Abstract
In this work we first explore the use of Support Vector Regression to forecast day-ahead daily and 3-hourly aggregated photovoltaic (PV) energy production on Spain using as inputs Numerical Weather Prediction forecasts of global horizontal radiation and total cloud cover. We then introduce an empirical “clear sky” PV energy curve that we use to disaggregate these predictions into hourly day-ahead PV forecasts. Finally, we use Ridge Regression to refine these day-ahead forecasts to obtain same-day hourly PV production updates that for a given hour \(h\) use PV energy readings up to that hour to derive updated PV forecasts for hours \(h+1, h+2, \ldots \). While simple from a Machine Learning point of view, these methods yield encouraging first results and also suggest ways to further improve them.
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
Benghanem, M., Mellit, A., Alamri, S.: Ann-based modelling and estimation of daily global solar radiation data: a case study. Energy Convers. Manage. 50(7), 1644–1655 (2009)
Chang, C., Lin, C.: LIBSVM a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chu, W., Keerthi, S.S., Ong, C.J.: Bayesian support vector regression using a unified loss function. IEEE Trans. Neural Netw. 15(1), 29–44 (2004)
ECMWF: European Center for Medium-range Weather Forecasts (2005). http://www.ecmwf.int/
GFS: NOAA Global Forecast System (2014). http://www.emc.ncep.noaa.gov/index.php?branch=GFS
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
Kleissl, J.: Solar Energy Forecasting and Resource Assessment. Academic Press, New York (2013)
Lin, C.J., Weng, R.C.: Simple probabilistic predictions for support vector regression. Technical report, Department of Computer Science, National Taiwan University (2003)
Mellit, A., Kalogirou, S.A.: Artificial intelligence techniques for photovoltaic applications: a review. Prog. Energy Combust. Sci. 34(5), 574–632 (2008)
Myers, D.R.: Solar radiation modeling and measurements for renewable energy applications: data and model quality. Energy 30(9), 1517–1531 (2005)
Pedro, H., Coimbra, C.: Assessment of forecasting techniques for solar power output with no exogenous inputs. Sol. Energy 86, 2017–2028 (2012)
Pedro, H., Coimbra, C.: Stochastic learning methods. In: Kleissl, J. (ed.) Solar Energy Forecasting and Resource Assessment, pp. 383–407. Academic Press, New York (2013)
Scholkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Ulbricht, R., Fischer, U., Lehner, W., Donker, H.: First steps towards a systematical optimized strategy for solar energy supply forecasting. In: Proceedings of the DARE 2013, Data Analytics for Renewable Energy Integration Workshop, pp. 14–25 (2013)
Wolff, B., Lorenz, E., Kramer, O.: Statistical learning for short-term photovoltaic power predictions. In: Proceedings of the DARE 2013, Data Analytics for Renewable Energy Integration Workshop, pp. 2–13 (2013)
Acknowledgments
With partial support from Spain’s grants TIN2010-21575-C02-01 and TIN2013-42351-P, and the UAM–ADIC Chair for Machine Learning. We thank Red Elctrica de Espaa for useful discussions and making available PV energy data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Fernández, Á., Gala, Y., Dorronsoro, J.R. (2014). Machine Learning Prediction of Large Area Photovoltaic Energy Production. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2014. Lecture Notes in Computer Science(), vol 8817. Springer, Cham. https://doi.org/10.1007/978-3-319-13290-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-13290-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13289-1
Online ISBN: 978-3-319-13290-7
eBook Packages: Computer ScienceComputer Science (R0)