A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique

Kumler, Andrew; Xie, Yu; Zhang, Yingchen

doi:10.1016/j.solener.2018.11.046

Title: A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique

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Abstract

Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Here, we introduce a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique is used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction are predicted for future time steps using a two-stream approximation and a 5-min exponential weighted moving average, respectively. Our model evaluation using the long-term observations of GHI at NREL's Solar Radiation Research Laboratory (SRRL) shows that the PSPI has a better performance than the persistence and smart persistence models in all forecast time horizons between 5 and 60 min, which is more significant in cloudy-sky conditions. Finally, compared tomore »« less

Authors:

^[1]; Xie, Yu ^[1];

^[1]

National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Publication Date:: Sat Nov 24 00:00:00 EST 2018

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Electricity (OE)

OSTI Identifier:: 1485563

Alternate Identifier(s):: OSTI ID: 1636130

Report Number(s):: NREL/JA-5D00-72407
Journal ID: ISSN 0038-092X

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: Solar Energy

Additional Journal Information:: Journal Volume: 177; Journal Issue: C; Journal ID: ISSN 0038-092X

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY; 47 OTHER INSTRUMENTATION; PSPI; global horizontal irradiance; GHI; solar radiation; solar zenith angle

Citation Formats


                    Kumler, Andrew, Xie, Yu, and Zhang, Yingchen. A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique.  United States: N. p., 2018. 
Web.  doi:10.1016/j.solener.2018.11.046.

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                    Kumler, Andrew, Xie, Yu, & Zhang, Yingchen. A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique.  United States.  https://doi.org/10.1016/j.solener.2018.11.046

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                    Kumler, Andrew, Xie, Yu, and Zhang, Yingchen. Sat .  
"A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique".  United States.  https://doi.org/10.1016/j.solener.2018.11.046.  https://www.osti.gov/servlets/purl/1485563.

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@article{osti_1485563,

  title        = {A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique},

  author       = {Kumler, Andrew and Xie, Yu and Zhang, Yingchen},

  abstractNote = {Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Here, we introduce a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique is used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction are predicted for future time steps using a two-stream approximation and a 5-min exponential weighted moving average, respectively. Our model evaluation using the long-term observations of GHI at NREL's Solar Radiation Research Laboratory (SRRL) shows that the PSPI has a better performance than the persistence and smart persistence models in all forecast time horizons between 5 and 60 min, which is more significant in cloudy-sky conditions. Finally, compared to the persistence and smart persistence models, the PSPI does not require additional observations of various atmospheric parameters but is customizable in that additional observations, if available, can be ingested to further improve the GHI forecast. An advanced technology of cloud forecast is also expected to improve the future performance of the PSPI.},

  doi          = {10.1016/j.solener.2018.11.046},

  journal      = {Solar Energy},

  number       = C,

  volume       = 177,

  place        = {United States},

  year         = {Sat Nov 24 00:00:00 EST 2018},

  month        = {Sat Nov 24 00:00:00 EST 2018}

}

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