{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T01:17:59Z","timestamp":1784769479907,"version":"3.55.0"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T00:00:00Z","timestamp":1738627200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004961","name":"Tecnol\u00f3gico de Monterrey","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004961","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The double facade solar chimney along with energy storage components is a sustainable building technology that harnesses the power of the sun to regulate indoor temperatures. Extensive research has been conducted on the theoretical simulation of such systems. The novelty of this work is to explore the potential of explainable artificial intelligence in improving the design and optimization of the double-skin solar chimney. The need for this research is owing to the high computational limitations of the physical model of such systems, thus the application of explainable artificial intelligence based upon artificial neural network can address this research gap. The paper solved a validated physical model and demonstrates the suitability of artificial neural network twins as computational-efficient subrogates that can be later used by a multiobjective optimization function to find the optimal design values for the facades of double-skin buildings. The results of the comparison between the physical and the artificial neural network model show the practical advantage of utilizing the digital twin model without compromising accuracy. The results have indicated that the artificial neural network can achieve a high coefficient of determination ranging from 0.921 to 0.999 on different performance indicators that implies a high goodness of fit. Accordingly, the optimization study based upon a nonsorting genetic algorithm (NSGA-II) has indicated a high ventilation rate of 2.86 1\/h and an efficiency of 37.21%. The insights of this work have reflected that exploring the societal implications of sustainable building technologies such as the double facade solar chimney through educational initiatives can cultivate a new generation of society who not only understand the technical aspects but also appreciate the broader social and environmental contexts of their work, eventually to have future buildings with integrated passive systems.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf015","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T02:27:46Z","timestamp":1738636066000},"page":"16-35","source":"Crossref","is-referenced-by-count":7,"title":["Explainable artificial intelligence twin for metaheuristic optimization: Double-skin facade with energy storage in buildings"],"prefix":"10.1093","volume":"12","author":[{"given":"Rasikh","family":"Tariq","sequence":"first","affiliation":[{"name":"Institute for the Future of Education, Tecnol\u00f3gico de Monterrey , Av. 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