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

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.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1605703
Report Number(s):
[NREL/JA-2C00-74237]
Grant/Contract Number:  
[AC36-08GO28308]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Physics: Conference Series
Additional Journal Information:
[ Journal Volume: 1452]
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; wind forecasting; wind speed; simulations; power grid

Citation Formats

Perr-Sauer, Jordan, Tripp, Charles, Optis, Michael, and King, Jennifer R. Short-Term Wind Forecasting Using Statistical Models with a Fully Observable Wind Flow. United States: N. p., 2020. Web. doi:10.1088/1742-6596/1452/1/012083.
Perr-Sauer, Jordan, Tripp, Charles, Optis, Michael, & King, Jennifer R. Short-Term Wind Forecasting Using Statistical Models with a Fully Observable Wind Flow. United States. doi:10.1088/1742-6596/1452/1/012083.
Perr-Sauer, Jordan, Tripp, Charles, Optis, Michael, and King, Jennifer R. Tue . "Short-Term Wind Forecasting Using Statistical Models with a Fully Observable Wind Flow". United States. doi:10.1088/1742-6596/1452/1/012083.
@article{osti_1605703,
title = {Short-Term Wind Forecasting Using Statistical Models with a Fully Observable Wind Flow},
author = {Perr-Sauer, Jordan and Tripp, Charles and Optis, Michael and King, Jennifer R},
abstractNote = {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.},
doi = {10.1088/1742-6596/1452/1/012083},
journal = {Journal of Physics: Conference Series},
number = ,
volume = [1452],
place = {United States},
year = {2020},
month = {3}
}

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Works referenced in this record:

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