Master optimization process based on neural networks ensemble for 24-h solar irradiance forecast. (January 2015)

• Record Type: Journal Article
• Authors: Cornaro, C.; Pierro, M.; Bucci, F.
• Is Part Of: Solar energy. Volume 111(2015)
• Page Start: 297
• Page End: 312
• Publication Date: 2015-01
• Subjects: Solar irradiance -- Forecast -- Neural network -- MOS -- Ensemble; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2014.10.036
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: 24 h ahead hourly solar irradiance forecast using Artificial Neural Networks (ANN). A master optimization process based on ANN ensemble for the Rome site is used. 29% improvement on persistence using MOS–ANN model that corrects NWP with local data. Abstract: In the paper two models implemented to forecast the hourly solar irradiance with a day in advance are described. The models, based on Artificial Neural Networks (ANN), are generated by a master optimization process that defines the best number of neurons and selects a suitable ensemble of ANN. The two models consist of a Statistical (ST) model that uses only local measured data and a Model Output Statistics (MOS) that corrects Numerical Weather Prediction (NWP) data. ST and MOS are tested for the University of Rome "Tor Vergata" site. The models are trained and validated using one year data. Through a cross training procedure, the dependence of the models on the training year is also analyzed. The performance of ST, NWP and MOS models, together with the benchmark Persistence Model (PM), are compared. The ST model and the NWP model exhibit similar results. Nevertheless different sources of forecast errors between ST and NWP models are identified. The MOS model gives the best performance, improving the forecast of approximately 29% with respect to the PM.