1Moulay Ismail University, Faculty of Science, Department of Physics, Team of Renewable energy and energy efficiency, BP 11201, Zitoune, Meknes, Morocco
2Moulay Ismail University, Faculty of Science and Technique, Mining, Water and Environmental Engineering Laboratory, BP 509, Boutalamine, Errachidia, Morocco
3Moulay Ismail University, ENSAM, Laboratory of Mathematical and Computational Modeling, Marjane II, BP 15290, Al Mansour, 50000, Meknes, Morocco
4 Moulay Ismail University, Faculty of Science, Department of Geology, Laboratory of Water Sciences and environmental engineering, BP 11201, Zitoune, Meknes, Morocco
BibTex Citation Data :
@article{IJRED41451, author = {Mohamed Chaibi and El Mahjoub Benghoulam and Lhoussaine Tarik and Mohamed Berrada and Abdellah El Hmaidi}, title = {Machine Learning Models Based on Random Forest Feature Selection and Bayesian Optimization for Predicting Daily Global Solar Radiation}, journal = {International Journal of Renewable Energy Development}, volume = {11}, number = {1}, year = {2022}, keywords = {Feature selection; Mean Decrease in Accuracy; Mean Decrease in Impurity; Bayesian optimization; Solar radiation}, abstract = {Prediction of daily global solar radiation with simple and highly accurate models would be beneficial for solar energy conversion systems. In this paper, we proposed a hybrid machine learning methodology integrating two feature selection methods and a Bayesian optimization algorithm to predict H in the city of Fez, Morocco. First, we identified the most significant predictors using two Random Forest methods of feature importance: Mean Decrease in Impurity (MDI) and Mean Decrease in Accuracy (MDA). Then, based on the feature selection results, ten models were developed and compared: (1) five standalone machine learning (ML) models including Classification and Regression Trees (CART), Random Forests (RF), Bagged Trees Regression (BTR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP); and (2) the same models tuned by the Bayesian optimization (BO) algorithm: CART-BO, RF-BO, BTR-BO, SVR-BO, and MLP-BO. Both MDI and MDA techniques revealed that extraterrestrial solar radiation and sunshine duration fraction were the most influential features. The BO approach improved the predictive accuracy of MLP, CART, SVR, and BTR models and prevented the CART model from overfitting. The best improvements were obtained using the MLP model, where RMSE and MAE were reduced by 17.6% and 17.2%, respectively. Among the studied models, the SVR-BO algorithm provided the best trade-off between prediction accuracy (RMSE=0.4473kWh/m²/day, MAE=0.3381kWh/m²/day, and R²=0.9465), stability (with a 0.0033kWh/m²/day increase in RMSE), and computational cost.}, pages = {309--323} doi = {10.14710/ijred.2022.41451}, url = {https://ejournal.undip.ac.id/index.php/ijred/article/view/41451} }
Refworks Citation Data :
Article Metrics:
Last update:
Multi-attribute optimization of sustainable aviation fuel production-process from microalgae source
Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions
Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems
Mathematical modeling of a Hybrid Mutated Tunicate Swarm Algorithm for Feature Selection and Global Optimization
Quantifying urban three-dimensional building form effects on land surface temperature: a case study of Beijing, China
Optimized Random Forest for Solar Radiation Prediction Using Sunshine Hours
Use of machine learning to identify key factors regulating volatilization of semi-volatile organic chemicals from soil to air
Hybrid Feature Selection and Ensemble Classifier with Optimization for Sentiment Classification
Metaheuristic Algorithms for Solar Radiation Prediction: A Systematic Analysis
Approximation of daily solar radiation: A comprehensive review on employing of regression models
Novel CNN-based transformer integrating Boruta algorithm for production prediction modeling and energy saving of industrial processes
RETRACTED: Multi-attribute optimization of sustainable aviation fuel production-process from microalgae source
Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine
Assessing the performance of a monocrystalline solar panel under different tropical climatic conditions in Cameroon using artificial neural network
Hyper‐parametric improved machine learning models for solar radiation forecasting
Last update: 2024-11-21 03:35:36
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. Articles are freely available to both subscribers and the wider public with permitted reuse.
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options: Creative Commons Attribution-ShareAlike (CC BY-SA). Authors and readers can copy and redistribute the material in any medium or format, as well as remix, transform, and build upon the material for any purpose, even commercially, but they must give appropriate credit (cite to the article or content), provide a link to the license, and indicate if changes were made. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
International Journal of Renewable Energy Development (ISSN:2252-4940) published by CBIORE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.