Open Access

ARTICLE

Research on Spatial Statistical Downscaling Method of Meteorological Data Applied to Photovoltaic Prediction

Yan Jin^1,*, Dingmei Wang², Ruiping Zhang¹, Haiying Dong¹

1 School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730000, China
2 Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou, 730000, China

* Corresponding Author: Yan Jin. Email:

Energy Engineering 2022, 119(5), 1923-1940. https://doi.org/10.32604/ee.2022.018750

Received 14 August 2021; Accepted 29 October 2021; Issue published 21 July 2022

Abstract

Aiming at the low spatial resolution of meteorological data output from a numerical model in photovoltaic power prediction, a geographically weighted statistical downscaling method considers the influence factors such as normalized vegetation index (NDVI), digital elevation model (DEM), slope direction, longitude and latitude is proposed. This method is based on the correlation between meteorological data and NDVI, DEM, slope direction, latitude and longitude, and introduces DEM and local Moran index to improve the regression model, and obtains 100 * 100 m high-resolution meteorological spatial distribution data. Finally, combining the measured data of the study area and the established EOF iterative downscaling method to verify and compare the downscaling results. The results show that the error between the downscaled meteorological data and the measured value is smaller, and the comprehensive downscaling accuracy of the geographically weighted regression method is higher, and the model fitting effect is better. Therefore, this method can effectively improve the influence of errors caused by lower resolution, and provide a more reliable meteorological basis for the prediction of photovoltaic power.

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