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 Article Faculty Publications -Articles 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 Deepthi, Raavi; Obulesu, O.; M, Mr.Mahendra; Seggem, Ramanjaneyulu; Reddy, T.Raghunadha; Singh, Monisha Title A name given to the resource AI-driven load forecasting and energy management in smart grids using hybrid deep models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Electric Power Systems Research;Volume;258;Issue;;Article No.;113103; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.epsr.2026.113103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.epsr.2026.113103</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036653879?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036653879?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 Deepthi R., Department of Information Technology, Sreenidhi institute of science and technology, Telangana, Hyderabad, 501301, India; Obulesu O., Department of Computer Science and Engineering (Data Science), G. Narayanamma Institute of Technology &amp; Science (For Women), Telangana, Hyderabad, 500104, India; M M.M., Department of Computer Science and Engineering (Data Science), G.Narayanamma Institute of Technology &amp; Science (For Women), Telangana, Hyderabad, 500104, India; Seggem R., Department of Computer Science Engineering, Geethanjali College of Engineering and Technology, Telangana, Hyderabad, 501303, India; Reddy T.R., Department of Computer Science and Engineering, Matrusri Engineering College, Telangana, Hyderabad, 500059, India; Singh M., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, 560029, India Description An account of the resource Modern power systems are becoming more complex, and integrating renewable energy sources (RES) calls for sophisticated solutions for accurate load forecasting and efficient energy management. To improve forecast accuracy and operational efficiency in smart grids, the research suggests a hybrid deep learning (DL) structure that blends convolutional neural networks (CNN) with long short-term memory (LSTM) systems. The LSTM element records sequential connections within historical energy usage, while the CNN element extracts geographical features from environmental variables such as temperature, humidity, and solar radiation. A comprehensive preprocessing pipeline comprising data cleaning, normalization, and feature selection ensures high-quality inputs for model training. The proposed LSTM-bCNN model is evaluated using a publicly available dataset, and its performance is benchmarked against traditional and contemporary models including ARIMA, SVM, RF, and standalone LSTM. According to findings from experiments, the mixture model obtains the highest R-squared (R) value, the lowest Mean Absolute Error (MAE), and the Root Mean Squared Error (RMSE), confirming its robustness in capturing complex patterns in energy consumption. This research highlights the possible of hybrid DL models in enabling intelligent, adaptive, and resilient energy management systems (EMS) within next-generation smart grids. 2026 Elsevier B.V. Subject The topic of the resource And energy management; Convolutional neural networks; Load forecasting; Long short-term memory; Spatial characteristics Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 3787796; CODEN: EPSRD Language A language of the resource English Type The nature or genre of the resource Article 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