1.11 - Solar Power Forecasts

Lennard Visser, Elke Lorenz, Detlev Heinemann, Wilfried G.J.H.M. van Sark

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Power generation from solar and wind energy systems is highly variable due to its dependence on meteorological conditions. An efficient use of these fluctuating energy sources requires reliable forecast information for management and operation strategies. We give an overview of different applications and state-of-the-art models for solar photovoltaic power forecasting. These include physical, regressive, machine learning and time series models and generate either point or probabilistic forecsts. Additionally, techniques of collecting information on solar irradiance, cloud movement, and weather predictions are discussed, including on-site measurements by sensor networks, all-sky imaging, satellite imaging and numerical weather prediction-based models. Finally, we present an overview of error metrics that are commonly used in solar power forecasting to evaluate the performance of the forecast models.

Original languageEnglish
Title of host publicationComprehensive Renewable Energy, Second Edition
Subtitle of host publicationVolume 1-9
EditorsTrevor M. Letcher
PublisherElsevier
Chapter1.11
Pages213-233
Number of pages21
Volume1
Edition2nd
ISBN (Electronic)9780128197349
ISBN (Print)9780128197271
DOIs
Publication statusPublished - Jan 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd. All rights reserved.

Keywords

  • All-sky imaging
  • Cloud motion vectors
  • Grid integration of PV power
  • Machine learning models
  • Meteorological variables
  • Numerical weather prediction
  • Performance metrics
  • Photovoltaics
  • Point forecasts
  • Probabilistic forecasts
  • PV power forecasts
  • PV systems
  • Regression models, time series models
  • Satellite imaging

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