| Title |
Enhancing Accuracy of Solar Power Forecasting by Input data Preprocessing and Competitive Model Selection Methods |
| Authors |
박세준(Se-Jun Park) ; 최원석(Won-Seok Choi) ; 이두희(Duehee Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.9.1201 |
| Keywords |
solar power forecasting; preprocessing; weighted average; data interpolation; extreme gradient boost |
| Abstract |
This paper compares various prediction models and preprocessing methods based on data from the Kaggle competition "AMS 2013-2014 Solar Energy Prediction Contest". Four predictive models are used: Linear Regression (LR), Random Forest (RF), Gradient Boost Machine (GBM), and Extreme Gradient Boost (XGBOST). The forecasting accuracy of these four prediction models was compared by changing the preprocessing methods. There are four preprocessing methods proposed in this paper. First, training data is designed by averaging closest four points using the weighted average. Furthermore, training data is designed by averaging points within a circle using the weighted average. Second, various prediction intervals are tested. Third, we propose a data selection method by analyzing the correlation of each parameter. Fourth, the data interpolation is tested. Forecasting accuracy is measured by the mean absolute error |