The transferability of random forest and support vector machine for estimating daily global solar radiation using sunshine duration over different climate zones

2021 ◽  
Author(s):  
Wei Wu ◽  
Mao-Fen Li ◽  
Xia Xu ◽  
Xiao-Ping Tang ◽  
Chao Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Solar Radiation ◽  
Sunshine Duration ◽  
Global Solar Radiation ◽  
Support Vector ◽  
Climate Zones

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.

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

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

A support vector machine approach to estimate global solar radiation with the influence of fog and haze

2018 ◽  
Vol 128 ◽  
pp. 155-162 ◽  
Author(s):  
Wanxiang Yao ◽  
Chunxiao Zhang ◽  
Haodong Hao ◽  
Xiao Wang ◽  
Xianli Li
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Support Vector

scholarly journals Global Solar Radiation Forecasting using Artificial Neural Network and Support Vector Machine

2021 ◽  
Vol 2129 (1) ◽  
pp. 012079
Author(s):  
Emmanuel Philibus ◽  
Roselina Sallehuddin ◽  
Yusliza Yussof ◽  
Lizawati Mi Yusuf
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Solar Radiation ◽  
Land Surface ◽  
Statistical Tests ◽  
Global Solar Radiation ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Global solar radiation (GSoR) forecasting involves predicting future energy from the sun based on past and present data. Literature reveals that not all meteorological stations record solar radiation, some equipments are faulty, and are not available in every location due to high cost. Hence, the need to predict and forecast using predictors such as land surface temperature (LST). Satellite data when were used to complement ground-based stations have been yielding good results. Different artificial intelligence (AI) methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) present different forecasting performances. Motivated by existing literature-related contradictions on the performance superiority of ANN and SVM in GSoR forecasting, the two techniques were compared based on several statistical tests. Experimental results show that ANN outperformed SVM by 2.9864% accuracy, making it superior in the forecast of GSoR.

scholarly journals An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7367
Author(s):  
Mohamed Chaibi ◽  
EL Mahjoub Benghoulam ◽  
Lhoussaine Tarik ◽  
Mohamed Berrada ◽  
Abdellah El Hmaidi
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Predictive Accuracy ◽  
Sunshine Duration ◽  
Predictive Performance ◽  
Global Solar Radiation ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Testing Stage

Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. However, the predictions of these models are difficult to explain and trust. This paper aims to demonstrate the utility of two interpretation techniques to explain and improve the predictions of ML models. We compared first the predictive performance of Light Gradient Boosting (LightGBM) with three benchmark models, including multilayer perceptron (MLP), multiple linear regression (MLR), and support-vector regression (SVR), for estimating the global solar radiation (H) in the city of Fez, Morocco. Then, the predictions of the most accurate model were explained by two model-agnostic explanation techniques: permutation feature importance (PFI) and Shapley additive explanations (SHAP). The results indicated that LightGBM (R2 = 0.9377, RMSE = 0.4827 kWh/m2, MAE = 0.3614 kWh/m2) provides similar predictive accuracy as SVR, and outperformed MLP and MLR in the testing stage. Both PFI and SHAP methods showed that extraterrestrial solar radiation (H0) and sunshine duration fraction (SF) are the two most important parameters that affect H estimation. Moreover, the SHAP method established how each feature influences the LightGBM estimations. The predictive accuracy of the LightGBM model was further improved slightly after re-examination of features, where the model combining H0, SF, and RH was better than the model with all features.

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