Embedding Climate Dynamics and Prediction with Deep Learning for Wind Power Forecasting: Short-Term to Long-Term Perspective

Wind power generation plays an increasingly significant role in the global shift towards renewable energy sources for climate mitigation. However, its susceptibility to climate variability underscores the critical importance of accurate energy generation prediction for ensuring a stable energy supply. In this paper, we analyze the correlation between climate data and energy generation data, extracting essential factors based on this analysis. We propose a Convolutional Neural Network-based model capable of four-hour short-term forecasting by representing these factors as embedding matrices. Furthermore, we combine this model with a Long Short Term Memory model to extend the forecasting period to 24 hours, validating its performance in the day-ahead market bidding context. Using empirical data from South Korea, our short-term forecasting model achieved an accuracy of 76%, while the long-term model demonstrated 85% accuracy, highlighting its potential for practical applications in wind energy generation and market operations.