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Research Papers

Forecasting of Global Horizontal Irradiance Using Sky Cover Indices

[+] Author and Article Information
Ricardo Marquez

e-mail: rmarquez3@ucmerced.edu

Vesselin G. Gueorguiev

e-mail: vgueorguiev@ucmerced.edu
Mechanical Engineering and
Applied Mechanics (MEAM),
School of Engineering,
University of California,
Merced, CA 95343

Carlos F. M. Coimbra

Mem. ASME
Mechanical and Aerospace Engineering (MAE),
Jacobs School of Engineering,
University of California,
San Diego, CA 92093
e-mail: ccoimbra@ucsd.edu

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received April 26, 2011; final manuscript received December 23, 2011; published online October 23, 2012. Assoc. Editor: Carsten Hoyer-Klick.

J. Sol. Energy Eng 135(1), 011017 (Oct 23, 2012) (5 pages) Paper No: SOL-11-1099; doi: 10.1115/1.4007497 History: Received April 26, 2011; Revised December 23, 2011

This work discusses the relevance of three sky cover (SC) indices for solar radiation modeling and forecasting. The three indices are global in the sense that they integrate relevant information from the whole sky and thus encode cloud cover information. However, the three indices also emphasize different specific meteorological processes and sky radiosity components. The three indices are derived from the observed cloud cover via total sky imager (TSI), via measurements of the infrared radiation (IR), and via pyranometer measurements of global horizontal irradiance (GHI). We enhance the correlations between these three indices by choosing optimal expressions that are benchmarked against the GHI SC index. The similarity of the three indices allows for a good qualitative approximation of the GHI irradiance when using any of the other two indices. An artificial neural network (ANN) algorithm is employed to improve the quantitative modeling of the GHI sky cover index, thus improving significantly the forecasting details of GHI when all three indices are used.

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References

Marquez, R., and Coimbra, C. F. M., 2011, “Forecasting of Global and Direct Solar Irradiance Using Stochastic Learning Methods, Ground Experiments and the NWS Database,” Sol. Energy, 85(5), pp. 746–756. [CrossRef]
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Long, C. N., Ackerman, T. P., Gaustad, K. L., and Cole, J. N. S., 2006, “Estimation of Fractional Sky Cover From Broadband Shortwave Radiometer Measurements,” J. Geophys. Res., 111(D11204), pp. 1–11. [CrossRef] [PubMed]
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Figures

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Fig. 1

Left: example of image taken by the TSI for a cloudy day. Right: processed image using manufacturer's image classification algorithms. White regions on lower and top right region indicate opaque clouds while thin clouds are represented by intermediate shading.

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Fig. 2

Scatter plots of SCGHI versus δIR. The line is a quadratic fit used to define SCIR. The parameters of the polynomial are also shown under the legend box.

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Fig. 3

Time series of the sky cover index inferred from the TSI, IR, and GHI for October and November 2010. The night, dawn, and dusk values are excluded from the graph. The instrument maintenance day (Nov. 17) was also removed from the set shown.

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Fig. 4

GHI time series measured and estimated based on SCIR and SCTSI. Night values are removed from this time-series plot.

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Fig. 5

Scatter plot for comparing GHI based on IR and TSI. The correlation coefficients (R2) for IR and TSI estimates are 0.935 and 0.923, respectively.

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Fig. 6

GHI time series for measured and forecasted values based on SCGHI, SCIR, and SCTSI. The legend shows which inputs were included in the forecasting model. Night values are not included.

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