Solar Radiation Forecasting Based on the Hybrid CNN-CatBoost Model
Hyojeoung Kim, Sujin Park, Hee-Jun Park, Heung-Gu Son, Sahm Kim
Abstract
The renewable energy industry is rapidly expanding due to environmental pollution from fossil fuels and continued price hikes. In particular, the solar energy sector accounts for about 48.7% of renewable energy, at the highest production ratio. Therefore, climate prediction is essential because solar power is affected by weather and climate change. However, solar radiation, which is most closely related to solar power, is not currently predicted by the Korea Meteorological Administration; therefore, solar radiation prediction technology is needed. In this study, we predict solar radiation using extra-atmospheric solar radiation and three weather variables: temperature, relative humidity, and total cloud volume. We compared the performance of single models of machine and deep learning in previous work. For the single-model comparison, we used boosting techniques, such as extreme gradient boosting and categorical boosting (CatBoost) in machine learning, and the recurrent neural network (RNN) family (long short-term memory and gated recurrent units). In this paper, we compare CatBoost (previously the best model) with CNN and present a CNN-CatBoost hybrid model prediction method that combines CatBoost in machine learning and CNN in deep learning for the best predictive performance for a single-model comparison. In addition, we checked the accuracy change when adding wind speed and precipitation to the hybrid model. The model that considers wind speed and precipitation improved at all but three (Gangneung, Suwon, and Cheongju) of the 18 locations.