A Novel Approach to Power Optimization Using Machine-Learning Techniques

The optimization of power systems has become increasingly important with the rise in global energy demand and the integration of renewable energy sources like solar and wind. Traditional power optimization methods often rely on static models, which struggle to handle the variability and unpredictability associated with renewable energy. In response, machine learning (ML) techniques provide a dynamic, data-driven approach that adapts to real-time conditions. This paper presents a novel power optimization approach that leverages a combination of regression models, reinforcement learning (RL), and deep learning (DL) to forecast energy demand, optimize power distribution, and improve grid efficiency. Regression models are used for short-term demand forecasting by analyzing historical consumption patterns and environmental factors, while RL models enable real-time decision-making to manage energy flow, reduce losses, and balance supply and demand. Deep learning techniques are employed to identify long-term patterns in energy consumption and generation, facilitating accurate long-term forecasts. The proposed methodology is validated through experiments conducted on real-world data, demonstrating its superior performance compared to conventional optimization methods. Results show that the ML-based approach significantly reduces energy waste, improves forecasting accuracy, and enhances overall system efficiency. This paper contributes to the field by showcasing how advanced ML techniques can optimize power systems, improve grid reliability, and promote sustainability in energy distribution.