| Title |
Application and Case Study of Spatial Statistical Interpolation Technology for Estimating the Photovoltaic power plant Solar Radiation in an Arbitrary Location |
| Authors |
이예지(Lee, Ye-Ji) ; 최두성(Choi, Do-Sung) ; 고명진(Ko, Myeong-Jin) |
| DOI |
https://doi.org/10.5659/JAIK.2022.38.9.225 |
| Keywords |
Renewable Energy; Photovoltaic System; Solar Radiation; Inverse Distance Weighting Method; Spatial Interpolation |
| Abstract |
In order to predict and manage the amount of power output of a photovoltaic system in a distributed grid, solar radiation prediction is
essential. In order to increase the accuracy of the solar radiation prediction model, data measured at the target location where the power
plant is located should be used. However, if there is no observation data, public data such as ASOS and AWS operated by the government
can be effectively utilized. if the target location is far away from the station, uncertainty in the prediction is expected to increase due to the
difference in distance. In this study, in order to solve this problem, solar radiation was estimated using inverse distance weighted interpolation
(IDW), a spatial statistical technique that can estimate the values of unsampled locations. In addition, the possibility of application of the
inverse distance weighted interpolation (IDW) was confirmed by validating the prediction model as a case study of six solar power plants in
operation. As a result, the average MAPE of study cases was 9.05%, which was found to be 1.27 times more accurate on average than the
nearest ASOS, respectively. In particular, it was confirmed that the inverse distance weighted interpolation (IDW) prediction error was the
lowest in July, and the accuracy of the prediction was higher in regions with denser contours of predicted solar radiation. |