A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting

Zhang, Xinmin; Li, Yuan; Lu, Siyuan; Hamann, Hendrik; Hodge, Bri-Mathias Scott; Lehman, Brad

doi:10.1109/TSTE.2018.2832634

Title: A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting

Abstract

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. In conclusion, the normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.

Authors:

Zhang, Xinmin ^[1]; Li, Yuan ^[2]; Lu, Siyuan ^[3]; Hamann, Hendrik ^[3]; Hodge, Bri-Mathias Scott ^[4]; Lehman, Brad ^[5]

Northeastern Univ., Malden, MA (United States)
Sichuan Univ., Chengdu (China)
IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Northeastern Univ., Boston, MA (United States)

Publication Date:: Thu May 03 00:00:00 EDT 2018

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

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

OSTI Identifier:: 1439549

Report Number(s):: NREL/JA-5D00-71656
Journal ID: ISSN 1949-3029

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Sustainable Energy

Additional Journal Information:: Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 1949-3029

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY; solar power forecasting; photovoltaic systems; analog; ensemble; modeling

Citation Formats


                    Zhang, Xinmin, Li, Yuan, Lu, Siyuan, Hamann, Hendrik, Hodge, Bri-Mathias Scott, and Lehman, Brad. A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting.  United States: N. p., 2018. 
Web.  doi:10.1109/TSTE.2018.2832634.

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                    Zhang, Xinmin, Li, Yuan, Lu, Siyuan, Hamann, Hendrik, Hodge, Bri-Mathias Scott, & Lehman, Brad. A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting.  United States.  https://doi.org/10.1109/TSTE.2018.2832634

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                    Zhang, Xinmin, Li, Yuan, Lu, Siyuan, Hamann, Hendrik, Hodge, Bri-Mathias Scott, and Lehman, Brad. Thu .  
"A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting".  United States.  https://doi.org/10.1109/TSTE.2018.2832634.  https://www.osti.gov/servlets/purl/1439549.

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@article{osti_1439549,

  title        = {A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting},

  author       = {Zhang, Xinmin and Li, Yuan and Lu, Siyuan and Hamann, Hendrik and Hodge, Bri-Mathias Scott and Lehman, Brad},

  abstractNote = {This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. In conclusion, the normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.},

  doi          = {10.1109/TSTE.2018.2832634},

  journal      = {IEEE Transactions on Sustainable Energy},

  number       = 1,

  volume       = 10,

  place        = {United States},

  year         = {Thu May 03 00:00:00 EDT 2018},

  month        = {Thu May 03 00:00:00 EDT 2018}

}

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Journal Article:

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https://doi.org/10.1109/TSTE.2018.2832634

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Cited by: 90 works

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Figures / Tables:

Fig. 1: Overview of solar-time based analog ensemble PV power forecasting method applied to SEMA regional forecast.

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Works referencing / citing this record:

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A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation
journal, January 2020

Lotfi, Mohamed; Javadi, Mohammad; Osório, Gerardo J.
Energies, Vol. 13, Issue 1
DOI: 10.3390/en13010216

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