Abstract
We first analyze some features of numerical weather predictions (NWP) for global solar radiation and notice that they are undersmooth. This finding opens a way to improvements via various smoothing strategies. Then we introduce a statistical modeling framework based on modern semiparametric regression. We use a numerical weather prediction (NWP) model output as one of the inputs for our statistical model. The statistical model is build on the modern regression formalism, utilizing nonparametric B-splines for nonlinear parts whose exact shape is unknown a priori (apart from physically motivated smoothness). Then we illustrate its abilities for systematic development of strategies for NWP calibration and further development. The results are useful both for practical forecasting and as a source of feedback for NWP modelers.
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
EPIA 2014: European Photovoltaic Industry Association. Global Market Outlook for Photovoltaics 2014–2018. http://www.cleanenergybusinesscouncil.com/site/resources/files/reports/EPIA_Global_Market_Outlook_for_Photovoltaics_2014-2018_-_Medium_Res.pdf
COST Action ES1002: Weather Intelligence for Renewable Energies. http://www.wire1002.ch/
Diagne, H.M., David, M., Lauret, P., Boland, J.: Solar irradiance forecasting: state-of-the-art and proposition for future developments for small-scale insular grids. In: Proceedings of the World Renewable Energy Forum 2012, Denver, USA (2012)
Paulescu, M., Paulescu, E., Gravila, P., Badescu, V.: Weather Modeling and Forecasting of PV System Operation. Springer, London (2013)
Reikard, G.: Predicting solar radiation at high resolution: a comparison of time series forecasts. Sol. Energy 83, 342–349 (2009)
Kumar, R., Aggarwal, R.D., Sharma, J.D.: New regression model to estimated global solar radiation using artificial neural network. Adv. Energy Eng. 1, 66–73 (2013)
Brabec, M., Paulescu, M., Badescu, V.: Tailored vs black-box models for forecasting hourly average solar irradiance. Sol. Energy 111, 320–331 (2015)
Warner, T.T.: Weather Modeling and Forecasting of PV System Operation. Cambridge University Press, Cambridge (2011)
Mathiesen, P., Kleissl, J.: Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States. Sol. Energy 85, 967–977 (2011)
Perez, R., Lorenz, E., Pelland, S., Beauharnois, M., Knowe, G.V., Hemker, K., Heinemann, D., Remund, J., et al.: Comparison of numerical weather prediction solar irradiance forecasts in the US Canada and Europe. Sol. Energy 94, 305–326 (2013)
Glahn, B., Gilbert, K., Cosgrove, R., Ruth, D., Sheets, K.: The gridding of MOS. Weather Forecast. 24, 520–529 (2009)
Koenker, R.: Quantile Regression. Cambridge University Press, Cambridge (2005)
Banerjee, S., Carlin, B.P., Gelfand, A.E.: Hierarchical Modeling and Analysis for Spatial Data. Chapman and Hall/CRC Press, London (2004)
Wood, S.N.: Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC, London (2006)
R Core Team 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
The R-INLA project 2016. http://www.r-inla.org/
Acknowledgments
The work on this article was partly supported by the long-term strategic development financing of the Institute of Computer Science (RVO:67985807) and by the Czech Science Foundation grant GA13-34856S, Advanced random field methods in data assimilation for short-term weather prediction.
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
Brabec, M., Eben, K., Pelikan, E., Krc, P., Resler, J., Jurus, P. (2018). Statistical Modeling for Improvement of Numerical-Model-Based Solar Radiation Forecasts. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-60834-1_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60833-4
Online ISBN: 978-3-319-60834-1
eBook Packages: EngineeringEngineering (R0)