| Title |
Improvement of Solar Power Forecasting Using Interpretation of Artificial Intelligence |
| Authors |
오재영(Jae-Young Oh) ; 이용건(Yong-Geon Lee) ; 김기백(Gibak Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.7.1111 |
| Keywords |
Solar power forecasting; Explainable artificial intelligence; Feature importance |
| Abstract |
Artificial intelligence (AI) has been effectively applied to various industries thanks to the increased availability of data and computing power. Advanced machine learning techniques also contribute to the widespread application of AI. However, it is becoming more difficult to interpret the AI implemented by advanced and highly complex machine learning algorithm. In this paper, for solar power forecasting system, we conduct SHAP value analysis which is one of the explainable AI techniques. We aim to improve the performance of the solar power forecasting by employing feature selection which is based on the feature importance computed by SHAP values. In the experimental results, three different machine learning algorithms (SVM, ANN, XGBoost) are applied for solar power forecasting and shown to improve the forecasting performance in all three methods. |