Recent developments in multivariate wind and solar power forecasting

Mikkel L. Sorensen, Peter Nystrup, Mathias B. Bjerregard, Jan K. Moller, Peder Bacher, Henrik Madsen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The intermittency of renewable energy sources, such as wind and solar, means that they require reliable and accurate forecasts to integrate properly into energy systems. This review introduces and examines a selection of state-of-the-art methods that are applied for multivariate forecasting of wind and solar power production. Methods such as conditional parametric and combined forecasting already see wide use in practice, both commercially and scientifically. In the context of multivariate forecasting, it is vital to model the dependence between forecasts correctly. In recent years, reconciliation of forecasts to ensure coherency across spatial and temporal aggregation levels has shown great promise in increasing the accuracy of renewable energy forecasts. We introduce the methodology used for forecast reconciliation and review some recent applications for wind and solar power forecasting. Many forecasters are beginning to see the benefit of the greater information provided by probabilistic forecasts. We highlight stochastic differential equations as a method for probabilistic forecasting, which can also model the dependence structure. Lastly, we discuss forecast evaluation and how choosing a proper approach to evaluation is vital to avoid misrepresenting forecasts.
Original languageEnglish
Article numbere465
JournalWiley Interdisciplinary Reviews: Energy and Environment
Volume12
Issue number2
Number of pages20
ISSN2041-8396
DOIs
Publication statusPublished - 2023

Keywords

  • Forecast evaluation
  • Forecast reconciliation
  • Solar power forecasting
  • Stochastic differential equations
  • Wind power forecasting

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