Solar Energy prediction and verification using model forecasts and ground based solar measurements

Transcription

1 Solar Energy prediction and verification using model forecasts and ground based solar measurements Kazadzis S. 1*, Kosmopoulos P.G. 1, Lagouvardos K. 1, Kotroni V. 1 1 Institute for Environmental Research & Sustainable Development, National Observatory of Athens, Greece * corresponding author kazadzis@meteo.noa.gr Abstract The present study focuses on the prediction and verification of Solar Energy using model forecasts and ground based solar measurements. For this purpose we used the National Observatory of Athens (NOA) network of solar radiation measurements as well as solar radiation operational forecasts provided by MM5 model. Initially we calculated correlations in seasonal and hourly basis between the observations and the model simulated values. Thereafter we calculated the Mean Bias Error & Root mean Square Error for various Sky Conditions. As a result we verified the reliability of our forecast model. Finally we made a depiction of the total contours of the sky conditions for every value and every day of a whole year period, as well as a classification of sky conditions based on the calculated clearness index. 1 Introduction Greece is one of the EU endowed countries for electricity production from renewable sources because of its geographical position. To manage the electricity grid with high amount of solar energy will require high-quality information on every aspect of solar power generation, particularly, the solar radiation forecasting. In this work, we present a comprehensive evaluation study of the reliability of global horizontal irradiance (GHI) forecasts provided by NOA operational weather forecasting chain, based on MM5 model. The study is carried out in Greece, and uses 48 h forecasts of GHI at 2-h temporal resolution. The aim is to investigate the current performance of the model for solar yield forecasting in the study region, one of the areas with larger solar capacity in the EU. 2 Data and Methodology

2 2.1 Data The automated surface meteorological stations deployed by NOA are the Davis Wireless Vantage Pro2 Plus, manufactured by Davis Instruments (USA). As part of the NOA's automated surface meteorological station network, total solar radiation is also measured in a number of stations uniformly distributed across Greece. For the purposes of this study total solar radiation measured at 8 stations were used, namely at: Amfiklia, Arta, Chania, Drama, Florina, Kranidi, Paralia Achaias and Spata. Solar irradiance is measured with pyranometers covering a wavelength range of nm with a resolution of 1 Wm -2. The instruments have been calibrated against simulated observations with the radiative transfer model LibRadtran, and were found to have an accuracy of about 5%. GHI measurements were collected every 10 min and were integrated to match the NOA's model temporal resolution (2 hours). For the purposes of this study short wave solar radiation forecasts have been also used. These forecasts are provided by the operational weather forecasting chain based on MM5 model, that is running at NOA since 2001 (Kotroni and Lagouvardos 2004). The radiation scheme used in the operational chain is based on that proposed by Stephens (1984). This scheme is sophisticated enough to account for longwave and shortwave interactions with explicit cloud and clear air and it provides surface radiation fluxes as well as atmospheric temperatures tendencies MM5 simulations updated the radiative transfer calculations at 30-min intervals. The GHI forecasts have been extracted at 2-h intervals for the first (D1) and the second (D2) day of the forecast chain for the year For each one of the surface stations the closest to the station location model grid point has been selected while the four grid points surrounding the selected one have been also delivered for the validation. 2.2 Methodology We tried to evaluate the influence of the different solar zenith angles along the day, the influence of the different meteorological conditions along the year in the forecasting skill of the model and finally the performance of the model based on the sky conditions. The different sky conditions considered in this work are the clear sky, the scattered clouds, the broken clouds and the overcast conditions and were established based on the clearness index. Forecasts were evaluated in terms of the mean bias error (MBE) and the root mean square error (RMSE), defined in absolute terms as (Lorenz et al. 2009b):

3 where are the residuals (forecast errors), calculated as the difference between the forecasted values (x f) and the observed values (x o), and N is the total number of values. MBE quantifies the overall bias and detects if the model is producing overestimation (MBE>0) or underestimation (MBE<0). On the other hand, RMSE accounts for the spread of the error distribution. Also relative error measures (rmbe and rrmse) were computed. The calculation of the clearness index, k t, is described by the relationship k t=h/h 0 where H the global solar radiation and H 0 the extraterrestrial insulation. The clearness index is a valuable tool in the characterization (or classification) of sky conditions. For the present study the basic classes we used were: k t>0.85 for clear-sky, 0.6<k t<0.85 for scattered clouds, 0.3<k t<0.6 for broken clouds and k t<0.3 for overcast conditions. 3 Results 3.1 GHI forecasts evaluation results: dependence on season and time of the day Figure 1 (left) presents the relative RMSE of the eight ground stations as a function of the season of the year. As expected, the model forecast RMSE values show a clear seasonal dependence over the study region. Fig 1. (left) Relative RMSE values of the model GHI forecasts in comparison to ground measured values. Values are the averaged relative RMSE values of all ground stations and are displayed for the different season of the year. (right) Values are the averaged relative RMSE values of all ground stations and are displayed for the different time horizon (forecast D1 and D2) and time (in 2-hour base) of the day. Particularly, the lowest values are found in summer and the highest values in winter. The highest errors occur at Amfiklia station except the winter season and

4 the lowest values occur at Chania and Kranidi stations. The most homogeneous values are found in winter (smaller scatter of values), while the largest scatter of values is found in spring. Figure 1 (right) shows the relative RMSE as a function of the time of the day. As expected, the model forecast RMSE values show a clear time dependence with the highest values after 12:00 UTC (local noon). Figure 2 shows the temporal reliability of the MM5 model near local noon. The forecasts accuracy tends to increase at summer values. Fig 2. Temporal reliability at 10:00 UTC of the MM5 model. Values are the percentage trends of the observed measurements and the model for D1 and D2. Table 1. GHI forecast evaluation results as a function of the season and time of the day for two ground stations. The model MBE and RMSE statistical scores are showed in absolute (W/m 2 ) and relative magnitude (in brackets at the right, in percentage). Analysis Seasonal Time (UTC) Forecast horizon Kranidi Amfiklia MBE RMSE MBE RMSE D1 Winter 21.8 (8.2) (73.5) 8.4 (3.4) (75.6) Spring 67.7 (14.9) (45.8) (40.5) (74.7) Summer 52.8 (9.6) 84.7 (15.3) 96.6 (19.5) (37.1) Autumn 6.6 (1.7) (42.4) 10.1 (3.0) (61.4) Annual period 40.5 (9.4) (38.6) 73.1 (19.7) (57.3) D2 Winter 46.8 (17.6) (69.5) 24.1 (9.7) (75.4) Spring 61.8 (13.6) (44.6) (41.2) (76.1) Summer 51.7 (9.4) 89,1.0 (16.1) 88.1 (17.8) (39.6) Autumn -2.0 (-0.5) (43.9) -0.9 (-0.3) (65.7) Annual period 41.2 (9.6) (38.1) 71.5 (19.2) (59.5) D (-14.1) 90.2 (32.7) (-11.5) (48.0) (4.2) (33.4) 35.4 (8.3) (44.1) (11.5) (34.1) 98.6 (17.6) (51.1) (15.3) (38.2) (24.4) (54.3) (16.2) (37.4) 99.3 (32.3) (61.4) D (-14.2) 90.4 (32.8) (-11.6) (48.1) (4.3) (33.3) 43.1 (10.2) (46.8) (11.6) (32.4) 91.9 (16.4) (53.7) (14.6) (38.0) 121 (24.3) (56.8) (16.9) (38.7) 86.4 (18.1) (60.4)

5 Table 1 summarizes the performance of the model GHI forecasts as a function of the season and the time of the day for the locations of Kranidi and Amfiklia. Positive values of the MBE indicate that model tends to overestimate the GHI. For the seasonal analysis MBE values tend to be higher in spring and lower in winter and autumn. RMSE values show a steady inter-annual variability with highest values in winter and lowest in summer. As expected, the forecasts accuracy tends to decrease with the forecast lead time. For the time-of-the-day dependent analysis negative values of MBE are found at 6:00 UTC, indicating that the model tends to underestimate GHI. Positive values are found at 12:00 UTC, indicating that at this time the model tends to overestimate GHI. RMSE highest values occur at 12:00 UTC while relative RMSE highest values occur at 14:00 UTC and lowest values occur at about 8:00 UTC. 3.2 GHI forecasts evaluation results: dependence on the sky conditions Figure 3 shows the relative RMSE values of all stations as a function of the classified clearness index k t. The forecast accuracy for the whole region shows a marked dependence on the sky conditions. For the annual period, values range from below 30% for clear sky, about 55% for scattered clouds, from 125 to 150% for broken clouds and up to more than 250% for overcast conditions. Similar results were provided by Lara-Fanego et al. (2012) in an evaluation study of the GHI forecasts for southern Spain. Fig 3. Relative RMSE values of the model GHI forecasts in comparison to ground measured values. Values are the results for all ground stations and are displayed as a function of the clearness index (classified to different sky conditions). Figure 4 shows the analytical contour of clearness index (k t) as classified for the different sky conditions for Amfiklia and Kranidi ground stations. This figure in

6 fact, summarizes the climatological patterns in the study region as well as the geographic and the topographic conditions that form the regional sky conditions. Fig 4. Clearness index classified to different sky conditions (Clear sky, Cloudy and Overcast) for every value and every day of a whole year period, for (left) Amfiklia and (right) Kranidi station. 4 Conclusions In this work different analyses were carried out for the GHI forecasts. The seasonal analysis showed that MM5 model tends to overestimate GHI for all the seasons of the year except some cases in autumn season (Chania). The relative RMSE also showed a clear seasonal dependence with values ranging from about 20% during summer to over 50% for the rest of the seasons. The time-of-the-day analysis showed a marked dependence of the forecasting error on the time of the day. The sky-conditions-dependent analysis showed a clearly dependence of the forecasting error on the sky conditions. We found differences between sky conditions varying by a relative factor of more than 10. Nevertheless, it should be noted that the GHI forecasts depend not only on the radiation scheme implemented in the numerical weather prediction model but are highly dependent on the model ability to correctly reproduce the spatial and temporal distribution of clouds. Thus in cases of low predictability of cloud/precipitation conditions the GHI forecasts might present larger errors. References Kotroni V and Lagouvardos K (2004). Evaluation of MM5 high resolution real-time forecasts over the urban area of Athens, Greece. Journal of Applied Meteorology 43, Lara-Fanego V, Ruiz-Arias JA, Pozo-Vazquez D, Santos-Alamillos FJ, Tovar-Pescador J (2012). Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain). Solar Energy 86, Lorenz E, Hurka J, Heinemann D, Beyer HG (2009b). Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J. Selected Topics Appl. Earth Observations Remote Sens. 2(1) Stephens GL (1984). Review: The parameterization of radiation for numerical weather prediction and climate models. Mon. Wea. Rev. 112,