Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model

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.

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

“…Despite the technological advancements of H8 such as higher spatial resolution, the superior performance of COMS bands can be explained in part by the different orbits of the two satellites. COMS is situated at 128.2 • E and adjusted to the Korean peninsula, whereas H8 is at 140.7 • E, which is biased to the east of Korea [24]. However, some of the differences in p% of NMAE between COMS and H8 were marginal and possibly insufficient to differentiate particularly superior performance of a single satellite dataset.…”

“…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.…”

“…In this study, COMS images were cropped to 61 by 61-pixel subsets, while H8 images were cropped to 121 by 121-pixel subsets. Since this study only used individual pixels from each satellite image, cropping the images is not a mandatory preprocessing step; however, cropped subsets can be used in patches for additional experiments to consider local conditions by exploiting neighboring pixels of the satellite image [24]. Next, COMS images are converted to albedo and brightness temperature values using look-up tables provided by the NMSC.…”

“…Since PV power data were only available for a one-year span, the inputted data may include seasonal bias for specific months [24]. One example of monthly bias as a result of seasonality can occur in the form of timestamps with low to zero irradiation in the input dataset from the first setup.…”

Forecasting the Global Horizontal Irradiance based on Boruta Algorithm and Artificial Neural Networks using a Lower Cost

“…The results obtained show very good accuracy. In this regards, it noticed that in many countries, the number of GHI measurement locations is sufficient as in KSA and insufficient in other countries as in Korea [37], [38], where the satellite images can be helpful sources for getting the GHI over a wide area space, in these cases, usually predicted by secondary parameters such as readily obtainable climatic variables.…”