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Solar radiation forecasting with non-lineal statistical techniques and qualitative predictions from Spanish National Weather Service
Martín L., Zarzalejo L.F., Polo J., Navarro A., Marchante R.
1. INTRODUCTIONSolar energy feed-in tariff is regulated by (RD 436/2004, 661/2007) in Spain. Predictions must be given for next 72 hours and deviations are strongly penalized. A new method to predict half daily values of solar radiation is presented.
Errors of the models essayed are measured in terms of mean root mean squared deviation (RMSD). The best NN(z) model is compared to persistence model (PER) in terms of improvement of RMDS.
EUROSUN 2008
October 2008
LISBON
The error of the first model is limited by an upper level which is due to deterministic nonlinear behaviour of the signal which can’t be followed correctly by neural network models. The second model improves considerably the prediction. The error has a lower level of nine percent which is the best prediction error that can be achieved with the methology presented.
LC
PREDICTIONS
2. METHODOLOGY
Solar radiation is transformed to a new gaussian and stationary variable. “Lost component” (LC) is the difference betwen extratrrestrial and ground measured solar radiation.
3. RESULTS
4. CONCLUSIONS
0
100 200 300 400 500 600 7000
1000
2000
3000
4000
5000
6000
Half Day
Lost Component
2
1
1ˆ
N
i ii
RMSD x xN
35.0
42.5 N
Madrid RRN AEMet
15.0 W 12.5 W 10.0 W 7.5 W 5.0 W 2.5 W 0.0 2.5 E 5.0 E 7.5 E 10.0 E
N
37.5 N
40.0 N
45.0 N
1 m
p
i ierrorimprovement
i ierror
1 2 3 4 5 622
24
26
28
30
32
34
36
38
Prediction horizon (Halfdaily)
%R
MS
E P
redi
ctio
n G
(W/m
2 Hal
fday
)
NN(1)NN(2)NN(3)NN(4)NN(5)NN(6)NN(7)NN(8)NN(9)NN(10)Persistence
1 2 3 4 5 65
10
15
20
25
30
35
40
Prediction horizon (Halfdaily)
%R
MS
E P
red
icti
on
G (
W/m
2 Hal
fday
)
NN(1)NN(2)NN(3)NN(4)NN(5)NN(6)NN(7)NN(8)NN(9)NN(10)Persistence
Neural Network (NN) is used to predict future values from observations. NN(z) índica el tamaño del vector patrón de entrada empleado z=1…10.
Synoptic predictions of sky conditions (SYN) are used as input to the neual network to test the improvement of the predictions. AEMET offers this predicitons in its web page for each location of Spain and 7 days in adavance.
WITH
SYN
CONDITIONS
División de Energías Renovables (Departamento de Energía), CIEMAT, Av. Complutense nº22, Madrid, 28040, (Madrid) España, +34 913466048, [email protected]
