Types of heat generation in Ukraine in 2016 and their cost
Январь 31st, 2016
The NWP uncertainties of the rainfall forecast is based on nonstationary, nonlinear and dynamic effects as stated by Todini [18]. The solar radiation forecast, also a function of the cloud cover, is expected to be subjected to the same underlying effects. Time series models which using the DWT are capable to interpret non stationary effects as stated in [27] and time series models using the autoregression (AR) models are capable to interpret dynamic effects [28]. Applying the AR model to the Haar and the biorthogonal DWT family, shows that the highest order biorthogonal wavelet transform of the Matlab™ tool set (bior6.8) :, has the best performance for the DWTAR MOS of the solar radiation correction as shown in [29]. Thus, it can be considered, that the DWT, which uses the highest order biorthogonal wavelet corrects best the dynamic and non stationary characteristics of statistic MOS model. Improving the correction of the non linear effects, the AR model is substituted by an ANN in the present article.
3.1 Statistical correction of the NWP model output
The idea of the proposed statistical correction method is based on the time series correction MOS as presented in [15] for local correction. The Kalman Filter is substituted by the ANNDWT (section 3.5). This method is utilized to estimate the correction of the solar radiation for the forecasted day, based on the recognized pattern of the NWP residual obtained from twelve previous days. The residual {sA} between the measured {H} and the forecasted daily mean solar radiation {HA} is estimated to correct the NWP output with this estimations {sA}. The forecasted {HA} and the measured {H} solar radiation time series are in a first step both decomposed in its subseries {HAs} and {Hs} by the DWT. For mx time scales one obtains s = 1 … (mx + 1) subsignals of residuals {sas} of the ARPS forecasts by the equation (2). For s = 1 … mx, the vectors {sas} are the subsignals which represent the details and for s = (mx + 1), the {sas} is the subsignal which represents the approximation of the ARPS residuals.
{sas} = {Has} — {Hs} (2)
The independent {sAS} subsignal vectors can also be obtained by the direct decomposition of the residual vector {sA}. Each of these time series is utilized for the training of its corresponding ANN, minimizing the squared error (eqn. 3) in a supervised learning process [30] to estimate the predictand sAs i for the day i, based on the characteristic pattern of the predictors, the sAS — values of (i1) to (ik) previous days. The symbol E stands for the energy of the error [30].
E _ 2 ^^ij (s As, i (sAs, (i1) ■■■ sAs, (ik)) — sAs, i) ^ тІП (3)
This function has a support length of thirteen sampled discrete values.
The forecast output of the NWP model is corrected with the sum of the (mx+1) estimated residual values ё а5,і.
In this work, we have only considered explanatory variables that can be derived from DEM. This makes the results more general and useful, since these variables are readily available elsewhere. Particularly, in this study we have evaluated the effect of the elevation and shadows cast as external variables in the kriging procedure. To deal with the seasonality of the solar radiation variability, the study is carried out for each month independently. The topographic characteristics were derived based on a 1 km spatial resolution DEM and the IDRISI GIS software [6] was used to carry out the regression analysis and the whole residual kriging procedure.
Regarding the elevation, it is clear that this variable is related to the atmospheric attenuation of the incoming solar radiation at the earth surface. The higher the elevation, the lower the atmospheric layer thickness and, therefore, the lower the atmospheric attenuation. Nevertheless, the importance of this explanatory variable depends on the elevation gradient. In the present study, this gradient is relative small (minimum elevation is 4 m and maximum 1212 m).
The other external explanatory variable that has been explored in this study is related to the shadow cast caused by the topography. The slope and aspect effect on the measured values can not be evaluated in this study since the stations are located on horizontal surfaces. The skyview factor influence, nevertheless, can be evaluated. Particularly, to assess this effect, a skyview factor can be used. This skyview factor can be defined as the ratio between radiation actually received by a planar surface to that received from the entire hemispheric radiating environment (without any obstruction). It represents the shadows cast by topographic features. Sky view factor can be derived from DEM using several available procedures. In the present study the skyview factor was computed using the GRASS GIS [6] environment in the following way: given a location, the maximum elevation angle of sky obstruction were computed for 36 directions around this location. Then, a continuous curve of angle values was computed using linear interpolation. The area under this continuous curve, normalized to the entire hemisphere is considered the skyview factor. Note that the value of the skyview factor varies from 1, when the whole sky is obscured, to 0, when no obstructions are present. In actual cases, skyview factor seldom are higher than 0.20.
In this study, a modified version of this skyview factor was used. Particularly, the skyview factor was computed just using the topographic information in the south direction. That is, the angles were only computed for the half of the sky view, from solar azimuth angles between 90° (eastern ) to 90° (western), instead of for the complete circumference (solar azimuth angles from 180° to 180°). We will call this new skyview factor semiskyview factor hereinafter. As for the complete circumference, the semiskyview factor may take values between 0 and 1. Actual values for all the stations used in this study ranged between 0 and 0.195. This new explanatory variable reveled to be much more effective in explaining the solar radiation variability than a complete skyview factor. The rationale behind this result is that, as it is known, in the northern hemisphere, the amount of radiation received from solar azimuth angles between below 90° and above 90° is scarce. Therefore, the topographic information associated with these angles is not relevant in explaining the solar radiation variability and just increases the noise of the explanatory variable, without adding new information. This new semiskyview factor was used as independent variable in the residual kriging procedure.
The Heliosat method is a technique of determining the global radiation at the ground by using data from a geostationary satellite.
In the Heliosat method it is assumed that the intensity of the visible solar irradiance which is scattered back to the satellite from the Earth and the atmosphere, behaves proportional to the atmospheric reflection. Also the isotropy of the atmospheric reflection is suppositional in order to disable the influence of the atmosphere’s heterogeneity on the reflection properties.
Due to the dominating dependence of the reflection on the cloudiness, it is feasible to derive an important quantity characterizing the degree of cloudiness existing within a solid angle from the radiation measured by the satellite. Via this quantity it is possible to deduce the transmission properties of the atmosphere and then determine the global irradiance.
Figure 1C shows a typical case of a clear day with the snow covering the Sierra Nevada Mountains. In these particular images, the method Heliosat2 can lead to subestimations, because the pixels are considered to be covered by clouds instead of by snow. In this work, a preprocessing method is applied to perform a detection of possible snow covers. Consisting in a comparison between the mountain pixels versus the surrounding ones, where a very low albedo in the surrounding if compared with the mountain is considered to belong to a clear day with snow covers. The method offers a success of near 85% in the detection of snow covers and it will be used. Error reduction due to this detection is discussed in the Results section.
1.2. Horizon calculation
Computational cost of the horizon calculation can be a problem when dealing with large areas; that issue has been addressed by developing an algorithm that reduces drastically the time spent in this process, without loosing much information about the actual horizon. The used DTM file contains around two million values of altitude, and the horizon for a specific point should be calculated by using all that information, but in this work most of the points are skipped and only around 70000 are utilized. The skipping step is defined as a function of the distance to the studied point, and a random contribution is also added. Finally, the horizon is only calculated for those selected points which altitude is higher than the studied point one, reducing the time consumption. Figure 3 shows the random and the selected points in the calculation of the horizon.
A comparison of the calculated horizon following the described algorithm and the real horizon (calculated by using the whole set of DTM points) appears in Figure 4, where it can be noted the little influence of the selection in the final calculation. Finally, for each day, the horizon will be overlaid with the sun path, obtaining the actual sunrise and sunset hours from the solar altitudes that will be introduced to the model.
Fig. 4 . Differences between real and used horizons. 
Daily irradiation values were calculated with and without inclusion of local effects; afterwards, the error was calculated in terms of Mean Bias Error (MBE) and Root Mean Square Error (RMSE), as a percentage of the mean measured value. Table 2 shows the obtained errors for the different stations and the three considered methods: A. Heliosat2 with no horizon and using a mean altitude for the pixel, B. Heliosat2 considering the calculated horizon and the altitude of the point and C. same as B but including the detection of clear days with snow covers.
Table 2. Obtained errors for the 14 stations and three different methods

Error reduction is detected for all the stations, and the importance of horizon effects appears to be larger than the effect of the snow covers. In fact, the consideration of snow covers is almost negligible in the case of the stations 13 and 14, as they are located in the plane area, outside the mountains, where snow is not usually present, but it can decrease up to 2% in the upper stations.
In addition, the station 9 is the one with a major improvement in the estimates, this is ought to the fact that is placed in a gully, having the larger horizon obstructions compared with the other stations. The mean RMSE considering the whole set of stations is reduced from 16% to 11%, verifying the good behaviour of the proposed methodology.
Once the performance of the method has been analyzed and in view of the satisfactory results provided in the global radiation estimates for the 14 stations located on a complex topography area, we generated a map with daily radiation values for the zone. Figure 5 shows a map created using this methodology.
Fig. 5 . Mapping of daily solar irradiation over a complex topography area. 
Results obtained using the proposed method showed a good agreement with the measured ones and it is interesting to note that this procedure can be applied under all kind of sky conditions. A proposed method to calculate the horizon of a point was tested with satisfactory results.
The RMSE was diminished for the whole set of stations with a mean reduction of 4.7%. MBE was also improved, with almost no over or underestimation for most of the stations. It has been observed that the improvement in the estimates is small in plane areas with almost no horizon obstructions, but becomes significant in the locations inside the mountain. The effect of the snow was also studied and it was pointed out that for the stations in the upper area it could lead to a reduction of the RMSE up to 2%, being smaller or nil in the lower stations. Finally, the method was employed over a wide area allowing the generation of an irradiation map on a complex terrain. If no additional information would be considered, the HELIOSAT 2 model would give a fixed value for the whole pixel, taking into account a mean altitude of the pixel and no horizon. On the other hand, using this methodology, the topographic variability is included in the model and a map of irradiation can be made in an easy way for a complex topography site. In future works, this method is intended to be extended to the estimation of different components of the radiation (i. e. Direct Radiation, Photosynthetically Active Radiation PAR); and also to be used together with Artificial Neural Networks.
This work has been financed by the Ministerio de Ciencia y Tecnologia of Spain (projects ENE/2004 0786C0301 and ENE200767849C0202). We would like to thank CIEMAT for the use of the satellite images.
[1] Dubayah and Van Katwijk, 1992. The topographic distribution of annual incoming solar radiation in the grand River basin. Geophys Res. Leter, 19, 22312234.
[2] Fu and Rich, 2000. A geometric solar radiation model and its applications in the agriculture and forestry. Proceedings of Second International Conference on Geospatial Information in Agriculture and Forestry, Lake Buena Vista.
[3] Diabate, L., Demarcq, H., MichaudRegas, N. and Wald, L., 1988. Estimating incident solar radiation at the surface from images of the Earth transmitted by geostationary satellites: the Heliosat Project. International Journal of Solar Energy 5, 261278.
[4] Dribssa, E., Cogliani, E., Lavagno, E. and Petrarca, S., 1999. A modification of the Heliosat method to improve its performance. Solar Energy 65, 369377.
[5] Zarzalejo, L. F., 2006. Irradiancia solar global horaria a partir de imagenes de satelite. Serie: Coleccion Documentos CIEMAT. Editorial CIEMAT, Madrid (Espana).
[6] ESRA, 2000. The European solar radiation atlas. Vol. 1: Fundamentals and maps. Editado por: Scharmer, K. and Reif, J. Les Presses de l’Ecole des Mines, Paris (France).
Benchmarking of solar radiation products can be done in different ways. If a kind of reference data is available which is assumed to be the “truth”, the modelled data sets can be compared and ranked how well they represent the reference data. But there is not always reference data available: e. g. for solar radiation spatial products (maps). Here benchmarking can assess the uncertainty of mapping products by their crosscomparison.
For site specific time series there are a number of different measures for benchmarking. A first set is based on first order statistics. These are the well known bias, root mean square deviations, standard deviations, their relative values to the average of the data set and the correlation coefficient. They compare how well data pairs at the same point of time compare with each other. They are important if one needs an exact representation of real data, e. g. for evaluations of real operating systems or forecasts of solar radiation parameters.
This exact match is not always important, e. g. for system design studies. Here the similarity of statistical properties as frequency distributions is more important than the exact match of data pairs. The MESoR project therefore suggests a number of parameters based on second order statistics [6].
Besides a common set of measures the selection of valid pairs of data is of importance to achieve comparable results. Valid data pairs should have passed the quality control procedure as described above, measured global horizontal ground data should be above zero (valid measurement, sun above the horizon) and the modelled data should be valid. Average values (e. g. for relative bias or standard deviations) are calculated based on the valid data pairs. If averages from multiple stations are to be calculated, all data pairs should be treated with the same weight. Averages should be calculated from the complete data set and not from the single stations results. This gets relevant if the stations have different numbers valid data pairs.
A benchmarking exercise applying the measures and rules to available data bases within Europe will be done in the second half of 2008.
Benchmarking of angular distributions is yet difficult, as there are a number of different instruments available with very different characteristics in terms of number of sensors, acquired parameters
(irradiance, radiance, luminance), geometry of the measurement directions in the sky hemisphere, spectral sensitivity of the sensors, aperture angle, size and shape and the sensors and the sensors linearity and dynamics. Further, there are only very few measurements available so far.
Solar maps can be benchmarked in two ways, either point based or map based. The point based benchmarking is similar to the time series benchmarking. Data is extracted from the maps and compared to the measurements (“ground truth”). First and second order statistics can be applied. Map based cross comparison of solar radiation provides means for improved understanding of regional distribution of the uncertainty by combining all existing resources (calculating the average of all) and quantifying their mutual agreement by the means of standard deviation. A sample evaluation has been done with five spatial data bases: ESRA, PVGIS, Meteonorm, Satellight and NASE SSE.
Fig. 1: Yearly sum of global horizontal irradiation, average of all five data bases [kWh/m2] (left), uncertainty at 95% confidence interval from the comparison of the five data bases. [%] (right). 
A set of benchmarks has been proposed to evaluate solar radiation forecasts. Depending on the application different methods to assess prediction quality are appropriate. For the users of the forecast the verification scheme should be kept as simple as possible and be reduced to a minimum set of accuracy measures. Statistical measures are similar to time series but need to be adapted to forecast specific issues. Forecast should not only be evaluated against the measured data but also against reference models as persistence or autoregressive models to show the advantage of a forecasting system. For forecasting not only the quality of the prediction of a single site is of interest but also the accuracy of prediction of an ensemble of distributed sites e. g. feeding into an electricity grid node.
L. Martin1*, E. Lorenz2, A. Sood2, L. F. Zarzalejo1, K. Suselj2,J. Polo1, A. Navarro1, R.
1 CIEMAT, Department of Energy, Av. Complutense n°22, Madrid, 28040, Spain
2 Oldenburg University, Germany Energy and Semiconductor Research Laboratory Energy Meteorology Group
3 IrSOLaV, Calle Santiago Grisolia (PTM) 2, Tres Cantos, 28045, Madrid, Spain
* Corresponding Author, luis. martin@ciemat. es
Although considerable effort has been done to make use of solar energy efficiently from industrial revolution, expecting fossil fuels would run out in the future, only minimal resources have been directed towards forecasting incoming energy at ground level. However, the necessity to have forecasting models which could optimize the integration of solar thermal power and photovoltaic into electric grid within different sources of electric power generation will grow up as they gain recognition as an energetic resource in the near future. In this work an evaluation of ERA40 reanalysis data from ECMWF and Weather Research and Forecasting (WRF) mesoscale model is done over 40 stations from Spanish National Radiometrical Network which belongs to the Spanish National Weather Service (AEMet). The evaluaton is done in a hourly and daily resolution just for the next time step in the case of WRF evaluation and for daily resolution in the case of ERA40 data. The results from WRF model are compared with predictions from European Center for Medium Range Forecasting (ECMWF).
Keywords: EuroSun 2008, proceedings, formatting guidelines, styles
Although considerable effort has been done to make use of solar energy efficiently from industrial revolution, expecting fossil fuels would run out in the future, only minimal resources have been directed towards forecasting incoming energy at ground level [1]. However, the necessity to have forecasting models which could optimize the integration of solar thermal power and photovoltaic into electric grid within different sources of electric power generation will grow up as they gain recognition as an energetic resource in the near future.
Photovoltaic and solar thermoelectric power are main sources of solar energy for electricity generation. Currently the potential market is huge and its development is being supported by agreements in Kyoto protocol and by progressive series of regulations regarding green energy (feedin tariff) established in several countries like Spain and German [2]. In the case of Spain, current legislation (Royal Decree 436/2004, 12th of March) allows to minimize investment risks to promoters and to contribute opening up great perspectives to solar energy development.
Energy stock market participation is regulated by two basic rules: on the one hand it is necessary to predict the amount of energy which will be produced, up to 72 hours before, and on the other hand deviations of energy produced compared to programmed one are strongly penalized.
In this work a skill evaluation of the Weather Research and Forecasting (WRF) mesoscale model and ERA40 reanalysis data from ECMWF is done over 40 stations from Spanish National Radiometrical Network which belongs to the Spanish National Weather Service (AEMet). The prediction is done in a hourly and daily resolution just for the next time step. The results from WRF model are compared with predictions from European Center for Medium Range Forecasting (ECMWF).