Comparative Studies of Wind Power and Load Demand Forecasting Using Random Forest and LSTM Models

In modern power systems, both wind power generation and load demand are critical yet highly variable components that must be accurately forecasted to ensure grid stability and energy efficiency. While wind power forecasting addresses the supply-side uncertainty due to weather-driven fluctuations, load demand forecasting targets consumption patterns influenced by human behavior and usage trends. Despite differing in nature, both share a time-dependent structure, making them well-suited for machine learning models like Random Forest and LSTM that can uncover hidden patterns and temporal relationships. By integrating these ML techniques, power system operators can make more informed, real-time decisions, reduce reliance on reserves, and enhance energy dispatch planning. Thus, combining wind and load forecasting through intelligent models improves predictive accuracy and plays a pivotal role in optimizing the operation, reliability, and sustainability of smart grids.