{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:47:06Z","timestamp":1772556426054,"version":"3.50.1"},"reference-count":27,"publisher":"International Association of Online Engineering (IAOE)","issue":"1","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Eng. Ped."],"abstract":"<jats:p>A substantial percentage of the world\u2019s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when compared to a baseline building management system, all while keeping indoor thermal comfort levels high. According to the study, one effective way to make school buildings smart, eco-friendly, and energy efficient is to use a hybrid AI-driven method.<\/jats:p>","DOI":"10.3991\/ijep.v16i1.60437","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:33:23Z","timestamp":1772548403000},"source":"Crossref","is-referenced-by-count":0,"title":["Design of a Hybrid AI-Driven Engineering Model for Energy-Efficient and Sustainable Educational Systems"],"prefix":"10.3991","volume":"16","author":[{"family":"Ibtihal R. Niama ALRubeei","sequence":"first","affiliation":[]},{"family":"Hussain Ali Mutar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5201-2084","authenticated-orcid":false,"given":"Haider TH. Salim","family":"ALRikabi","sequence":"additional","affiliation":[]},{"family":"Ihab L. Hussein Alsammak","sequence":"additional","affiliation":[]},{"family":"Huda Abbas Kanber","sequence":"additional","affiliation":[]},{"family":"Ban Hassan Majeed","sequence":"additional","affiliation":[]}],"member":"2371","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"38081","doi-asserted-by":"crossref","unstructured":"[1] F. A. Alfaoyzan and R. A. Almasri, \"Benchmarking of energy consumption in higher education buildings in Saudi Arabia to be sustainable: Sulaiman Al-Rajhi University case,\" Energies, vol. 16, no. 3, p. 1204, 2023, doi: https:\/\/doi.org\/10.3390\/en16031204.","DOI":"10.3390\/en16031204"},{"key":"38083","doi-asserted-by":"crossref","unstructured":"[2] Y. Zhao, C. Zhang, Y. Zhang, Z. Wang, and J. 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Alsahag, \"Hybrid Multi-Stage Forecasting for Sustainable Electricity Demand Planning in the Netherlands,\" 2025, doi: https:\/\/doi.org\/10.3390\/su17167192.","DOI":"10.20944\/preprints202505.1823.v1"},{"key":"38113","doi-asserted-by":"crossref","unstructured":"[17] G. Ramos Ruiz, E. Lucas Segarra, and C. Fern\u00e1ndez Bandera, \"Model predictive control optimization via genetic algorithm using a detailed building energy model,\" Energies, vol. 12, no. 1, p. 34, 2018, doi: https:\/\/doi.org\/10.3390\/en12010034.","DOI":"10.3390\/en12010034"},{"key":"38115","doi-asserted-by":"crossref","unstructured":"[18] W. Cao et al., \"Short-term energy consumption prediction method for educational buildings based on model integration,\" Energy, vol. 283, p. 128580, 2023, doi: https:\/\/doi.org\/10.1016\/j.energy.2023.128580.","DOI":"10.1016\/j.energy.2023.128580"},{"key":"38117","doi-asserted-by":"crossref","unstructured":"[19] M. 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Arun et al., \"Fuzzy logic-supported building design for low-energy consumption in urban environments,\" Case Studies in Thermal Engineering, vol. 64, p. 105384, 2024\/12\/01\/ 2024, doi: https:\/\/doi.org\/10.1016\/j.csite.2024.105384.","DOI":"10.1016\/j.csite.2024.105384"},{"key":"38123","doi-asserted-by":"crossref","unstructured":"[22] A. H. M. Alaidi, Z. A. Ramadhan, J. Alrubaye, H. Mutar, and I. Svyd, \"AI-based monkeypox detection model using Raspberry Pi 5 AI Kit,\" Sustainable Engineering and Innovation, vol. 7, no. 1, pp. 1-14, 2025, doi: https:\/\/doi.org\/10.37868\/sei.v7i1.id393.","DOI":"10.37868\/sei.v7i1.id393"},{"key":"38125","doi-asserted-by":"crossref","unstructured":"[23] A. R. Al-Badri and A. H. 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Jasim, \"CARBON FOOTPRINT OF IRAQ\u2019S ENERGY SECTOR: ANALYSIS AND RECOMMENDATIONS FOR A LOW-CARBON FUTURE,\" doi: DOI: 10.29350\/jops."}],"container-title":["International Journal of Engineering Pedagogy (iJEP)"],"original-title":[],"link":[{"URL":"https:\/\/online-journals.org\/index.php\/i-jep\/article\/download\/60437\/17049","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/online-journals.org\/index.php\/i-jep\/article\/download\/60437\/17049","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:33:54Z","timestamp":1772548434000},"score":1,"resource":{"primary":{"URL":"https:\/\/online-journals.org\/index.php\/i-jep\/article\/view\/60437"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,3]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3,3]]}},"URL":"https:\/\/doi.org\/10.3991\/ijep.v16i1.60437","relation":{},"ISSN":["2192-4880"],"issn-type":[{"value":"2192-4880","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,3]]}}}