Short term solar radiation forecasting: Island versus continental sites. (15th October 2016)

• Record Type: Journal Article
• Authors: Boland, John; David, Mathieu; Lauret, Philippe
• Is Part Of: Energy. Volume 113(2016)
• Page Start: 186
• Page End: 192
• Publication Date: 2016-10-15
• Subjects: Fourier series -- ARMA -- Artificial neural nets -- Insular locations -- Solar forecasting; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.06.139
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Due its intermittency, the large-scale integration of solar energy into electricity grids is an issue and more specifically in an insular context. Thus, forecasting the output of solar energy is a key feature to efficiently manage the supply-demand balance. In this paper, three short term forecasting procedures are applied to island locations in order to see how they perform in situations that are potentially more volatile than continental locations. Two continental locations, one coastal and one inland are chosen for comparison. At the two time scales studied, ten minute and hourly, the island locations prove to be more difficult to forecast, as shown by larger forecast errors. It is found that the three methods, one purely statistical combining Fourier series plus linear ARMA models, one combining clear sky index models plus neural net models, and a third using a clear sky index plus ARMA, give similar forecasting results. It is also suggested that there is great potential of merging modelling approaches on different horizons. Highlights: Solar energy forecasting is more difficult for insular than continental sites. Fourier series plus linear ARMA models are one forecasting method tested. Clear sky index models plus neural net models are also tested. Clear sky index models plus linear ARMA is also an option. All three approaches have similar skill.