Delaunay 삼각분할을 활용한 태양광 설비의 기대 발전량 예측 모델 개발 = Solar Expected Power Generation Prediction Model Using Delaunay Triangulation

다국어 초록 (Multilingual Abstract)

Amid growing concerns about climate change and global energy security, renewable energy uti- lization is increasingly recognized as a crucial solution. This study aims to enhance the accu- racy of solar power generation forecasting by using spatial data refinement techniques based on Delaunay Triangulation. This approach integrates climate data from geographically distributed observation stations to create enhanced datasets for predictive modeling. By using Grid- SearchCV and Gradient Boosting Regression, model hyperparameters were optimized to achieve reliable forecasting results. Two predictive models were compared: Model_1 combined data from three climate observation stations within each triangulated area, using a weighted approach for input generation, while Model_2 used data from the closest single observation station for prediction. The results demonstrated that the triangulated, multi-station approach (Model_1) pro- vided more accurate forecasts than the single-station approach (Model_2), underscoring the effectiveness of Delaunay Triangulation for solar power prediction. These findings offer valu- able insights for solar power plant site selection and operational planning, potentially boosting energy efficiency and economic returns in solar power projects.