A direct methodology for intra-day forecasts (1–6 h ahead) of power output (PO) from photovoltaic (PV) solar plants is proposed. The forecasting methodology uses publicly available images from geosynchronous satellites to predict PO directly without resorting to intermediate irradiance (resource) forecasting. Forecasts are evaluated using four years (January 2012–December 2015) of hourly PO data from 2 nontracking, 1 MWp PV plants in California. For both sites, the proposed methodology achieves forecasting skills ranging from 24% to 69% relative to reference persistence model results, with root-mean-square error (RMSE) values ranging from 90 to 136 kW across the studied horizons. Additionally, we consider the performance of the proposed methodology when applied to imagery from the next generation of geosynchronous satellites, e.g., Himawari-8 and geostationary operational environmental satellite (GOES-R).

References

1.
Quesada-Ruiz
,
S.
,
Chu
,
Y.
,
Tovar-Pescador
,
J.
,
Pedro
,
H. T. C.
, and
Coimbra
,
C. F. M.
,
2014
, “
Cloud-Tracking Methodology for Intra-Hour DNI Forecasting
,”
Sol. Energy
,
102
, pp.
267
275
.
2.
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
.
3.
Marquez
,
R.
,
Gueorguiev
,
V.
, and
Coimbra
,
C. F. M.
,
2012
, “
Forecasting of Global Horizontal Irradiance Using Sky Cover Indices
,”
ASME J. Sol. Energy Eng.
,
135
(1), p.
011017
.
4.
Saade
,
E.
,
Clough
,
D. E.
, and
Weimer
,
A. W.
,
2013
, “
Use of Image-Based Direct Normal Irradiance Forecasts in the Model Predictive Control of a Solar-Thermal Reactor
,”
ASME J. Sol. Energy Eng.
,
136
(
1
), p.
010905
.
5.
Perez
,
R.
,
Lorenz
,
E.
,
Pelland
,
S.
,
Beauharnois
,
M.
,
Van Knowe
,
G.
,
Hemker
,
K. J.
,
Heinemann
,
D.
,
Remund
,
J.
,
Muller
,
S. C.
,
Traunmuller
,
W.
,
Steinmauer
,
G.
,
Pozo
,
D.
,
Ruiz-Arias
,
J. A.
,
Lara-Fanego
,
V.
,
Ramirez-Santigosa
,
L.
,
Gaston-Romero
,
M.
, and
Pomares
,
L. M.
,
2013
, “
Comparison of Numerical Weather Prediction Solar Irradiance Forecasts in the US, Canada and Europe
,”
Sol. Energy
,
94
, pp.
305
326
.
6.
Pelland
,
S.
,
Galanis
,
G.
, and
Kallos
,
G.
,
2013
, “
Solar and Photovoltaic Forecasting Through Post-Processing of the Global Environmental Multiscale Numerical Weather Prediction Model
,”
Prog. Photovolt.: Res. Appl.
,
21
(
3
), pp.
284
296
.
7.
Larson
,
D. P.
,
Nonnenmacher
,
L.
, and
Coimbra
,
C. F. M.
,
2016
, “
Day-Ahead Forecasting of Solar Power Output From Photovoltaic Plants in the American Southwest
,”
Renewable Energy
,
91
, pp.
11
20
.
8.
Pierro
,
M.
,
Bucci
,
F.
,
De Felice
,
M.
,
Maggioni
,
E.
,
Perroto
,
A.
,
Spada
,
F.
,
Moser
,
D.
, and
Cornaro
,
C.
,
2016
, “
Deterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting
,”
ASME J. Sol. Energy Eng.
,
139
(
2
), p.
021010
.
9.
Aryaputera
,
A. W.
,
Yang
,
D.
, and
Walsh
,
W. M.
,
2015
, “
Day-Ahead Solar Irradiance Forecasting in a Tropical Environment
,”
ASME J. Sol. Energy Eng.
,
137
(
5
), p.
051009
.
10.
Cornaro
,
C.
,
Bucci
,
F.
,
Del Frate
,
F.
,
Peronaci
,
S.
, and
Taravat
,
A.
,
2015
, “
Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction
,”
ASME J. Sol. Energy Eng.
,
137
(
3
), p.
031011
.
11.
Perez
,
R.
,
Kivalov
,
S.
,
Schlemmer
,
J.
,
Hemker
,
K.
, Jr.
,
Renné
,
D.
, and
Hoff
,
T. E.
,
2010
, “
Validation of Short and Medium Term Operation Solar Radiation Forecasts in the US
,”
Sol. Energy
,
84
(12), pp.
2161
2172
.
12.
Marquez
,
R.
,
Pedro
,
H. T. C.
, and
Coimbra
,
C. F. M.
,
2013
, “
Hybrid Solar Forecasting Method Uses Satellite Imaging and Ground Telemetry as Inputs to ANNs
,”
Sol. Energy
,
92
, pp.
176
188
.
13.
Inman
,
R. H.
,
Pedro
,
H. T. C.
, and
Coimbra
,
C. F. M.
,
2013
, “
Solar Forecasting Methods for Renewable Energy Integration
,”
Prog. Energy Combust. Sci.
, 39(6), pp.
535
576
.
14.
Law
,
E. W.
,
Prasad
,
A. A.
,
Kay
,
M.
, and
Taylor
,
R. A.
,
2014
, “
Direct Normal Irradiance Forecasting and Its Application to Concentrated Solar Thermal Output Forecasting—A Review
,”
Sol. Energy
,
108
, pp.
287
307
.
15.
Antonanzas
,
J.
,
Osorio
,
N.
,
Urraca
,
R.
,
de Pison
,
F. J. M.
, and
Antonanzas-Torres
,
F.
,
2016
, “
Review of Photovoltaic Power Forecasting
,”
Sol. Energy
,
136
, pp.
78
111
.
16.
Nonnenmacher
,
L.
, and
Coimbra
,
C. F. M.
,
2014
, “
Streamline-Based Method for Intra-Day Solar Forecasting Through Remote Sensing
,”
Sol. Energy
,
108
, pp.
447
459
.
17.
Chu
,
Y.
,
Pedro
,
H. T. C.
, and
Coimbra
,
C. F. M.
,
2013
, “
Hybrid Intra-Hour DNI Forecasts With Sky Image Processing Enhanced by Stochastic Learning
,”
Sol. Energy
,
98
(Pt. C), pp.
592
603
.
18.
Chu
,
Y.
,
Li
,
M.
,
Pedro
,
H. T. C.
, and
Coimbra
,
C. F. M.
,
2015
, “
Real-Time Prediction Intervals for Intra-Hour DNI Forecasts
,”
Renewable Energy
,
83
, pp.
234
244
.
19.
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
(7), pp.
2017
2028
.
20.
Zamo
,
M.
,
Mestre
,
O.
,
Arbogast
,
P.
, and
Pannekoucke
,
O.
,
2014
, “
A Benchmark of Statistical Regression Methods for Short-Term Forecasting of Photovoltaic Electricity Production, Part I: Deterministic Forecast of Hourly Production
,”
Sol. Energy
,
105
, pp.
792
803
.
21.
Zamo
,
M.
,
Mestre
,
O.
,
Arbogast
,
P.
, and
Pannekoucke
,
O.
,
2014
, “
A Benchmark of Statistical Regression Methods for Short-Term Forecasting of Photovoltaic Electricity Production—Part II: Probabilistic Forecast of Daily Production
,”
Sol. Energy
,
105
, pp.
804
816
.
22.
Perez
,
R.
,
Schlemmer
,
J.
,
Hemker
,
Kivalov
,
S.
,
Kankiewicz
,
A.
, and
Gueymard
,
C.
,
2015
, “
Satellite-to-Irradiance Modeling—A New Version of the SUNY Model
,” 42nd IEEE Photovoltaic Specialist Conference (
PVSC
), New Orleans, LA, June 14–19, pp. 1–7.
23.
Perez
,
R.
,
Ineichen
,
P.
,
Moore
,
K.
,
Kmiecik
,
M.
,
Chain
,
C.
,
George
,
R.
, and
Vignola
,
F.
,
2002
, “
A New Operational Model for Satellite-Derived Irradiances: Description and Validation
,”
Sol. Energy
,
73
, pp.
307
317
.
24.
Perez
,
R.
,
Ineichen
,
P.
,
Kmiecik
,
M.
,
Moore
,
K.
,
Renne
,
D.
, and
George
,
R.
,
2004
, “
Producing Satellite-Derived Irradiances in Complex Arid Terrain
,”
Sol. Energy
,
77
(4), pp.
367
371
.
25.
Vignola
,
V.
,
Harlan
,
P.
,
Perez
,
R.
, and
Kmiecik
,
M.
,
2007
, “
Analysis of Satellite Derived Beam and Global Solar Radiation Data
,”
Sol. Energy
,
81
(6), pp.
768
772
.
26.
Zagouras
,
A.
,
Pedro
,
H. T. C.
, and
Coimbra
,
C. F. M.
,
2015
, “
On the Role of Lagged Exogenous Variables and Spatial-Temporal Correlations in Improving the Accuracy of Solar Forecasting Methods
,”
Renewable Energy
,
78
, pp.
203
218
.
27.
Mozorra Aguiar
,
L.
,
Pereira
,
B.
,
David
,
M.
,
Diaz
,
F.
, and
Lauret
,
P.
,
2015
, “
Use of Satellite Data to Improve Solar Radiation Forecasting With Bayesian Artificial Neural Networks
,”
Sol. Energy
,
122
, pp.
1309
1324
.
28.
Chu
,
Y.
,
Urquhart
,
B.
,
Gohari
,
S. M. I.
,
Pedro
,
H. T. C.
,
Kleissl
,
J.
, and
Coimbra
,
C. F. M.
,
2015
, “
Short-Term Reforecasting of Power Output From a 48 MWe Solar PV Plant
,”
Sol. Energy
,
112
, pp.
68
77
.
29.
Lipperheide
,
M.
,
Bosch
,
J. L.
, and
Kleissl
,
J.
,
2015
, “
Embedded Nowcasting Method Using Cloud Speed Persistence for a Photovoltaic Power Plant
,”
Sol. Energy
,
112
, pp.
232
238
.
30.
Lonij
,
V. P.
,
Brooks
,
A. E.
,
Cronin
,
A. D.
,
Leuthold
,
M.
, and
Koch
,
K.
,
2013
, “
Intra-Hour Forecasts of Solar Power Production Using Measurements From a Network of Irradiance Sensors
,”
Sol. Energy
,
97
, pp.
58
66
.
31.
Kaur
,
A.
,
Nonnenmacher
,
L.
,
Pedro
,
H. T. C.
, and
Coimbra
,
C. F. M.
,
2016
, “
Benefits of Solar Forecasting for Energy Imbalance Markets
,”
Renewable Energy
,
86
, pp.
819
830
.
32.
Fonseca
,
J. G.
,
Oozeki
,
T.
,
Ohtake
,
H.
,
Shimose
,
K.
,
Takashima
,
T.
, and
Ogimoto
,
K.
,
2014
, “
Regional Forecasts and Smoothing Effect of Photovoltaic Power Generation in Japan: An Approach With Principal Component Analysis
,”
Renewable Energy
,
68
, pp.
403
413
.
33.
Wolff
,
B.
,
Kuhnert
,
J.
,
Lorenz
,
E.
,
Krame
,
O.
, and
Heinemann
,
D.
,
2016
, “
Comparing Support Vector Regression for PV Power Forecasting to a Physical Modeling Approach Using Measurement, Numerical Weather Prediction, and Cloud Motion Data
,”
Sol. Energy
,
135
, pp.
197
208
.
34.
Vapnik
,
V. N.
,
1998
,
Statistical Learning Theory
,
Wiley
,
New York
.
35.
Chang
,
C. C.
, and
Lin
,
C. J.
,
2002
, “
Training ν-Support Vector Regression: Theory and Algorithms
,”
Neural Comput.
,
14
(8), pp.
1959
1977
.
36.
Chang
,
C. C.
, and
Lin
,
C. J.
,
2011
, “
LIBSVM: A Library for Support Vector Machines
,”
ACM Trans. Intell. Syst. Technol.
,
2
(3), pp.
1
27
.
37.
Ineichen
,
P.
, and
Perez
,
R.
,
2002
, “
A New Airmass Independent Formulation for the Linke Turbidity Coefficient
,”
Sol. Energy
,
73
(
3
), pp.
151
157
.
38.
Ineichen
,
P.
,
2006
, “
Comparison of Eight Clear Sky Broadband Models against 16 Independent Data Banks
,”
Sol. Energy
,
80
(
4
), pp.
468
478
.
39.
Gueymard
,
C. A.
,
2012
, “
Clear-Sky Irradiance Predictions for Solar Resource Mapping and Large-Scale Applications: Improved Validation Methodology and Detailed Performance Analysis of 18 Broadband Radiative Models
,”
Sol. Energy
,
86
(
8
), pp.
2145
2169
.
40.
Marquez
,
R.
, and
Coimbra
,
C. F. M.
,
2012
, “
Proposed Metric for Evaluation of Solar Forecasting Models
,”
ASME J. Sol. Energy Eng.
,
135
(1), p.
011016
.
41.
Hintze
,
J. L.
, and
Nelson
,
R. D.
,
1998
, “
Violin Plots: A Box Plot-Density Trace Synergism
,”
Am. Stat.
,
52
(
2
), pp.
181
184
.
42.
Zagouras
,
A.
,
Inman
,
R. H.
, and
Coimbra
,
C. F. M.
,
2014
, “
On the Determination of Coherent Solar Microcolimates for Utility Planning and Operators
,”
Sol. Energy
,
102
, pp.
173
188
.
43.
Hammer
,
A.
,
Kuhnert
,
J.
,
Weinreich
,
K.
, and
Lorenz
,
E.
,
2015
, “
Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index
,”
Remote Sens.
,
7
(7), pp.
9070
9090
.
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