1 Powerpoint Templates Day ahead and intra

CM2E – 4-9 May 2014
Day ahead and intra-day solar forecasting applied to an
insular site
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M. DAVID, P. LAURET, M.H. DIAGNE, P.J. TROMBE
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PIMENT, Université de La Réunion
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Martinique
Energie Environnement
4-9 May 2014
Powerpoint
Templates
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Introduction
CM2E – 4-9 May 2014
Reunion Island:
oSmall tropical Island situated in the Indian Ocean
oNon interconnected grid
oHigh penetration rate of PV (>25% of total installed power in 2013)
oPV output power = 30% of produced power at least 1 time a
month (legal limit)
oHigh solar fluctuations can affect the grid balance
oNeeds in solar forecasting
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Framework
Day ahead forecasts:
oScheduling of resources and commitment of units of production
oHourly granularity
oNWP (ECMWF) and post-processing techniques
CM2E – 4-9 May 2014
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Intra day forecasts:
oTo monitor the production and to adjust the scheduling
oHourly granularity
oTimes series models and machine learning methods
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Measured Data
oHourly Global Solar Irradiance (GHI)
o1 site -> Saint-Pierre (coastal part with high solar potential)
o2 years without missing data:
▪ Year 2012 = training
▪ Year 2013 = validation
oFiltering of data: solar zenith angle < 85°
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Saint-Pierre
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Day ahead forecasts
Initial ECMWF GHI forecast
oGenerated at 12h00 UTC (16h00 in Reunion)
oSpatial resolution: 0.125° x 0.125° (≈ 14 x 14 km)
oTemporal resolution: 1 hour
oNo post-processing
CM2E – 4-9 May 2014
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ECMWF + MOS post-processing
oApplication of MOS proposed by Lorenz et al.
oDesigned in 3 steps :
• Spatial aggregation and temporal interpolation
• Low Total Cloud Cover = Clear Sky
• Bias removal with a polynomial function of fourth order
in the clear sky index (kt*) and the cosine of the solar
zenith angle (θz)
oCalibrated for local conditions
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ECMWF + Neural Network post-processing
oNN used to model the bias of the NWP forecast
o Inputs: forecasted clear sky index (kt*) and cosine of
the solar zenith angle (θz)
o Powerpoint
complexity
controlled with Bayesian techniques
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Day ahead forecasts
Focus on the MOS post-processing
CM2E – 4-9 May 2014
oNo needs in temporal interpolation
oSpatial aggregation does not improve the forecast accuracy
oLonger is the period used for the calibration, lower is the error
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Forecast error over spatial averaging of
Day-ahead forecast error over the size of the
ECMWFPour
day-ahead
forecast
around
Saintsliding window for the calibration of the
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Pierre
polynomial function of fourth order
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Day ahead forecasts
Relative errors of day-ahead forecast
CM2E – 4-9 May 2014
(mean GHI = 498.2 W.m-2)
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Intra day forecasts
1. Single input: measured kt*
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Reference models
o Persistence
o Climatology (average kt* of year 2012)
CM2E – 4-9 May 2014
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Time series model
o ARMA recursive least square (ARMArls)
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Machine learning techniques
o Neural Network (NN) with Bayesian optimization
o Support Vector Machine (SVM)
o Gaussian Process (GP)
2. Hybrid inputs: measured and forecasted (ECMWF) kt*
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Time series model
1. Kalman filter (Kalman+ECMWF)
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Machine learning technique
o Neural Network (NN+ECMWF)
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Intra day forecasts
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CM2E – 4-9 May 2014
Intra day forecasts
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CM2E – 4-9 May 2014
Intra day forecasts
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CM2E – 4-9 May 2014
Intra day forecasts
ECMWF
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Perspectives
1. Test of the models for other sites in Reunion and for
other islands (Corsica, Guadeloupe, Martinique ?,
Hawaii)
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CM2E – 4-9 May 2014
2. Intra day forecasts: 10 minutes granularity
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3. Implementation of ECMWF forecasts in an ARMAX
Recursive Least Square model
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4. Use of exogenous data (cloud height, speed and
direction) as inputs of the learning machine
techniques
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5. Envelope of uncertainties
Acknowledgment
The authors would like to thank the European Centre for Medium-Range Weather
Forecasts (ECMWF) for providing forecast data.
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A part of the work was accomplished in the preliminary phase of the SOLFIN project
funded by ADEME.
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CM2E – 4-9 May 2014
Thank you !
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ECMWF grid
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