| Abstract: |
The rapid integration of renewable energy sources and the increasing complexity of modern power systems necessitate advanced intelligent energy management solutions. This research investigates machine learning-driven approaches for intelligent energy management and demand prediction in smart grids, with specific focus on the Indian energy sector context. The study employs a comprehensive analysis of deep learning models including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Regression (SVR), and hybrid architectures for accurate load forecasting and demand prediction. The hypothesis posits that hybrid machine learning models demonstrate superior performance compared to traditional forecasting methods in smart grid applications. Results indicate that hybrid CNN-LSTM models achieve prediction accuracy of 93.38% with RMSE values ranging from 0.56 to 3.99% MAPE across different datasets. The study analyzes data from Indian smart grid implementations showing electricity demand growth of 6.3% annually, with renewable capacity projected to reach 500 GW by 2030. Findings demonstrate that ML-driven energy management systems reduce energy wastage by 12.96% while improving grid stability to 96.25%, thereby validating the hypothesis that advanced machine learning techniques significantly enhance smart grid operational efficiency and sustainability. |
