Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sharma, Madhvi; Verma, Tanya; Kamal, Arif; Keskar, Yogesh; Upreti, Kamal Title A name given to the resource Towards Net-Zero Hotels: AI-Enabled Energy Transition and Demand Response Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320661" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320661</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033649380?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033649380?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sharma M., Amity School of Architecture and Planning, Amity University, Rajasthan, Jaipur, India; Verma T., Dean School of Architecture and Design, K R Mangalam University, Haryana, Gurugram, India; Kamal A., Architecture Section, Aligarh Muslim University, Aligarh, India; Keskar Y., Amity school of Architecture and Planning, Rajasthan, Jaipur, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India Description An account of the resource This paper presents an AI and machine learning-driven approach to support energy transition and demand-side management in hotel operations, aiming for net-zero energy goals in hot and arid regions. The proposed framework leverages predictive modeling to forecast HVAC energy consumption based on key environmental and operational factors such as temperature, humidity, occupancy, and thermostat settings. Among the tested models, LightGBM demonstrated superior performance in terms of accuracy and computational efficiency. Additionally, a demand response strategy was simulated, where small thermostat adjustments during high-load periods led to significant energy savings without affecting guest comfort. The results highlight the effectiveness of integrating AI-powered decision systems into hospitality energy management, contributing to climate-resilient, sustainable hotel infrastructure. 2025 IEEE. Subject The topic of the resource AI in sustainability; demand response; hotel energy management; HVAC prediction; LightGBM; machine learning; Net-zero energy; smart hospitality systems Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online