplore.ieee.org/document/10883313/" target="_blank" href="https://ieeexplore.ieee.org/document/10883313/"> Egyéb URL </a> </span> </span> <span class="bookchapter-ids">Befoglaló link(ek):</span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <span class="isbnOrIssn"> ISBN: </span> <a style="color:black" title="9798350379365" target="_blank" href="https://www.worldcat.org/search?q=isbn%3A9798350379365"> 9798350379365 </a> </span> </span> <div class="mtid"><span class="long-pub-mtid">Közlemény: 36088511</span> | <span class="status-data status-ADMIN_APPROVED"> Admin láttamozott </span> <span class="long-book-mtid">Befoglaló: 35705765</span> Forrás | <span class="type-subtype">Könyvrészlet ( Konferenciaközlemény ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">kézi felvitel</span> </div> <div class="lastModified">Utolsó módosítás: 2025.08.22. 10:55 Andódy Katalin (BME admin4) </div> </div> </div>