AI-Driven Power Forecasting for Renewable Energy: A Multi-Terrain Analysis from Shandong Province Wind Farms

Shandong Province, a critical hub for renewable energy in China, presents a diverse set of challenges and opportunities in wind power development. The region's wind farms span inland plains, coastal plains, and hilly terrains, with installed capacities ranging from 28,400 kW to 800,000 kW. While these diverse landscapes offer significant potential for wind power, several challenges persist, including grid integration issues, regulatory inconsistencies, and the need for advanced technologies to enhance energy efficiency. Additionally, social acceptance concerns related to environmental impacts further complicate the development of renewable energy projects. This study leverages wind and power data from multiple wind farms in Shandong Province to develop machine learning-based power forecasting models. Specifically, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks are employed to address spatiotemporal variability in wind power generation across diverse terrains. Results highlight the influence of geographic and meteorological factors on forecasting accuracy and underscore the potential of AI-driven approaches to mitigate uncertainties associated with wind power integration into the grid. Our findings demonstrate that terrain-specific modeling, coupled with advanced forecasting techniques, can significantly improve the reliability of wind power generation in complex environments. By addressing key challenges unique to Shandong Province, this research contributes valuable insights into sustainable energy planning and the broader integration of renewable energy into China's power grid.