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 Avin, Lydia Mary; Vinodha, D.; Jenefa, J.; Sambandam, Rakoth Kandan; Vetriveeran, Divya; Swamy, C. Manjunatha Title A name given to the resource Enhancing Reliability and Accuracy in Wind Energy Forecast Using CNN-LSTM Hybrid Model 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;114 SIST;pp.41-52 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4718-7_4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4718-7_4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105014492026?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105014492026?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 Avin L.M., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Vinodha D., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Jenefa J., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Sambandam R.K., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Vetriveeran D., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Swamy C.M., Christ (Deemed to Be University), Karnataka, Bengaluru, India Description An account of the resource The global shift to sustainable energy is increasing the demand for wind energy. Accurate forecasting becomes crucial for renewable energy systems to function effectively in terms of resource allocation, grid management, and overall reliability. The need for wind energy is growing as a result of the worlds transition to sustainable energy. For renewable energy systems to operate efficiently in terms of resource allocation, grid management, and overall reliability, accurate forecasting becomes essential. It is challenging for current forecasting methods to correctly predict the dynamic nature of wind energy demand. For utilities and grid managers, the inherent variability and unpredictability in wind energy generation pose serious issues. The forecasting models that are now in use are challenged by the variable and sporadic character of wind energy generation. This makes it more difficult to integrate wind energy into the electrical grid effectively and increases the risk of grid instability and inefficient resource utilization. This research addresses these challenges by proposing a hybrid forecasting model that integrates the strengths of Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). By capturing both spatial and temporal dependencies in wind data, the hybrid model aims to enhance accuracy and reliability in wind energy forecasts. The precise forecasting of wind energy is made more difficult by shifting weather patterns, changing environmental factors, and shifting patterns of energy usage. Improving the forecasting models accuracy and dependability in the renewable energy industry requires addressing these difficulties. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Forecasting; Hybrid models; Wind energy Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981964717-0; 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