Solar radiation forecasting with non-lineal statistical techniques and                                                    ...
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Solar radiation forecasting with non lineal statistical techniques and qualitative predictions from spanish national weather service

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Solar radiation forecasting with non lineal statistical techniques and qualitative predictions from spanish national weather service

  1. 1. Solar radiation forecasting with non-lineal statistical techniques and 20 08 UN 008 qualitative predictions from Spanish National Weather Service OS r 2 EUR be to BON Oc IS Martín L., Zarzalejo L.F., Polo J., Navarro A., Marchante R. L1. INTRODUCTION 3. RESULTS Solar energy feed-in tariff is regulated by (RD 436/2004, Errors of the models essayed are measured in terms of 661/2007) in Spain. Predictions must be given for next 72 mean root mean squared deviation (RMSD). The best hours and deviations are strongly penalized. A new method NN(z) model is compared to persistence model (PER) in to predict half daily values of solar radiation is presented. terms of improvement of RMDS.2. METHODOLOGY 1 N 45.0 ° N ∑ ( xi − xi ) 2 RMSD = ˆ 42.5 ° NSolar radiation is transformed to a new gaussian and N i=1 40.0 ° N • Madrid RRN AEMetstationary variable. “Lost component” (LC) is the difference  i − ierrorm  37.5 ° Nbetwen extratrrestrial and ground measured solar radiation. improvement =  1 − ÷  i − ierror ÷ 35.0 ° N ° 15.0 ° W12 ° ° 0.0 E .5 W10.0 ° W ° E 5.0 ° E 7.5 E 1  p  7.5 ° W 5.0 ° W 2.5 ° W 0.0 ° 2.56000 385000 Lost Component L 36 NN(1)4000 C NN(2) 34 Halfday) NN(3) P NN(4)3000 R NN(5) 32 2 E NN(6)2000 D NN(7) % RM SE Prediction G (W /m I 30 NN(8) C NN(9) T NN(10)1000 28 P ersistence I O 0 0 N 26 100 200 300 400 500 600 700 S Half Day 24Synoptic predictions of sky conditions (SYN) are used as 22 1 2 3 4 5 6input to the neual network to test the improvement of the 40 Pre diction horizon (Halfdaily )predictions. AEMET offers this predicitons in its web page W Ifor each location of Spain and 7 days in adavance. T 35 H NN(1) Halfday) NN(2) 30 NN(3) S NN(4) Y 2 NN(5) %RMSE Prediction G (W/m N 25 NN(6) NN(7) C 20 NN(8) NN(9) O NN(10) N Persistence D 15 I T 10 I O N 5 S 1 2 3 4 5 6 Prediction horizon (Halfdaily) 4. CONCLUSIONS 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 improvesNeural Network (NN) is used to predict future values from considerably the prediction. The error has a lower levelobservations. NN(z) índica el tamaño del vector patrón de of nine percent which is the best prediction error that canentrada empleado z=1…10. be achieved with the methology presented. División de Energías Renovables (Departamento de Energía), CIEMAT, Av. Complutense nº22, Madrid, 28040, (Madrid) España, +34 913466048, luis.martin@ciemat.es

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