Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

Abstract

Solar radiation is the Earth’s primary source of energy and has an important role in the surface radiation balance, hydrological cycles, vegetation photosynthesis, and weather and climate extremes. The accurate prediction of solar radiation is therefore very important in both the solar industry and climate research. We constructed 12 machine learning models to predict and compare daily and monthly values of solar radiation and a stacking model using the best of these algorithms were developed to predict solar radiation. The results show that meteorological factors (such as sunshine duration, land surface temperature, and visibility) are crucial in the machine learning models. Trend analysis between extreme land surface temperatures and the amount of solar radiation showed the importance of solar radiation in compound extreme climate events. The gradient boosting regression tree (GBRT), extreme gradient lifting (XGBoost), Gaussian process regression (GPR), and random forest models performed better (poor) prediction capabilities of daily and monthly solar radiation. The stacking model, which included the GBRT, XGBoost, GPR, and random forest models, performed better than the single models in the prediction of daily solar radiation but showed no advantage over the XGBoost model in the prediction of the monthly solar radiation. We conclude that the stacking model and the XGBoost model are the best models to predict solar radiation.

Improving the Innovative Performance of Renewable Energy Enterprises in China: Effects of Subsidy Policy and Intellectual Property Legislation

“…Shahab et al, (2021) discussed the application of different types of deep learning algorithms in the field of solar and wind energy resources and evaluated their performance with a new taxonomy [59]. Huang et al, (2021) considered the superposition and XGBoost models and concluded that they are the best models for predicting solar radiation [60]. These are the latest technologies in the renewable energy industry.…”

Investigating Predictability of the TRHR Seasonal Precipitation at Long Lead Times Using a Generalized Regression Model with Regularization

“…In this section, the binary precipitation is predicted using the multinomial regression version (i.e., the elastic net logistic regression) of our model. While new machine learning techniques like the classification and regression tree (CART) can have better model skill in multi-class prediction of climate variables (Choubin et al, 2018;Huang et al, 2021), the elastic net logistic regression is tested to demonstrate flexibility and consistency with altered deviance functions. The multinomial regression may have more use in practical application since amplitudes of the predictand tend to be underestimated because of the regularization (Peng et al, 2020).…”

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

“…That function can be used in a different dataset, named testing one, to evaluate the model, and if the results are satisfactory, it can be used in the classification or regression of any kind of application needed. In that group we find methods, such as Decision Trees, e.g., Random Forest (RF) [5] or XGBoost (XGB) [6], Artificial Neural Networks (ANN) [7], Deep Learning (DL) [8], and Support Vector Machine (SVM) [9]. The second group in machine learning is unsupervised learning (Figure 1), in which algorithms do not have labelled data to train from, and must decide upon other ways to divide a given dataset, or reduce the dimensions of it, for further analysis.…”