In this paper, several models to forecast the hourly solar irradiance with a day in advance using artificial neural network techniques have been developed and analyzed. The forecast irradiance is the one incident on the plane of the modules array of a photovoltaic plant. Pure statistical (ST) models that use only local measured data and model output statistics (MOS) approaches to refine numerical weather prediction data are tested for the University of Rome “Tor Vergata” site. The performance of ST and MOS, together with the persistence model (PM), is compared. The ST models improve the performance of the PM of around 20%. The combination of ST and NWP in the MOS approach gives the best performance, improving the forecast of approximately 39% with respect to the PM.
Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction
Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received March 28, 2014; final manuscript received December 18, 2014; published online January 8, 2015. Assoc. Editor: Philippe Blanc.
- Views Icon Views
- Share Icon Share
- Cite Icon Cite
- Search Site
Cornaro, C., Bucci, F., Pierro, M., Del Frate, F., Peronaci, S., and Taravat, A. (June 1, 2015). "Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction." ASME. J. Sol. Energy Eng. June 2015; 137(3): 031011. https://doi.org/10.1115/1.4029452
Download citation file:
- Ris (Zotero)
- Reference Manager