Abstract
The increasing share of fluctuating renewable energy sources on the world-wide energy production leads to a rising public interest in dedicated forecasting methods. As different scientific communities are dedicated to that topic, many solutions are proposed but not all are suited for users from utility companies. We describe an empirical approach to analyze the scientific relevance of renewable energy forecasting methods in literature. Then, we conduct a survey amongst forecasting software providers and users from the energy domain and compare the outcomes of both studies.
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
Notes
- 1.
Global Energy Forecasting Competition, http://www.gefcom.org.
- 2.
References
Chen, Z., Yang, Y.: Assessing forecast accuracy measures. Technical report 2004–2010, Iowa State University, Department of Statistics & Statistical Laboratory (2004)
Colak, I., Sagiroglu, S., Yesilbudak, M.: Data mining and wind power prediction: a literature review. Renew. Energy 46, 241–247 (2012)
Glassley, W., Kleissl, J., van Dam, C.P., Shiu, H., Huang, J., Braun, G., Holland, R.: Current state of the art in solar forecasting. Technical report, California Renewable Energy Collaborative (CREC) (2012)
Hong, T., Pinson, P., Fan, S.: Global energy forecasting competition 2012. Int. J. Forecast. 30(2), 357–363 (2013)
Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)
Kostylev, V., Pavlovski, A.: Solar power forecasting performance - towards industry standards. In: 1st International Workshop on the Integration of Solar Power into Power Systems, Aarhus, Denmark (2011)
Madsen, H., Kariniotakis, G., Nielsen, H., Nielsen, T., Pinson, P.: A protocol for standardizing the performance evaluation of short-term wind power prediction models. Anemos project, European Commission (2004)
Monteiro, C., Bessa, R., Miranda, V., Botterud, A., Wang, J., Conzelmann, G., et al.: Wind power forecasting: state-of-the-art 2009. Technical report, Argonne National Laboratory (ANL) (2009)
National Renewable Energy Laboratory. Transmission Grid Integration - Data and Resources. http://www.nrel.gov/electricity/transmission/data_resources.html. Accessed 16 July 2016
Tascikaraoglu, A., Uzunoglu, M.: A review of combined approaches for prediction of short-term wind speed and power. Renew. Sustain. Energy Rev. 34, 243–254 (2014)
Ulbricht, R., Hahmann, M., Donker, H., Lehner, W.: Systematical evaluation of solar energy supply forecasts. In: Lee Woon, W., Aung, Z., Madnick, S. (eds.) Data Analytics for Renewable Energy Integration, pp. 108–121. Springer, Heidelberg (2014)
Yadav, A.K., Chandel, S.S.: Solar radiation prediction using artificial neural network techniques: a review. Renew. Sustain. Energy Rev. 33, 772–781 (2014)
Acknowledgment
The work presented in this paper was funded by the European Regional Development Fund (EFRE) under co-financing by the Free State of Saxony and Robotron Datenbank-Software GmbH.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix: List of Reviewed Journal Articles
Appendix: List of Reviewed Journal Articles
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ulbricht, R., Thoß, A., Donker, H., Gräfe, G., Lehner, W. (2017). Dealing with Uncertainty: An Empirical Study on the Relevance of Renewable Energy Forecasting Methods. In: Woon, W., Aung, Z., Kramer, O., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2016. Lecture Notes in Computer Science(), vol 10097. Springer, Cham. https://doi.org/10.1007/978-3-319-50947-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-50947-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50946-4
Online ISBN: 978-3-319-50947-1
eBook Packages: Computer ScienceComputer Science (R0)