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 Kurian, Rhea; Sharon Roji Priya, C.; Christa, T.S.; Darshana, R.; Mathew, Bettina S. Title A name given to the resource Risk Analysis Using Ensemble Learning Model for Smart Energy Sustainability in Indian Cities 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 Smart Innovation, Systems and Technologies;Volume;435 SIST;pp.395-409 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-2182-8_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2182-8_30</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011352278?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011352278?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 Kurian R., Christ (Deemed to be University), Karnataka, Bangalore, India; Sharon Roji Priya C., Christ (Deemed to be University), Karnataka, Bangalore, India; Christa T.S., Christ (Deemed to be University), Karnataka, Bangalore, India; Darshana R., Christ (Deemed to be University), Karnataka, Bangalore, India; Mathew B.S., Christ (Deemed to be University), Karnataka, Bangalore, India Description An account of the resource Analyzing risks involved in smart energy sustainability entails identifying, assessing, and mitigating diverse types of risks that include financial, operational, and environmental factors that can affect the dependability, efficiency, and ecological friendliness of energy systems in smart cities. The study uses a high-frequency dataset from smart meters in Mathura and Bareilly districts in India collected over 2 years from May 2019 to October 2021 which contains millions of data points. To forecast energy consumption patterns and reveal possible risks we used machine learning models like linear regression, random forest, gradient boosting, and extra tree classifier. By using several machine learning algorithms such as multiple linear regression (MLR), classification trees (CTs), random forests (RFs), and support vector machines (SVMs) this paper developed an empirical model to establish an interrelationship between district heating systems investments influence on the performance improvement variables for sustainable development goals. Notably, the ensemble learning approach had a remarkable precision rate of 94.69% indicating its importance in forecasting and managing demand for power. Moreover, the findings provide insights that could help policymakers and service providers improve urban energy sustainability and efficiency. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Energy consumption; Ensemble learning; Forecasting; Risk analysis; Smart energy; Smart meter data; Sustainability Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981962181-1; 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