Open Access
ARTICLE
Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models
Jianhua Dong1, Lifeng Wu2, Xiaogang Liu1, *, Cheng Fan1, Menghui Leng3, Qiliang Yang1
1 Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming, 650500, China.
2 School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang, 330099, China.
3 Jiangxi Key Laboratory of Hydrology-Water Resources and Water Environment, Nanchang Institute of Technology, Nanchang, 330099, China.
* Corresponding Author: Xiaogang Liu; Email: .
Computer Modeling in Engineering & Sciences 2020, 123(1), 49-73. https://doi.org/ 10.32604/cmes.2020.09014
Received 02 November 2019; Accepted 19 December 2019; Issue published 01 April 2020
Abstract
Solar radiation is an important parameter in the fields of computer modeling,
engineering technology and energy development. This paper evaluated the ability of three
machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Support Vector
Machine (SVM) and Multivariate Adaptive Regression Splines (MARS), to estimate the
daily diffuse solar radiation (
Rd). The regular meteorological data of 1966-2015 at five
stations in China were taken as the input parameters (including mean average temperature
(
Ta), theoretical sunshine duration (
N), actual sunshine duration (
n), daily average air
relative humidity (
RH), and extra-terrestrial solar radiation (
Ra)). And their estimation
accuracies were subjected to comparative analysis. The three models were first trained
using meteorological data from 1966 to 2000. Then, the 2001-2015 data was used to test
the trained machine learning model. The results show that the XGBoost had better
accuracy than the other two models in coefficient of determination (R
2
), root mean square
error (RMSE), mean bias error (MBE) and normalized root mean square error (NRMSE).
The MARS performed better in the training phase than the testing phase, but became less
accurate in the testing phase, with the R
2 value falling by 2.7-16.9% on average. By
contrast, the R
2 values of SVM and XGBoost increased by 2.9-12.2% and 1.9-14.3%,
respectively. Despite trailing slightly behind the SVM at the Beijing station, the XGBoost
showed good performance at the rest of the stations in the two phases. In the training
phase, the accuracy growth is small but observable. In addition, the XGBoost had a
slightly lower RMSE than the SVM, a signal of its edge in stability. Therefore, the three
machine learning models can estimate the daily
Rd based on local inputs and the
XGBoost stands out for its excellent performance and stability.
Keywords
Cite This Article
Dong, J., Wu, L., Liu, X., Fan, C., Leng, M. et al. (2020). Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models.
CMES-Computer Modeling in Engineering & Sciences, 123(1), 49–73.