| Abstract: |
The integration of machine learning methods in smart grid systems has revolutionized predictive energy control and load forecasting capabilities. This research examines advanced machine learning techniques including Long Short-Term Memory networks, Support Vector Machines, Random Forest, and ensemble methods for accurate load prediction and energy optimization. A comprehensive analysis was conducted using datasets from multiple smart grid implementations, demonstrating significant improvements in forecasting accuracy. LSTM models achieved Mean Absolute Percentage Error of 1.5% for hourly predictions, while hybrid CNN-LSTM approaches demonstrated superior performance with Root Mean Squared Error values of 1.415 for solar photovoltaic systems. Support Vector Regression outperformed traditional linear models with Mean Squared Error of 2.002 for renewable energy forecasting. Random Forest algorithms exhibited prediction accuracy of 98.05% in electricity consumption forecasting. The study reveals that machine learning-driven predictive control reduces operational costs, enhances grid reliability, and facilitates efficient renewable energy integration, contributing significantly to sustainable smart grid development and energy management optimization. |
