Spatial Assessment of Solar Radiation by Machine Learning and Deep Neural Network Models Using Data Provided by the COMS MI Geostationary Satellite: A Case Study in South Korea

Abstract

Although data-driven methods including deep neural network (DNN) were introduced, there was not enough assessment about spatial characteristics when using limited ground observation as reference. This work aimed to interpret the feasibility of several machine learning approaches to assess the spatial distribution of solar radiation on Earth based on the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) geostationary satellite. Four data-driven models were selected (artificial neural network (ANN), random forest (RF), support vector regression (SVR), and DNN), to compare their accuracy and spatial estimating performance. Moreover, we used a physical model to probe the ability of data-driven methods, implementing hold-out and k-fold cross-validation approaches based on pyranometers located in South Korea. The results of analysis showed the RF had the highest accuracy in predicting performance, although the difference between RF and the second-best technique (DNN) was insignificant. Temporal variations in root mean square error (RMSE) were dependent on the number of data samples, while the physical model showed relatively less sensitivity. Nevertheless, DNN and RF showed less variability in RMSE than the others. To examine spatial estimation performance, we mapped solar radiation over South Korea for each model. The data-driven models accurately simulated the observed cloud pattern spatially, whereas the physical model failed to do because of cloud mask errors. These exhibited different spatial retrieval performances according to their own training approaches. Overall analysis showed that deeper layers of networks approaches (RF and DNN), could best simulate the challenging spatial pattern of thin clouds when using satellite multispectral data.

Feasibility Assessments Using Unmanned Aerial Vehicle Technology in Heritage Buildings: Rehabilitation-Restoration, Spatial Analysis and Tourism Potential Analysis

“…On the other hand, specialized studies have shown that the proximity towards a forested surface reduces the amount of radiation. This fact is important for people exposed to heat islands specific to the highdensity areas of buildings, also having the role of mitigating the thermal stress in hot summer conditions [34,35]. The integrated analysis of the three analyzed databases clearly illustrate the anthropic intervention regarding the modeling of the relief for the realization of the residential infrastructure by embankments and the realization of terraces for the elevation of the flat surfaces on two levels.…”

“…On the other hand, specialized studies have shown that the proximity towards a forested surface reduces the amount of radiation. This fact is important for people exposed to heat islands specific to the high-density areas of buildings, also having the role of mitigating the thermal stress in hot summer conditions [34,35].…”

Direct Short-Term Forecast of Photovoltaic Power through a Comparative Study between COMS and Himawari-8 Meteorological Satellite Images in a Deep Neural Network

“…More recent studies have focused on integrating meteorological satellite images in more complex machine learning models and deep learning networks, as past studies have demonstrated that such methods have the potential to yield superior forecast accuracy, particularly for shorter forecast time horizons [8]. COMS images were used to estimate solar radiation using a deep neural network (DNN) [23,24] and Multi-functional Transport Satellite (MTSAT) images were used to estimate hourly global solar radiation [25]. The MTSAT satellite was replaced by H8 in 2015.…”

“…For this study, COMS bands 1, 2, 4, and 5 and corresponding H8 bands 3, 7, 14, and 15 were used. COMS band 3 (WV) was omitted from this study due to its low significance in comparison to the other bands based on the results of using the random forest algorithm to analyze the relative significance of input variables for solar irradiance estimation [23]. COMS images were downloaded from NMSC as level-1B binary images for the Korea observation area [39], while H8 satellite images were downloaded from Japan Aerospace Exploration Agency (JAXA) using the P-Tree System as level-1 NetCDF data for the Japan observation area [40].…”

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

“…It constructs linear regression in a high-dimensional feature space and is trained based on the principle of structural risk minimization (SRM) [57][58][59][60]. The training dataset of SVR is mapped into the high-dimensional feature space using a nonlinear transformation, which is calculated by kernel functions such as the radial basis function (RBF), Gaussian, and polynomial functions [61,62]. SVR was realized by the software matlab2014a through the fitrsvm function [63].…”