Abstract
Solar energy is one of the most widely used renewable energy sources in the world and its development and utilization are being integrated into people’s lives. Therefore, accurate solar radiation data are of great significance for site-selection of photovoltaic (PV) power generation, design of solar furnaces and energy-efficient buildings. Practically, it is challenging to get accurate solar radiation data because of scarce and uneven distribution of ground-based observation sites throughout the country. Many artificial neural network (ANN) estimation models are therefore developed to estimate solar radiation, but the existing ANN models are mostly based on conventional meteorological data; clouds, aerosols, and water vapor are rarely considered because of a lack of instrumental observations at the conventional meteorological stations. Based on clouds, aerosols, and precipitable water-vapor data from Moderate Resolution Imaging Spectroradiometer (MODIS), along with conventional meteorological data, back-propagation (BP) neural network method was developed in this work with Levenberg-Marquardt (LM) algorithm (referred to as LM-BP) to simulate monthly-mean daily global solar radiation (M-GSR). Comparisons were carried out among three M-GSR estimates, including the one presented in this study, the multiple linear regression (MLR) model, and remotely-sensed radiation products by Cloud and the Earth’s radiation energy system (CERES). The validation results indicate that the accuracy of the ANN model is better than that of the MLR model and CERES radiation products, with a root mean squared error (RMSE) of 1.34 MJ·m−2 (ANN), 2.46 MJ·m−2 (MLR), 2.11 MJ·m−2 (CERES), respectively. Finally, according to the established ANN-based method, the M-GSR of 36 conventional meteorological stations for 12 months was estimated in 2012 in the study area. Solar radiation data based on the LM-BP method of this study can provide some reference for the utilization of solar and heat energy.
Highlights
Due to the boom in population, urbanization, and industrialization, the energy demand has increased enormously, which causes rapid depletion of fossil fuel resources and pollution of the environment [1]
The results indicated that the multilayer perceptron (MLP) and radial basis neural network (RBNN) models are generally more accurate than the generalized regression neural network (GRNN), with the root mean squared error (RMSE) values of 2–3.29 MJ·m−2 ·day−1 for GRNN, 1.94–3.27 MJ·m−2 ·day−1 for MLP, and 1.96–3.25 MJ·m−2 ·day−1 for RBNN models
The artificial neural network (ANN)-based solar radiation model needed a comparison with estimation
Summary
Due to the boom in population, urbanization, and industrialization, the energy demand has increased enormously, which causes rapid depletion of fossil fuel resources and pollution of the environment [1]. Solar energy is a cleaner and more renewable energy resource, and its development and utilization can greatly alleviate the negative environmental effects caused by the harmful substances released by the combustion of fossil fuels, such as air pollution, acid rain, the greenhouse effect, ecological balance, destruction and so on. Energies 2018, 11, 3510 and the basic driving force on Earth, providing necessary energy for the water cycle, atmospheric motions, and biological activities [2]. It is one of the necessary input parameters in the crop evapotranspiration model, ecosystem carbon and nitrogen cycle, hydrology, and climate-change models [3]. Lack of solar radiation data limits research works in relevant fields; it is important to perform the simulation of solar radiation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
