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.
Highlights
Solar radiation is the Earth’s main source of energy and the amount of solar radiation reaching the Earth’s surface is affected by the atmosphere, hydrosphere and biosphere (Budyko, 1969; Islam et al, 2009)
Some scholars have carried out the comparative analysis of a variety of machine learning algorithms (Meenal and Selvakumar, 2018; Pang et al, 2020; Shamshirband et al, 2020), and all these works show that the artificial neural network (ANN) algorithm does not realize good prediction results but provides a direction for algorithm improvement
A CEEMDAN–CNN–LSTM model is proposed by Gao et al (2020) for hourly multi-region solar irradiance forecasting, and the results present that the model can achieve more accurate prediction performance than other models
Summary
Solar radiation is the Earth’s main source of energy and the amount of solar radiation reaching the Earth’s surface is affected by the atmosphere, hydrosphere and biosphere (Budyko, 1969; Islam et al, 2009). Chen et al (2011) used the support vector machine (SVM) method to construct a solar radiation prediction model and showed that the SVM-based algorithm had a differential predictive accuracy when using different kernel functions. Olatomiwa et al (2015) and Shamshirband et al (2016) both optimized the SVM algorithm and achieved good prediction results Tree algorithms, such as the random forest algorithm and the gradient boosting regression tree (GBRT) algorithm, have been used to construct solar radiation prediction models with encouraging results (Sun et al, 2016; Persson et al, 2017; Fan et al, 2018; Zeng et al, 2020). Mishra et al (2020) proposed a short-term solar radiation prediction model using WT-LSTM and achieved good results, showing that deep learning technology has great potential in solar radiation. A CEEMDAN–CNN–LSTM model is proposed by Gao et al (2020) for hourly multi-region solar irradiance forecasting, and the results present that the model can achieve more accurate prediction performance than other models
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