Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data

Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung

Beitragende

Abstract

In this article, an abstract framework for annual averaged wind power output generation prediction of wind turbines is presented which is heavily based on large wind speed data sets and power curve data of wind turbines due to the rising interest in wind energy as one main future renewable energy source. As combinations of arbitrary power curve modeling techniques and arbitrary wind speed distributions based on wind speed data are seldom combined, the abstract combination of these two aspects in wind power output generation prediction in one pipeline is thoroughly described here. Conclusively, one detailed example wind speed data set from a weather station situation in Bremen, Germany illustrates applicability of the presented framework.

Details

OriginalspracheEnglisch
Aufsatznummer101499
FachzeitschriftMethodsX
Jahrgang8
PublikationsstatusVeröffentlicht - Jan. 2021
Peer-Review-StatusJa

Schlagworte

Ziele für nachhaltige Entwicklung

  • SDG 7 – Erschwingliche und saubere Energie - Data Analysis, Energy Analysis, Power Curve Modeling, Power Output Generation, Wind Energy, Wind Power Output Generation Prediction Algorithm, Wind Speed Probability Distributions

ASJC Scopus Sachgebiete

  • Allgemein - Data Analysis, Energy Analysis, Power Curve Modeling, Power Output Generation, Wind Energy, Wind Power Output Generation Prediction Algorithm, Wind Speed Probability Distributions

Schlagwörter

  • Data Analysis, Energy Analysis, Power Curve Modeling, Power Output Generation, Wind Energy, Wind Power Output Generation Prediction Algorithm, Wind Speed Probability Distributions