Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration

2013 ◽  
Vol 75 ◽  
pp. 311-318 ◽  
Author(s):  
Ji-Long Chen ◽  
Guo-Sheng Li ◽  
Sheng-Jun Wu
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Sunshine Duration ◽  
Support Vector

A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Mawloud Guermoui ◽  
Kacem Gairaa ◽  
John Boland ◽  
Toufik Arrif
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Least Squares ◽  
Correlation Coefficient ◽  
Cuckoo Search ◽  
Measured Data ◽  
Parameters Optimization ◽  
Support Vector ◽  
Bee Colony ◽  
Svm Model

Abstract This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.

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

Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model

2016 ◽  
Vol 56 ◽  
pp. 428-435 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Kasra Mohammadi ◽  
Hossein Khorasanizadeh ◽  
Por Lip Yee ◽  
Malrey Lee ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Solar Radiation ◽  
Support Vector ◽  
Diffuse Solar Radiation

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

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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