Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

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Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The results ... continued below

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10 p.

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Zhang, J.; Hodge, B. M.; Florita, A.; Lu, S.; Hamann, H. F. & Banunarayanan, V. October 1, 2013.

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Description

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The results show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.

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10 p.

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  • To be presented at 3rd International Workshop on Integration of Solar Power into Power Systems, 21-22 October 2013, London, England

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  • Report No.: NREL/CP-5000-60142
  • Grant Number: AC36-08GO28308
  • Office of Scientific & Technical Information Report Number: 1114086
  • Archival Resource Key: ark:/67531/metadc865403

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  • October 1, 2013

Added to The UNT Digital Library

  • Sept. 16, 2016, 12:32 a.m.

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  • April 4, 2017, 12:58 p.m.

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Zhang, J.; Hodge, B. M.; Florita, A.; Lu, S.; Hamann, H. F. & Banunarayanan, V. Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint, article, October 1, 2013; Golden, Colorado. (digital.library.unt.edu/ark:/67531/metadc865403/: accessed September 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.