Online short-term solar power forecasting

Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

doi:10.1016/J.SOLENER.2009.05.016

Title: Online short-term solar power forecasting

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Abstract

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model. (author)

Authors:

Bacher, Peder; Madsen, Henrik ^[1]; Nielsen, Henrik Aalborg ^[2]

Informatics and Mathematical Modelling, Richard Pedersens Plads, Technical University of Denmark, Building 321, DK-2800 Lyngby (Denmark)
ENFOR A/S, Lyngsoe Alle 3, DK-2970 Hoersholm (Denmark)

Publication Date:: Thu Oct 15 00:00:00 EDT 2009

OSTI Identifier:: 21227411

Resource Type:: Journal Article

Journal Name:: Solar Energy

Additional Journal Information:: Journal Volume: 83; Journal Issue: 10; Other Information: Elsevier Ltd. All rights reserved; Journal ID: ISSN 0038-092X

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY; FORECASTING; LEAST SQUARE FIT; PHOTOVOLTAIC POWER SUPPLIES; DENMARK; WEATHER; POWER GENERATION; ERRORS; HOURLY VARIATIONS; REGRESSION ANALYSIS; TIME-SERIES ANALYSIS; Numerical weather predictions; Clear sky model; Quantile regression; Recursive least squares

Citation Formats


                    Bacher, Peder, Madsen, Henrik, and Nielsen, Henrik Aalborg. Online short-term solar power forecasting.  United States: N. p., 2009. 
        Web.  doi:10.1016/J.SOLENER.2009.05.016.

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                    Bacher, Peder, Madsen, Henrik, & Nielsen, Henrik Aalborg. Online short-term solar power forecasting.  United States.  https://doi.org/10.1016/J.SOLENER.2009.05.016

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                    Bacher, Peder, Madsen, Henrik, and Nielsen, Henrik Aalborg. 2009.  
        "Online short-term solar power forecasting".  United States.  https://doi.org/10.1016/J.SOLENER.2009.05.016.

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@article{osti_21227411,

  title        = {Online short-term solar power forecasting},

  author       = {Bacher, Peder and Madsen, Henrik and Nielsen, Henrik Aalborg},

  abstractNote = {This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model. (author)},

  doi          = {10.1016/J.SOLENER.2009.05.016},

  url          = {https://www.osti.gov/biblio/21227411},
  journal      = {Solar Energy},

  issn         = {0038-092X},

  number       = 10,

  volume       = 83,

  place        = {United States},

  year         = {Thu Oct 15 00:00:00 EDT 2009},

  month        = {Thu Oct 15 00:00:00 EDT 2009}

}

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