Ensemble of Support Vector Methods to Estimate Global Solar Radiation in Algeria

2018 ◽  
pp. 155-163 ◽  
Author(s):  
Nahed Zemouri ◽  
Hassen Bouzgou
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Support Vector

Support vector regression methodology for estimating global solar radiation in Algeria

2018 ◽  
Vol 133 (1) ◽  
Author(s):  
Mawloud Guermoui ◽  
Abdelaziz Rabehi ◽  
Kacem Gairaa ◽  
Said Benkaciali
Keyword(s):  
Solar Radiation ◽  
Support Vector Regression ◽  
Global Solar Radiation ◽  
Support Vector

A support vector machine–firefly algorithm-based model for global solar radiation prediction

Solar Energy ◽  
2015 ◽  
Vol 115 ◽  
pp. 632-644 ◽  
Author(s):  
Lanre Olatomiwa ◽  
Saad Mekhilef ◽  
Shahaboddin Shamshirband ◽  
Kasra Mohammadi ◽  
Dalibor Petković ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Firefly Algorithm ◽  
Global Solar Radiation ◽  
Support Vector

scholarly journals Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

2021 ◽  
Author(s):  
Mehdi jamei ◽  
Iman Ahmadianfar ◽  
Mozhdeh Jamei ◽  
Masoud Karbasi ◽  
Ali Asghar Heidari ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Renewable Energy Sources ◽  
Global Solar Radiation ◽  
Least Square ◽  
Support Vector ◽  
Synoptic Station ◽  
Adaptive Regression ◽  
Precise Assessment ◽  
Statistical Metrics ◽  
Better Than

Abstract Solar energy is one of the most important renewable energy sources. Assessing the solar potential of area needs analyzed information about the dataset of the measured global solar radiation (GSR). Recently, researches detected the high potential of state-of-the-art artificial intelligence (AI) methods in estimating the GSR successfully. In this study, a novel hybrid AI-based tool consisting of least square support vector machine (LSSVM) integrated with improved simulated annealing (ISA) is proposed to predict the GSR over the Ahvaz synoptic station located in the South-West of Iran. The potential of the proposed hybrid paradigm so-called LSSVM-ISA was evaluated by using multivariate adaptive regression spline (MARS), generalization regression neural network (GRNN), and multivariate linear regression with interactions (MLRI). For precise assessment of efficiency of the AI models, various statistical metrics and validation methods were used to assess the precision of the developed models. A comparison of the obtained results indicated that the LSSVM-ISA method performed better than the MARS, GRNN, and MLRI models. The achieved RMSE values of the MARS, GRNN, and MLRI models were decreased by 9%, 16%, and 30% using the LSSVM-ISA model. Finally, the results demonstrated that the LSSVM-ISA model could be successfully employed for accurately predicting GSR.

A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation

2015 ◽  
Vol 92 ◽  
pp. 162-171 ◽  
Author(s):  
Kasra Mohammadi ◽  
Shahaboddin Shamshirband ◽  
Chong Wen Tong ◽  
Muhammad Arif ◽  
Dalibor Petković ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Support Vector

Potential of radial basis function based support vector regression for global solar radiation prediction

2014 ◽  
Vol 39 ◽  
pp. 1005-1011 ◽  
Author(s):  
Zeynab Ramedani ◽  
Mahmoud Omid ◽  
Alireza Keyhani ◽  
Shahaboddin Shamshirband ◽  
Benyamin Khoshnevisan
Keyword(s):  
Solar Radiation ◽  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Global Solar Radiation ◽  
Support Vector ◽  
Radial Basis

Machine Learning Models Based on Random Forest Feature Selection and Bayesian Optimization for Predicting Daily Global Solar Radiation

2021 ◽  
Vol 11 (1) ◽  
pp. 309-323
Author(s):  
Mohamed Chaibi ◽  
El Mahjoub Benghoulam ◽  
Lhoussaine Tarik ◽  
Mohamed Berrada ◽  
Abdellah El Hmaidi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Solar Radiation ◽  
Predictive Accuracy ◽  
Sunshine Duration ◽  
Computational Cost ◽  
Global Solar Radiation ◽  
Bayesian Optimization ◽  
Support Vector

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.

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