Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model
| dc.contributor.author | Koo, YoungHyun | |
| dc.contributor.author | Oh, Myeongchan | |
| dc.contributor.author | Kim, Sung-Min | |
| dc.contributor.author | Park, Hyeong-Dong | |
| dc.date.accessioned | 2021-04-19T15:18:16Z | |
| dc.date.available | 2021-04-19T15:18:16Z | |
| dc.date.issued | 2020-01-07 | |
| dc.date.updated | 2021-04-19T15:18:16Z | |
| dc.description.abstract | The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m2 (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea. | |
| dc.description.department | Earth and Planetary Sciences | |
| dc.identifier | doi: 10.3390/en13020301 | |
| dc.identifier.citation | Energies 13 (2): 301 (2020) | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12588/474 | |
| dc.rights | Attribution 4.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | solar irradiance | |
| dc.subject | solar mapping | |
| dc.subject | Korea | |
| dc.subject | remote sensing | |
| dc.subject | COMS Meteorological Imager (MI) | |
| dc.subject | artificial neural network (ANN) | |
| dc.subject | solar energy | |
| dc.title | Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model | |
| dc.type | Article |