Abstract
Solar power forecasts are gaining continuous importance as the penetration of solar energy into the grid rises. The natural variability of the solar resource, joined to the difficulties of cloud movement modeling, endow solar power forecasts with a certain level of uncertainty. Important efforts have been carried out in the field to reduce as much as possible the errors. Various approaches have been followed, being the predominant nowadays the use of statistical techniques to model production.
In this study, we have performed a comparison study between two extensively used statistical techniques, support vector regression (SVR) machines and random forests, and two other techniques that have been scarcely applied to solar forecasting, deep neural networks and extreme gradient boosting machines. Best results were obtained with the SVR technique, showing a nRMSE of 22.49%. To complete the assessment, a weighted blended model consisting on an average weighted combination of individual predictions was created. This blended model outperformed all the models studied, with a nRMSE of 22.24%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Almeida, M.P., Perpinan, O., Narvarte, L.: PV power forecast using a nonparametric PV model. Solar Energy 115, 354–368 (2015)
Almonacid, F., Perez-Higueras, P.J., Fernendez, E.F., Hontoria, L.: A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Convers. Manage. 85, 389–398 (2014). http://dx.doi.org/10.1016/j.enconman.2014.05.090
Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de Pison, F.J., Antonanzas-Torres, F.: Review of photovoltaic power forecasting. Solar Energy 136, 78–111 (2016). http://dx.doi.org/10.1016/j.solener.2016.06.069
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chen, T., He, T.: XGBoost: eXtreme gradient boosting. R package version 0.4–2 (2015)
Da Silva Fonseca Junior, J.G., Oozeki, T., Ohtake, H., Shimose, K., Takashima, T., Ogimoto, K.: Regional forecasts and smoothing effect of photovoltaic power generation in Japan: an approach with principal component analysis. Renew. Energy 68, 403–413 (2014). http://dx.doi.org/10.1016/j.renene.2014.02.018
Hossain, R., Maung, A., Oo, T., Ali, A.B.M.S.: Hybrid prediction method for solar power using different computational intelligence algorithms. Smart Grid. Renew. Energy 4, 76–87 (2013)
Li, Y., Su, Y., Shu, L.: An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renew. Energy 66, 78–89 (2014). http://dx.doi.org/10.1016/j.renene.2013.11.067
Vapnik, V., Lerner, A.: Pattern recognition using generalized portrait method. Autom. Remote Control 24, 774–780 (1963)
Wang, F., Zhen, Z., Mi, Z., Sun, H., Su, S., Yang, G.: Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting. Energy Buildings 86, 427–438 (2015). http://dx.doi.org/10.1016/j.enbuild.2014.10.002
Acknowledgments
J. Antonanzas and R. Urraca would like to acknowledge the fellowship FPI-UR-2014 granted by the University of La Rioja. This work used the Beronia cluster (Universidad de La Rioja), which is supported by FEDER-MINECO grant number UNLR-094E-2C-225.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Antonanzas, J., Urraca, R., Pernía-Espinoza, A., Aldama, A., Fernández-Jiménez, L.A., Martínez-de-Pisón, F.J. (2017). Single and Blended Models for Day-Ahead Photovoltaic Power Forecasting. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-59650-1_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59649-5
Online ISBN: 978-3-319-59650-1
eBook Packages: Computer ScienceComputer Science (R0)