An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery

Alvaro Linares-Rodriguez, Samuel Quesada-Ruiz, David Pozo-Vazquez and Joaquin Tovar-Pescador

Energy, 2015, vol. 91, issue C, 264-273

Abstract: A new evolutionary design of an ANN (artificial neural network) ensemble model is developed to generate hourly DNI (direct normal irradiance) estimates. The procedure combines a genetic algorithm for selecting the best inputs with an ANN ensemble method. The ensemble model was calibrated and evaluated using three years of Meteosat-9 images and data measured at 28 high-quality ground stations over an extensive area, mainly in Europe. The most valuable inputs for DNI estimation are shown to be the following: all Meteosat-9 channels except ch08 and ch11; relative air mass m, integral Rayleigh optical thickness δr, extraterrestrial global irradiance G0, beam clear-sky index Bcs, and the cosine of zenith angle θ. No additional atmospheric information such as turbidity, aerosol optical depth or water vapor content are required for the model. Ensemble estimates were nearly unbiased (MBE = 1.98%) and overall RMSE (root mean square error) was 24.29% across an independent spatial and temporal dataset. This represents an improvement of 35% over other common methods for estimating DNI. The estimates were reasonably reliable in all seasons, and were more accurate in clear-sky conditions.

Keywords: Direct normal solar radiation; Evolutionary artificial neural network; Ensemble model; Meteosat images; Satellite-derived irradiances (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544215011172
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:energy:v:91:y:2015:i:c:p:264-273

DOI: 10.1016/j.energy.2015.08.043

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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