Fatmanur Ateş; Bekir Aksoy; Ramazan Şenol; İbrahim Üçgül; Arif Koyun · 10.1520/jte20230011 https://doi.org/10.1520/jte20230011
Journal of Testing and Evaluation
Determination of Electric Energy Production from Solar Chimney with Tree-Based Machine Learning Methods and Interpretation with SHAP Method Fatmanur Ates; Bekir Aksoy; Ramazan Senol; Ibrahim Ucgul; Arif Koyun Tree-based machine learning models (AdaBoost, gradient boosting, random forest, XGBoost and bagging) are trained on meteorological data from a solar chimney in Isparta to predict chimney inlet temperature and airflow rate, and thus power output and site suitability. XGBoost performed best, with MAPE values of about 0.008 for temperature and 0.047 for flow rate, and SHAP analysis was used to explain its predictions.
Machine learning / AI
Solar chimney power plant
2023
Journal of Testing and Evaluation
no
2023-08-22 12:00:00 00002209
determination of electric energy production from solar chimney with tree-based machine learning methods and interpretation with shap method
2023
Journal of Testing and Evaluation
Fatmanur Ateş; Bekir Aksoy; Ramazan Şenol; İbrahim Üçgül; Arif Koyun
10.1520/jte20230011
Summary
Tree-based machine learning models (AdaBoost, gradient boosting, random forest, XGBoost and bagging) are trained on meteorological data from a solar chimney in Isparta to predict chimney inlet temperature and airflow rate, and thus power output and site suitability. XGBoost performed best, with MAPE values of about 0.008 for temperature and 0.047 for flow rate, and SHAP analysis was used to explain its predictions.
Description written by Solar Tower. The original abstract is available from the publisher via the link below.
Open source