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Title: Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

Abstract

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% tomore » the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.« less

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1233280
Report Number(s):
NREL/CP-5D00-64960
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 5th International Workshop on Integration of Solar Power Into Power Systems, 19-20 October 2015, Brussels, Belgium
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; solar forecasting; uncertainty; numerical weather prediction; machine learning; sensitivity analysis; National Renewable Energy Laboratory; NREL

Citation Formats

Cheung, WanYin, Zhang, Jie, Florita, Anthony, Hodge, Bri-Mathias, Lu, Siyuan, Hamann, Hendrik F., Sun, Qian, and Lehman, Brad. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint. United States: N. p., 2015. Web.
Cheung, WanYin, Zhang, Jie, Florita, Anthony, Hodge, Bri-Mathias, Lu, Siyuan, Hamann, Hendrik F., Sun, Qian, & Lehman, Brad. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint. United States.
Cheung, WanYin, Zhang, Jie, Florita, Anthony, Hodge, Bri-Mathias, Lu, Siyuan, Hamann, Hendrik F., Sun, Qian, and Lehman, Brad. 2015. "Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint". United States. https://www.osti.gov/servlets/purl/1233280.
@article{osti_1233280,
title = {Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint},
author = {Cheung, WanYin and Zhang, Jie and Florita, Anthony and Hodge, Bri-Mathias and Lu, Siyuan and Hamann, Hendrik F. and Sun, Qian and Lehman, Brad},
abstractNote = {Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.},
doi = {},
url = {https://www.osti.gov/biblio/1233280}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Dec 08 00:00:00 EST 2015},
month = {Tue Dec 08 00:00:00 EST 2015}
}

Conference:
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