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 Kumar Chandar, S. Title A name given to the resource Electricity Demand Prediction: An Analytical Comparison of ARIMA and Artificial Neural Network 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 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings;pp.1272-1279 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICMCSI64620.2025.10883105" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMCSI64620.2025.10883105</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000019355?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000019355?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 Kumar Chandar S., School of Business and Management, CHRIST University, Karnataka, India Description An account of the resource Electricity plays a dominant role globally, especially in the economies of India. Accurately projecting its consumption is crucial for energy planning. This study focuses on forecasting electricity consumption across distinct sectors using Autoregressive Integrate Moving Average (ARIMA) and Artificial Neural Network (ANN). The efficacy of the models is evaluated via various error metrics and compared, demonstrating the superior performance of the ANN model over ARIMA model. 2025 IEEE. Subject The topic of the resource ARIMA; Artificial neural network; Energy Consumption Prediction; India Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152266-7; 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