Jinman Jeon  Jonghwan Choi

10.5626/JOK.2025.52.8.688

solar power generation forecasting multi-regional weather data Machine Learning Data Mining LSTM genetic algorithm

The intermittency of solar power generation is one of the factors that make stable power supply challenging. To overcome the intermittency issue caused by weather conditions, various solar power generation forecasting methods have been developed using statistical modeling techniques and deep learning. However, existing methods have limitations in improving prediction accuracy as they fail to account for the correlations and spatial dynamics inherent in meteorological data. In this study, we proposed a solar power generation forecasting method that enhances prediction accuracy by leveraging a long short-term memory (LSTM) model to extract temporal patterns from meteorological data across multiple cities and optimizing the importance of each city's weather patterns using a genetic algorithm. We evaluated the performance of the proposed method in three major solar power generation regions in South Korea and confirmed that it outperformed existing methods while effectively identifying key regions closely related to solar power generation.