Global horizontal irradiance prediction model considering the effect of aerosol optical depth based on the Informer model

Gao Xiu-Yan, Liu Jie-Mei, Yuan Yuan and Tan He-Ping

Renewable Energy, 2024, vol. 220, issue C

Abstract: Accurate and reliable global horizontal irradiance (GHI) prediction helps respond to the volatility of solar power output in advance. Aerosol optical depth (AOD) is the primary factor affecting the GHI in clear skies. In this study, the Informer model was first used to predict the GHI over the next 8 h by considering the AOD, meteorological parameters, and historical GHI data of Beijing as input variables, and the prediction of the GHI without AOD as a feature was calculated. The comparison results showed that the prediction errors of the models that considered AOD were lower than those of the models that did not. Moreover, when the AOD of the predicted day was larger, the difference in the mean absolute percentage error values between the two models was more significant. Hence, we proposed Planck's average AOD as a feature input to the GHI prediction model and found that the accuracy of the model improved regardless of the AOD size on the prediction day. The applicability of the developed model was verified using Golden's data. Overall, these findings indicate that this model can improve computational efficiency while maintaining good predictive performance.

Keywords: Global horizontal irradiance; Prediction; Aerosol optical depth; Planck's average; Informer model (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148123015860
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:220:y:2024:i:c:s0960148123015860

DOI: 10.1016/j.renene.2023.119671

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().