Abstract
Renewable Energy Sources (RES) are one of the key solutions to handle the world’s future energy needs, while decreasing carbon emissions. To produce electricity, large concentrating solar power plants depend on Direct Normal Irradiance (DNI), which is rapidly variable under broken clouds conditions. To work at optimum capacity while maintaining stable grid conditions, such plants require accurate DNI forecasts for various time horizons. The main goal of this study is the forecasting of DNI over two short-term horizons: 15-min and 1-h. The proposed system is purely based on historical local data and Artificial Neural Networks (ANN). For this aim, 1-min solar irradiance measurements have been obtained from two sites in different climates. According to the forecast results, the coefficient of determination (R2) ranges between 0.500 and 0.851, the Mean Absolute Percentage Error (MAPE) between 0.500 and 0.851, the Normalized Mean Squared Error (NMSE) between 0.500 and 0.851, and the Root Mean Square Error (RMSE) between 0.065 kWh/m2 and 0.105 kWh/m2. The proposed forecasting models show a reasonably good forecasting capability, which is decisive for a good management of solar energy systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kaur, A., Nonnenmacher, L., Pedro, H.T., Coimbra, C.F.: Benefits of solar forecasting for energy imbalance markets. Renew. Energy 86, 819–830 (2016)
Law, E.W., Prasad, A.A., Kay, M., Taylor, R.A.: Direct normal irradiance forecasting and its application to concentrated solar thermal output forecasting–a review. Sol. Energy 108, 287–307 (2014)
Chu, Y., Pedro, H.T., Coimbra, C.F.: Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning. Sol. Energy 98, 592–603 (2013)
Peruchena, C.M.F., GastĂłn, M., Schroedter-Homscheidt, M., Kosmale, M., Marco, I.M., GarcĂa-Moya, J.A., Casado-Rubio, J.L.: Dynamic paths: towards high frequency direct normal irradiance forecasts. Energy 132, 315–323 (2017)
Gueymard, C.A.: Temporal variability in direct and global irradiance at various time scales as affected by aerosols. Sol. Energy 86(12), 3544–3553 (2012)
Twidell, J., Weir, T.: Renewable energy resources. Routledge, London (2015)
Kleissl, J.: Solar energy forecasting and resource assessment. Academic Press, Boston (2013)
Jović, A., Brkić, K., Bogunović, N.: A review of feature selection methods with applications. In: Information and Communication Technology, 38th International Convention on Electronics and Microelectronics (MIPRO), pp. 1200–1205. IEEE (2015)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)
Ineichen, P., Perez, R.: A new airmass independent formulation for the Linke turbidity coefficient. Sol. Energy 73(3), 151–157 (2002)
Gueymard, C.A., Ruiz-Arias, J.A.: Extensive worldwide validation and climate sensitivity analysis of direct irradiance predictions from 1-min global irradiance. Sol. Energy 128, 1–30 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Atmani, H., Bouzgou, H., Gueymard, C.A. (2018). Intra-hour Forecasting of Direct Normal Solar Irradiance Using Variable Selection with Artificial Neural Networks. In: Hatti, M. (eds) Artificial Intelligence in Renewable Energetic Systems. ICAIRES 2017. Lecture Notes in Networks and Systems, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-73192-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-73192-6_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73191-9
Online ISBN: 978-3-319-73192-6
eBook Packages: EngineeringEngineering (R0)