Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Introduction Read chapter Read first chapter

2014 | OriginalPaper | Chapter

2. Renewable Energy Sources—Modeling and Forecasting

Authors : Juan M. Morales, Antonio J. Conejo, Henrik Madsen, Pierre Pinson, Marco Zugno

Published in: Integrating Renewables in Electricity Markets

Publisher: Springer US

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Short-term forecasts of renewable power generation are a necessary input to nearly all operational problems in electricity markets. For instance, both market and system operators may use them for the clearing of day-ahead and real-time electricity markets. In addition, market participants rely on forecasts for determining their optimal offering strategies in view of uncertainties brought in by renewable energy production. The various forms of renewable power predictions are introduced here based on real-world examples. Special emphasis is placed on probabilistic forecasts in their general form and to scenarios mimicking spatial and temporal dependencies, as well as potential dependencies among different types of renewable energy sources. The way forecasts are issued and subsequently evaluated is also covered.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Introduction
next chapter Clearing the Day-Ahead Market with a High Penetration of Stochastic Production
Footnotes
1
UTC actually stands for Coordinated Universal Time, which is a time standard by which we regulate time and clocks.
 
Literature
1.
Bacher, P., Madsen, H., Nielsen, H.A.: Online short-term solar power forecasting. Sol. Energy 83(10), 1772–1783 (2009)CrossRef
2.
Gneiting, T.: Quantiles as optimal point forecasts. Int. J. Forecasting 27(2), 197–207 (2011)CrossRef
3.
Gneiting, T., Balabdaoui, F., Raftery, A.E.: Probabilistic forecasts, calibration and sharpness. J. Royal Stat. Soc. B 69(2), 243–268 (2007)CrossRef
4.
Gneiting, T., Stanberry, L.I., Grimit, E.P., Held, L., Johnson, N.A.: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds. Test 17(2), 211–235 (2008)CrossRef
5.
Hyndman, R.J.: Highest-density forecast regions for nonlinear and non-normal time series models. J. Forecasting 14(5), 431–441 (1995)CrossRef
6.
Jolliffe, I.T., Stephenson, D.B.: Forecast Verification - A Practitioner’s Guide in Atmospheric Science, 2nd edn. Wiley, New York (2012)
7.
Lange, M., Focken, U.: Physical Approach to Short-term Wind Power Prediction. Springer, Berlin (2005)
8.
Lorenz, E., Hurka, J., Heinemann, D., Beyer, H.G.: Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2(1), 2–10 (2009)CrossRef
9.
Madsen, H., Pinson, P., Kariniotakis, G., Nielsen, H.A., Nielsen, T.S.: Standardizing the performance evaluation of short-term wind power prediction models. Wind Eng. 29(6), 475–489 (2005)CrossRef
10.
Madsen, H., Thyregod, P.: Introduction to General and Generalized Linear Models. Chapman & Hall/CRC, Boca Raton (2011)
11.
Matos, M.A., Bessa, R.J.: Setting the operating reserve using probabilistic wind power forecasts. IEEE T. Power Syst. 26(2), 594–603 (2011)CrossRef
12.
Morales, J.M., Minguez, R., Conejo, A.J.: A methodology to generate statistically dependent wind speed scenarios. Appl. Energy 87(3), 843–855 (2010)CrossRef
13.
14.
Pinson, P., Girard, R.: Evaluating the quality of scenarios of short-term wind power generation. Appl. Energy 96, 12–20 (2012)CrossRef
15.
Pinson, P., Madsen, H., Nielsen, H.A., Papaefthymiou, G., Klöckl, B.: From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy 12(1), 51–62 (2009)CrossRef
16.
Pinson, P., Nielsen, H.A., Møller, J.K., Madsen, H., Kariniotakis, G.N.: Non-parametric probabilistic forecasts of wind power: Required properties and evaluation. Wind Energy 10(6), 497–516 (2007)CrossRef
17.
Pinson, P., Reikard, G., Bidlot, J.R.: Probabilistic forecasting of the wave energy flux. Appl. Energy 93, 364–370 (2012)CrossRef
18.
Reikard, G., Pinson, P., Bidlot, J.R.: Forecasting ocean wave energy: The ECMWF wave model and time series methods. Ocean Eng. 38(10), 1089–1099 (2011)CrossRef
19.
Wilks, D.S.: The minimum spanning tree histogram as a verification tool for multidimensional ensemble forecasts. Mon. Weather Rev. 132(6), 1329–1340 (2004)
Metadata
Title
Renewable Energy Sources—Modeling and Forecasting
Authors
Juan M. Morales
Antonio J. Conejo
Henrik Madsen
Pierre Pinson
Marco Zugno
Copyright Year
2014
Publisher
Springer US
Book
Integrating Renewables in Electricity Markets

Print ISBN: 978-1-4614-9410-2

Electronic ISBN: 978-1-4614-9411-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-1-4614-9411-9_2