Uso de mtodos Ensemble para la prediccin de

Uso de métodos Ensemble para la predicción de la radiación solar University of. Rodríguez, Intelligent G. Montero, F. Díaz, H. Institute Montero, E. J. M. Systems Escobar, and R. Montenegro Numerical Applications in Engineering CMN - 2015 June 29 – July 2, 2015, Lisbon, Portugal MINECO PROGRAMA ESTATAL DE I+D+I ORIENTADA A LOS RETOS DE LA SOCIEDAD Project: CTM 2014 -55014 -C 3 -1 -R http: //www. siani. es/

Solar radiation prediction Contents The Wind 1. Introduction 2. Adaptive mesh of the terrain surface Field Model 3. Detection of shadows 4. Solar radiation modelling -Solar radiation equations for clear sky Beam radiation Diffuse radiation Reflected radiation -Solar radiation under overcast sky (real sky) Connection with HARMONIE Ensemble methods 5. Numerical experiments 6. Conclusions and future research

Solar radiation prediction Introduction The Wind Field Model Topography Albedo Shadows • Beam Radiation • Diffuse Radiation • Reflected Radiation • Global Radiation Clear Sky Experimental Data Prediction Mesoscale Data Real sky

Solar radiation prediction Construction of the terrain surface mesh • Build a sequence of nested meshes from a regular The Wind triangulation Field Modelof the rectangular region, such that the level j is obtained by a global refinement of the previous level j− 1 with the 4 -T Rivara’s algorithm • The number of levels m of the sequence is determined by the degree of discretization of the terrain • Define a new sequence until level m’ ≤ m applying a derefinement algorithm • Two derefinement parameters εh and εa are introduced and they determine the accuracy of the approximation to terrain surface and albedo, respectively

Solar radiation prediction Construction of the terrain surface mesh The Wind Field Model

Solar radiation prediction Shadow detection Accurate evaluation of radiation (Sun) (North) (East) Shadow detection

Solar radiation prediction Shadow detection Incidence angle (dexp) Incidence angle Projected shadows Potential triangle 3 Studied triangle Triangle in the shade Likely sunlit triangle Sunlit warning point Warning point in the shade Own shadows 0 if (|δexp| ≥ (π/2)) 1 if ((π/2) > |δexp| ≥ 0) Lfss= Sun

Solar radiation prediction Solar radiation equations for clear sky Solar radiation types Beam Diffuse Reflected

Solar radiation prediction Solar radiation equations for clear sky Solar constant Beam radiation The Wind Field Model Extraterrestrial irradiance G 0 normal to the solar beam Correction factor Beam irradiance normal to the solar beam Gb 0 c = G 0 exp{− 0, 8662 TLKmδR(m)} Beam irradiance on a horizontal surface Gbc(b) = Gb 0 c Lf Kd = Gb 0 c. Lf cos(δexp) Linke atmospheric turbidity factor Relative optical air mass h 0: Solar altitude angle Lf: Lighting factor Beam irradiance on an inclined surface Gbc(0) = Gb 0 c Lf Kd = Gb 0 c Lf sen(h 0) δexp: Incidence solar angle

Solar radiation prediction Solar radiation equations for clear sky Diffuse radiation Diffuse transmission Diffuse radiation on horizontal surfaces Diffuse radiation on inclined surfaces Sunlit surfaces Function depending on the solar altitude ho ≥ 0. 1 ho < 0. 1 Shadowed surfaces

Solar radiation prediction Solar radiation equations for clear sky Reflected radiation Mean ground albedo

Solar radiation prediction Solar radiation under overcast sky The values of global irradiation on a horizontal surface for overcast conditions H(0) are calculated as a correction of those of clear sky Hc(0) with the clear sky index kc The Wind Field Model If some measures of global radiation Hs are available at different measurement stations, the value of the clear sky index at those points may be computed as Then kc may be interpolated in the whole studied zone

Solar radiation prediction Connecting to a prognostic mesoscale model

Solar radiation prediction Numerical experiments The studied case corresponds to Gran Canaria, Canary Islands, in the Atlantic Ocean at 28. 06 lat, − 15. 25 long. The selected episode includes the period from June 1 st, 2015, until June 15 th, 2015 The Wind Field Model Elevation map of Gran Canaria Albedo map of Gran Canaria

Solar radiation prediction Ensemble methods q q q Non-hydrostatic meteorological model based on HIRLAM From large scale to 1 km or less scale (under development) Different models in different scales Assimilation data system Run by AEMET daily HARMONIE on Canary islands (http: //www. aemet. es/ca/idi/prediccion_numerica)

Solar radiation prediction Ensemble methods Terrain approximation Ma eight Max h 25 m ht 9 x heig 1950 m ONIE odel rain M r e T l a igit D from y h p gra Topo Terrain elevation (m) HARM n terrai f o n o tizati e r c s i d

Solar radiation prediction Ensemble methods Spatial discretization l ationa t u p m o mesh del c ive Mo Adapt Terrain elevation (m) h IE mes N O M HAR. 5 km Δh ~ 2

Solar radiation prediction Ensemble methods HARMONIE data points Used data (1/2 of total) 100 different configurations

Solar radiation prediction Ensemble methods

Solar radiation prediction Ensemble methods Analyzed points s 6 p 0 p 1 s 1 h 3 p 2 s 4 Station point h 4 Harmonie point p 3 Control point s 5 h 2 p 4 s 3 s 0 p 5 h 1 p 6

Solar radiation prediction Ensemble methods

Solar radiation modeling Conclusions and Future research Conclusions Adaptive solar model • Refined meshes allow to improve the results from HARMONIE. • The shadow procedure correct the overestimation of HARMONIE. Ensemble methods • Ensemble methods provides a valid range for solar radiation prediction. Future research Clear Sky Index • Obtain the clear sky index from the cloudiness information of HARMONIE. • Further research on the interpolation procedure used to compute the clear sky index. Parameters • Estimate some unknown parameters of the model using GAs or ANN.