This work presents an alternative metric for evaluating the quality of solar forecasting models. Some conventional approaches use quantities such as the root-mean-square-error (RMSE) and/or correlation coefficients to evaluate model quality. The direct use of statistical quantities to assign forecasting quality can be misleading because these metrics do not convey a measure of the variability of the time-series for the solar irradiance data. In contrast, the quality metric proposed here, which is defined as the ratio of solar uncertainty to solar variability, compares the forecasting error with the solar variability directly. By making the forecasting error to variability comparisons for different time windows, we show that this ratio is essentially a statistical invariant for each forecast model employed, i.e., the ratio is preserved for widely different time horizons when the same time averaging periods are used, and therefore provides a robust way to compare solar forecasting skills. We employ the proposed metric to evaluate two new forecasting models proposed here, and compare their performances with a persistence model.

References

References
1.
Lew
,
D.
, and
Piwko
,
R.
,
2010
, “
Western Wind and Solar Integration Study
,” National Renewable Energy Laboratories, Technical Report No. NREL/SR-550-47781.
2.
California Independent System Operator (CAISO),
2010
, “
Integration of Renewable Resources: Operational Requirements and Generation Fleet Capability at 20 Percent RPS
,” available online at http://www.caiso.com/2804/2804d036401f0.pdf.
3.
Rodriguez
,
G. D.
,
2010
, “
A Utility Perspective of the Role of Energy Storage in the Smart Grid
,” Power and Energy Society General Meeting,
IEEE
, Minneapolis, MN, July 25–29, pp.
1
2
.10.1109/PES.2010.5589870
4.
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
.10.1016/j.solener.2011.01.007
5.
Pedro
,
H. T. C.
, and
Coimbra
,
C. F. M.
,
2012
, “
Assessment of Forecasting Techniques for Solar Power Output With No Exogenous Inputs
,”
Sol. Energy
,
86
, pp.
2017
2028
.10.1016/j.solener.2012.04.004
6.
Perez
,
R.
,
Kivalov
,
S.
,
Schlemmer
,
J.
,
Hemker
,
K.
,
Renne
,
D.
, and
Hoff
,
T. E.
,
2010
, “
Validation of Short and Medium Term Operational Solar Radiation Forecasts in the US
,”
Sol. Energy
,
84
(
5
), pp.
2161
2172
.10.1016/j.solener.2010.08.014
7.
Cao
,
J.
, and
Lin
,
X.
,
2008
, “
Study of Hourly and Daily Solar Irradiation Forecast Using Diagonal Recurrent Wavelet Neural Networks
,”
Energy Convers. Manag.
,
49
(
6
), pp.
1396
1406
.10.1016/j.enconman.2007.12.030
8.
Mellit
,
A
.,
2008
, “
Artificial Intelligence Technique for Modelling and Forecasting of Solar Radiation Data: A Review
,”
Int. J. Artif. Intell. Soft Comput.
,
1
, pp.
52
76
.10.1504/IJAISC.2008.021264
9.
Martin
,
L.
,
Zarzalejo
,
L. F.
,
Polo
,
J.
,
Navarro
,
A.
,
Marchante
,
R.
, and
Cony
,
M.
,
2010
, “
Prediction of Global Solar Irradiance Based on Time Series Analysis: Application to Solar Thermal Power Plants Energy Production Planning
,”
Sol. Energy
,
84
(
10
), pp.
1772
1781
.10.1016/j.solener.2010.07.002
10.
Mills
,
A.
, and
Wiser
,
R.
,
2010
, “
Implications of Wide-Area Geographic Diversity for Short-Term Variability of Solar Power
,” Lawrence Berkeley National Laboratory, Technical Report No. LBNL-3884E.
11.
Hoff
,
T. E.
, and
Perez
,
R.
,
2010
, “
Quantifying PV Power Output Variability
,”
Sol. Energy
,
84
(
10
), pp.
1782
1793
.10.1016/j.solener.2010.07.003
12.
Ineichen
,
P
.,
2006
, “
Comparison of Eight Clear Sky Broadband Models Against 16 Independent Data Banks
,”
Sol. Energy
,
80
, pp.
468
478
.10.1016/j.solener.2005.04.018
13.
Zarzalejo
,
L. F.
,
Polo
,
J.
, and
Ramirez
,
L.
,
2004
, “
Gc_model5_irradiance
,”
(Matlab computer program) CD-ROM accompanying
Ref. [14].
14.
Badescu
,
V.
,
2008
,
Modeling Solar Radiation at the Earth Surface
,
Springer-Verlag
,
Berlin/Heidelberg
.
15.
Hoff
,
T. E.
, and
Perez
,
R.
,
2011
, “
Modeling PV Fleet Output Variability
,”
Sol. Energy
,
86
(
8
), pp.
2177
2189
.10.1016/j.solener.2011.11.005
16.
Lave
,
M.
, and
Kleissl
,
J.
,
2010
, “
Solar Variability of Four Sites Across the State of Colorado
,”
Renewable Energy
,
35
(
12
), pp.
2867
2873
.10.1016/j.renene.2010.05.013
17.
Bishop
,
C.
,
1995
,
Neural Networks for Pattern Recognition
,
Oxford University, Great Clarendon Street
,
Oxford
, UK.
18.
Marquez
,
R.
,
Gueorguiev
,
V. G.
, and
Coimbra
,
C. F. M.
,
2012
, “
Forecasting Solar Irradiance Using Sky Cover Indices
,”
ASME J. Sol. Energy Eng.
(in press).
19.
Lorenz
,
E.
,
Hurka
,
J.
,
Heinemann
,
D.
, and
Beyer
,
H. G.
,
2009
, “
Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems
,”
IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
,
2
(
1
), pp.
2
10
.10.1109/JSTARS.2009.2020300
20.
Lave
,
M.
,
Kleissl
,
J.
, and
Arias-Castro
,
E.
,
2012
, “
High-Frequency Irradiance Fluctuations and Geographic Smoothing
,”
Sol. Energy
,
86
(
8
), pp.
2190
2199
.10.1016/j.solener.2011.06.031
21.
Marcos
,
J.
,
Marroyo
,
L.
,
Lorenzo
,
E.
,
Alvira
,
D.
, and
Izco
,
E.
,
2011
, “
Power Output Fluctuations in Large Scale PV Plants: One Year Observations With One Second Resolution and a Derived Analytic Model
,”
Prog. Photovoltaics
,
19
(
2
), pp.
218
227
.10.1002/pip.1016
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