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Title: A New Approach for Short-Term Solar Radiation Forecasting Using the Estimation of Cloud Fraction and Cloud Albedo

Abstract

Solar generation is an increasing part of the energy portfolio in the United States. An accurate forecast of the available solar resource and power is essential to managing the electric grid, market operations, and reducing the cost of solar energy. High-frequency forecasts of solar radiation in intra-hour horizons is important for real-time electric power system energy management, especially at the distribution level. Conventional Numerical Weather Prediction models perform poorly in intra-hour, high-frequency forecasts because of the limits on real-time computing, spatial resolution, and infrequent availability of observations. Although a number of alternative technologies, e.g., time-series analysis and machine learning, have been used to fill this gap, the smart persistence model is among the top-performing models in short-term forecasting and therefore often serves as the baseline to evaluate other forecasting models. Although the smart persistence model often serves as the baseline model in these intra-hour forecasts, obvious uncertainties exist in the current smart persistence model: (1) clear-sky index does not respond to the variation of the solar incident angle when cloud conditions are persistent within the forecast horizon, and (2) cloud coverage is inherently persistent though it is constrained by cloud advection. In this study, we developed a Physics-Based Smart Persistencemore » Model for Intra-Hour Solar Forecasting (PSPI) that integrates cloudy property estimation, a radiative transfer model, and cloud fraction forecasts to improve the performance of the smart persistence model. Compared to the smart persistence model, PSPI does not require additional observations of various atmospheric parameters past global horizontal irradiance, but it is customizable because additional observations, if available, can be ingested to further improve the forecast. Our results show that the PSPI outperforms the persistence and smart persistence model on 5-minute, 15-minute, and 30-minute forecast horizons. The software package of PSPI is flexible to users' needs and provides low computational time to run at site-specific locations across the continental United States.« less

Authors:
 [1];  [1];  [1]
  1. 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), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1476449
Report Number(s):
NREL/TP-5D00-72290
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; solar; radiation; generation; forecasting; estimation; cloud fraction; cloud albedo

Citation Formats

Kumler, Andrew, Xie, Yu, and Zhang, Yingchen. A New Approach for Short-Term Solar Radiation Forecasting Using the Estimation of Cloud Fraction and Cloud Albedo. United States: N. p., 2018. Web. doi:10.2172/1476449.
Kumler, Andrew, Xie, Yu, & Zhang, Yingchen. A New Approach for Short-Term Solar Radiation Forecasting Using the Estimation of Cloud Fraction and Cloud Albedo. United States. doi:10.2172/1476449.
Kumler, Andrew, Xie, Yu, and Zhang, Yingchen. Mon . "A New Approach for Short-Term Solar Radiation Forecasting Using the Estimation of Cloud Fraction and Cloud Albedo". United States. doi:10.2172/1476449. https://www.osti.gov/servlets/purl/1476449.
@article{osti_1476449,
title = {A New Approach for Short-Term Solar Radiation Forecasting Using the Estimation of Cloud Fraction and Cloud Albedo},
author = {Kumler, Andrew and Xie, Yu and Zhang, Yingchen},
abstractNote = {Solar generation is an increasing part of the energy portfolio in the United States. An accurate forecast of the available solar resource and power is essential to managing the electric grid, market operations, and reducing the cost of solar energy. High-frequency forecasts of solar radiation in intra-hour horizons is important for real-time electric power system energy management, especially at the distribution level. Conventional Numerical Weather Prediction models perform poorly in intra-hour, high-frequency forecasts because of the limits on real-time computing, spatial resolution, and infrequent availability of observations. Although a number of alternative technologies, e.g., time-series analysis and machine learning, have been used to fill this gap, the smart persistence model is among the top-performing models in short-term forecasting and therefore often serves as the baseline to evaluate other forecasting models. Although the smart persistence model often serves as the baseline model in these intra-hour forecasts, obvious uncertainties exist in the current smart persistence model: (1) clear-sky index does not respond to the variation of the solar incident angle when cloud conditions are persistent within the forecast horizon, and (2) cloud coverage is inherently persistent though it is constrained by cloud advection. In this study, we developed a Physics-Based Smart Persistence Model for Intra-Hour Solar Forecasting (PSPI) that integrates cloudy property estimation, a radiative transfer model, and cloud fraction forecasts to improve the performance of the smart persistence model. Compared to the smart persistence model, PSPI does not require additional observations of various atmospheric parameters past global horizontal irradiance, but it is customizable because additional observations, if available, can be ingested to further improve the forecast. Our results show that the PSPI outperforms the persistence and smart persistence model on 5-minute, 15-minute, and 30-minute forecast horizons. The software package of PSPI is flexible to users' needs and provides low computational time to run at site-specific locations across the continental United States.},
doi = {10.2172/1476449},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {10}
}

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