Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery

Myeongchan Oh,Hyun-Goo Kim,Chang Ki Kim,Boyoung Kim,Changyeol Yun,Yong-Heack Kang

doi:10.3390/en14082216

Abstract

Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our results show that the skill score of the optimized model is 21% better than the previous CMV model. It is, thus, considered to be appropriate for use in short-term forecasting over large areas. The results of this study are expected to promote the use of spatial data in solar forecasting models, which could improve their accuracy and provide various insights for the planning and operation of photovoltaic plants.

Highlights

The growing importance of solar forecasting has been emphasized in many published studies and in review articles comparing different forecasting techniques
Block matching is the most popular cloud motion vector (CMV) model used for solar forecasting [11,14,15], and it identifies the vectors by estimating the displacement of similar areas in the before and after images
Three accuracy metrics were used in the analysis: The root mean squared error (RMSE), mean bias error (MBE), and the skill score (SS)

Summary

Introduction

The growing importance of solar forecasting has been emphasized in many published studies and in review articles comparing different forecasting techniques. Other CMV models using a type of optical flow (OF) have been applied in several regions [19,20,21,22], and remarkable performances have been obtained Many of these studies have shown that CMV models perform better than numerical weather prediction (NWP) models for short forecast horizons (less than a few hours) [12,14,15]. CMV models yield a high performance, they assume that the shape, movement, and intensity of the cloud are constant, but this does not reflect the highly dynamic behavior of clouds These assumptions are applied to the extraction methods of CMV, and this limits their accuracy and forecasting steps. Such limitations, significantly lower the performance of the model in areas with complex cloud dynamics.

Study Area

Cloud Motion Vector Models

Spatial Averaging

Spatial Accuracy Analysis

Spatiotemporal Optimization

Performance of Forecasting Models