Short-Term Wind Forecasting Using Statistical Models with a Fully Observable Wind Flow

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Abstract

The utility of model output data from the Weather Research and Forecasting mesoscale model is explored for very short-term forecasting (5-30 minutes horizon) of wind speed to be used in large scale simulations of an autonomous electric power grid. Using this synthetic data for the development and evaluation of short-term forecasting algorithms offer many unique advantages over observational data, such as the ability to observe the full wind flow field in the surrounding region. Several short-term forecasting algorithms are implemented and evaluated using the synthetic data at several different time horizons and for three different geographic locations. Comparison is made with observational data from one location. We find that short-term forecasts of the synthetic data considering wind flow from the surrounding region perform 26% better than persistence in terms of root mean square error at the 5-minute time horizon. This improvement is comparable to studies of observational data in the literature. These results provide motivation to use synthetic data for short term forecasting in grid simulations, and open the door to future algorithmic improvements.

Original languageAmerican English
Article numberArticle No. 012083
Number of pages12
JournalJournal of Physics: Conference Series
Volume1452
Issue number1
DOIs
StatePublished - 3 Mar 2020
EventNorth American Wind Energy Academy, NAWEA 2019 and the International Conference on Future Technologies in Wind Energy 2019, WindTech 2019 - Amherst, United States
Duration: 14 Oct 201916 Oct 2019

Bibliographical note

Publisher Copyright:
© 2020 IOP Publishing Ltd. All rights reserved.

NREL Publication Number

  • NREL/JA-2C00-74237

Keywords

  • power grid
  • simulations
  • wind forecasting
  • wind speed

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