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Title: Characterizing forecastability of wind sites in the United States

Abstract

With the rapid growth of wind power, managing its uncertainty and variability becomes critical in power system operations. Wind forecasting is one of the enablers to partially tackle challenges associated with wind power uncertainty. To improve the 'forecasting ability', defined as forecastability, different forecasting methods have been developed to assist grid integration of wind energy. However, forecasting performance not only relies on the power of forecasting models, but is also related to local weather conditions and (known as wind characteristics) wind farm properties. In this study, geospatial and instance spatial distributions of six wind characteristics and two forecasting error metrics are first analyzed based on 126,000 + wind sites in the United States. Forecasts in different look-ahead times are generated by using a machine learning based multi-model forecasting framework model and the Weather Research and Forecasting model. A forecastability quantification method is developed by characterizing the relationship between forecastability and wind series entropy using three regression methods, i.e., linear approximation, locally weighted scatterplot smoother nonlinear nonparametric regression, and quantile regression. It is found that the forecastability of a wind site can be successfully characterized by wind series characteristics, thereby providing valuable information at different stages of wind energy projects.

Authors:
ORCiD logo [1];  [1];  [1];  [2];  [2];  [1]
  1. Univ. of Texas, Richardson, TX (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1471481
Report Number(s):
NREL/JA-5D00-72346
Journal ID: ISSN 0960-1481
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Renewable Energy
Additional Journal Information:
Journal Volume: 133; Journal ID: ISSN 0960-1481
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; forecastability; time series analysis; wind power forecasting

Citation Formats

Feng, Cong, Sun, Mucun, Cui, Mingjian, Chartan, Erol Kevin, Hodge, Bri-Mathias, and Zhang, Jie. Characterizing forecastability of wind sites in the United States. United States: N. p., 2018. Web. doi:10.1016/j.renene.2018.08.085.
Feng, Cong, Sun, Mucun, Cui, Mingjian, Chartan, Erol Kevin, Hodge, Bri-Mathias, & Zhang, Jie. Characterizing forecastability of wind sites in the United States. United States. doi:10.1016/j.renene.2018.08.085.
Feng, Cong, Sun, Mucun, Cui, Mingjian, Chartan, Erol Kevin, Hodge, Bri-Mathias, and Zhang, Jie. Wed . "Characterizing forecastability of wind sites in the United States". United States. doi:10.1016/j.renene.2018.08.085. https://www.osti.gov/servlets/purl/1471481.
@article{osti_1471481,
title = {Characterizing forecastability of wind sites in the United States},
author = {Feng, Cong and Sun, Mucun and Cui, Mingjian and Chartan, Erol Kevin and Hodge, Bri-Mathias and Zhang, Jie},
abstractNote = {With the rapid growth of wind power, managing its uncertainty and variability becomes critical in power system operations. Wind forecasting is one of the enablers to partially tackle challenges associated with wind power uncertainty. To improve the 'forecasting ability', defined as forecastability, different forecasting methods have been developed to assist grid integration of wind energy. However, forecasting performance not only relies on the power of forecasting models, but is also related to local weather conditions and (known as wind characteristics) wind farm properties. In this study, geospatial and instance spatial distributions of six wind characteristics and two forecasting error metrics are first analyzed based on 126,000 + wind sites in the United States. Forecasts in different look-ahead times are generated by using a machine learning based multi-model forecasting framework model and the Weather Research and Forecasting model. A forecastability quantification method is developed by characterizing the relationship between forecastability and wind series entropy using three regression methods, i.e., linear approximation, locally weighted scatterplot smoother nonlinear nonparametric regression, and quantile regression. It is found that the forecastability of a wind site can be successfully characterized by wind series characteristics, thereby providing valuable information at different stages of wind energy projects.},
doi = {10.1016/j.renene.2018.08.085},
journal = {Renewable Energy},
number = ,
volume = 133,
place = {United States},
year = {2018},
month = {8}
}

Journal Article:
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Table 1: Part 1 Table 1: Part 1: Case study summary

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