ItemArticle

AI-driven load forecasting and energy management in smart grids using hybrid deep models

Title
AI-driven load forecasting and energy management in smart grids using hybrid deep models
Creator
Deepthi, Raavi; Obulesu, O.; M, Mr.Mahendra; Seggem, Ramanjaneyulu; Reddy, T.Raghunadha; Singh, Monisha
Description
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.
Source
Electric Power Systems Research;Volume;258;Issue;;Article No.;113103;
Date
01-01-2026
Publisher
Elsevier Ltd
Subject
And energy management; Convolutional neural networks; Load forecasting; Long short-term memory; Spatial characteristics
Coverage
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 & Science (For Women), Telangana, Hyderabad, 500104, India; M M.M., Department of Computer Science and Engineering (Data Science), G.Narayanamma Institute of Technology & 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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 3787796; CODEN: EPSRD
Format
online
Language
English
Type
Article

Citation

Deepthi, Raavi; Obulesu, O.; M, Mr.Mahendra; Seggem, Ramanjaneyulu; Reddy, T.Raghunadha; Singh, Monisha, “AI-driven load forecasting and energy management in smart grids using hybrid deep models,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 21, 2026, https://archives.christuniversity.in/items/show/22245.

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