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 Book Chapter Faculty Publications- Book Chapter 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 Murugesan, G.; Radha, B.; Nithya, A.S.; Gunavathi, R. Title A name given to the resource Power Consumption Forecasting with AI and IOT 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 Power Systems;Volume;Part F914;pp.57-78 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-89459-6_4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-89459-6_4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018052264?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018052264?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 Murugesan G., Department of Artificial Intelligence and Machine Learning, Sree Saraswathi Thyagaraja College, Tamil Nadu, Pollachi, India; Radha B., Department of Computer Technology and Data Science, Sri Krishna Arts and Science College, Tamil Nadu, Coimbatore, India; Nithya A.S., Department of Artificial Intelligence and Machine Learning, Sree Saraswathi Thyagaraja College, Tamil Nadu, Pollachi, India; Gunavathi R., Data Science Department, Christ (Deemed to be University), Maharashtra, Pune, India Description An account of the resource Electricity plays a fundamental and indispensable role in modern society, driving progress, development, and the overall quality of life. Electricity is profoundly ingrained in daily life. It powers homes, providing lighting, heating, cooling, and appliances that support, comfort, and convenience. From cooking meals to powering electronic devices and entertainment systems, electricity is vital for modern living, enhancing our quality of life and enabling various activities. Power forecasting is critical to the effective management and optimization of power generation, consumption, and distribution. Power consumption forecasting has evolved significantly with the introduction of advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). AI techniques, such as machine learning and deep learning, make use of the massive amounts of data produced by IoT devices like smart meters and energy monitoring devices. These devices continuously gather real-time data on power consumption, weather conditions, grid performance, and other relevant factors. AI algorithms can find patterns and correlations and provide accurate forecasts and important insights for power forecasting by processing and analyzing data. Machine learning algorithms, such as regression models, neural networks, and ensemble approaches, are trained using historical power consumption data and the features that have been chosen. The models discover the underlying patterns and correlations between input features and power consumption. These forecasts can be used for short-term load balancing, energy procurement planning, demand response management, and optimizing energy distribution. AI and IoT power usage projections give valuable data for decision-making and energy optimization techniques. These projections can be used by energy suppliers, grid operators, building managers, and consumers to plan energy usage, distribute resources efficiently, optimize demand response programs, and discover possibilities for energy saving. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource Data fusion; Distributed energy resources; Grid resilience; Load forecasting; Machine learning algorithms; Optimization algorithms; Renewable energy integration; Time series forecasting Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 16121287; Language A language of the resource English Type The nature or genre of the resource Book chapter 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