Solar potential in Turkey

Most of the locations in Turkey receive abundant solar-energy, because Turkey lies in a sunny belt between 36° and 42°N latitudes. Average annual temperature is 18 to 20 °C on the south coast, falls to 14–16 °C on the west coat, and fluctuates between 4 and 18 °C in the central parts. The yearly average solar-radiation is 3.6 kW h/m² day, and the total yearly radiation period is ∼2610 h. In this study, a new formulation based on meteorological and geographical data was developed to determine the solar-energy potential in Turkey using artificial neural-networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg–Marquardt (LM) learning algorithms and logistic sigmoid (logsig) transfer function were used in the networks. Meteorological data for last four years (2000–2003) from 12 cities (Çanakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balikesir, Artvin, Çorum, Konya, Siirt, and Tekirdaǧ) spread over Turkey were used in order to train the neural-network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine-duration, and mean temperature) are used in the input layer of the network. Solar-radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 3.832% and R² values to be about 99.9738% for the selected stations. The ANN models show greater accuracy for evaluating solar-resource posibilities in regions where a network of monitoring stations has not been established in Turkey. This study confirms the ability of the ANN to predict solar-radiation values accurately.     

Forecasting based on neural network approach of solar potential in Turkey

As Turkey lies near the sunny belt between 36 and 42°N latitudes, most of the locations in Turkey receive abundant solar energy. Average annual temperature is 18–20 °C on the south coast, falls down to 14–16 °C on the west coast, and fluctuates 4–18 °C in the central parts. The yearly average solar radiation is 3.6 kW h/m² day, and the total yearly radiation period is 2610 h. The main focus of this study is put forward to solar energy potential in Turkey using artificial neural networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg–Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for last 4 years (2000–2003) from 12 cities (Çanakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balıkesir, Artvin, Çorum, Konya, Siirt, Tekirdağ) spread over Turkey were used as training (nine stations) and testing (three stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used as input to the network. Solar radiation is the output. The maximum mean absolute percentage error was found to be less than 6.78% and R² values to be about 99.7768% for the testing stations. These values were found to be 5.283 and 99.897% for the training stations. The trained and tested ANN models show greater accuracy for evaluating solar resource posibilities in regions where a network of monitoring stations have not been established in Turkey. The predictions from ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology.     

Solar-energy potential in Turkey

In this study, a new formula based on meteorological and geographical data was developed to determine the solar-energy potential in Turkey using artificial neural-networks (ANNs). Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) learning algorithms and a logistic sigmoid transfer function were used in the network. Meteorological data for the last four years (2000 → 2003) from 18 cities (Bilecik, Kırşehir, Akhisar, Bingöl, Batman, Bodrum, Uzunköprü, Şile, Bartın, Yalova, Horasan, Polatlı, Malazgirt, Köyceğiz, Manavgat, Dörtyol, Karataş and Birecik) spread over Turkey were used as data in order to train the neural network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) were used in the input layer of the network. Solar radiation is the output layer. One-month test data for each city was used, and these months data were not used for training. The results show that the maximum mean absolute percentage error (MAPE) was found to be 3.448% and the R² value 0.9987 for Polatlı. The best approach was found for Kırşehir (MAPE=1.2257, R²=0.9998). The MAPE and R² for the testing data were 3.3477 and 0.998534, respectively. The ANN models show greater accuracy for evaluating solar-resource possibilities in regions where a network of monitoring stations has not been established in Turkey. This study confirms the ability of the ANN to predict solar-radiation values precisely.     

Modeling of solar radiation using remote sensing and artificial neural network in Turkey

Artificial neural networks (ANNs) were used to estimate solar radiation in Turkey (26–45°E, 36–42°N) using geographical and satellite-estimated data. In order to train the Generalized regression neural network (GRNN) geographical and satellite-estimated data for the period from January 2002 to December 2002 from 19 stations spread over Turkey were used in training (ten stations) and testing (nine stations) data. Latitude, longitude, altitude, surface emissivity for ?_4, surface emissivity for ?_5, and land surface temperature are used in the input layer of the network. Solar radiation is the output. Root Mean Square Error (RMSE) and correlation coefficient (R²) between the estimated and measured values for monthly mean daily sum with ANN values have been found as 0.1630 MJ/m² and 95.34% (training stations), 0.3200 MJ/m² and 93.41% (testing stations), respectively. Since these results are good enough it was concluded that the developed GRNN tool can be used to predict the solar radiation in Turkey.     

Assessment of diffuse solar energy under general sky condition using artificial neural network

In this paper, artificial neural network (ANN) models are developed for estimating monthly mean hourly and daily diffuse solar radiation. Solar radiation data from 10 Indian stations, having different climatic conditions, all over India have been used for training and testing the ANN model. The coefficient of determination (R²) for all the stations are higher than 0.85, indicating strong correlation between diffuse solar radiation and selected input parameters. The feedforward back-propagation algorithm is used in this analysis. Results of ANN models have been compared with the measured data on the basis of percentage root-mean-square error (RMSE) and mean bias error (MBE). It is found that maximum value of RMSE in ANN model is 8.8% (Vishakhapatnam, September) in the prediction of hourly diffuse solar radiation. However, for other stations same error is less than 5.1%. The computation of monthly mean daily diffuse solar radiation is also carried out and the results so obtained have been compared with those of other empirical models. The ANN model shows the maximum RMSE of 4.5% for daily diffuse radiation, while for other empirical models the same error is 37.4%. This shows that ANN model is more accurate and versatile as compared to other models to predict hourly and daily diffuse solar radiation.     

Monthly and yearly average maps of total and direct solar radiation in Turkey

Total and direct solar radiation are calculated using the bright sunshine hours measurements from 83 stations over Turkey. The results are presented in the form of monthly and yearly mean maps. The values associated with the isoradiation lines, which pass throughout the frontiers of the country, are compared with the results obtained in some neighbourhood countries.     

Assessment of solar and wind energy resources in Ethiopia. I. Solar energy

This paper describes how data from a variety of sources are merged to present new countrywide maps of the solar energy distribution over Ethiopia. The spatial coverage of stations with radiation data was found to be unsatisfactory for the purpose of a countrywide solar energy assessment exercise. Therefore, radiation had to be predicted from sunshine hours by employing empirical models. Using data from seven stations in Ethiopia, linear and quadratic correlation relationships between monthly mean daily solar radiation and sunshine hours per day have been developed. These regional models show a distinct improvement over previously employed countrywide models. To produce a national solar-energy distribution profile, a spatial extension of the radiation/sunshine relationships had to be carried out. To do this, the intercepts (a) and slopes (b) of each of the seven linear regression equations and another six from previous studies, completed in neighbouring Sudan, Kenya and Yemen, were used to interpolate the corresponding values to areas between them. Subsequent to these procedures, 142 stations providing only sunshine data were assigned their “appropriate” a and b values to estimate the amount of solar radiation received, which was then used to produce annual and monthly solar radiation distribution maps for Ethiopia. The results show that in all regions solar energy is an abundant resource.     

Estimating monthly mean valuesof daily total solar radiation for Australia

Monthly mean values of daily total solar radiation were obtained for the widest possible network acrossAustralia. Bureau of Meteorology sources yielded 11 stations with long term records of both measured daily total solar radiation and sunshine hour values. Monthly modified Angstrom equations were developed from these data and used to estimate radiation values for a further 90 stations in the Bureau of Meteorology network that had sunshine hour data. Measured daily total solar radiation data were obtained from a variety of sources mostly outside the Bureau of Meteorology network for an additional 33 stations. Finally, estimates of solar radiation from detailed cloud cover data were used for a further 12 stations, selected because they filled in significant gaps in coverage. These various sources yielded a total of 146 sets of monthly mean values of daily total solar radiation. For each month optimal surfaces, which were functions of position only, were fitted to this network of values using Laplacian smoothing splines with generalized cross validation. Residuals from the fitted surfaces at the data points were acceptably low. Fitted surfaces which included, in addition to position variables, a cloudiness index based on a transform of mean monthly precipitation further reduced these residuals. The latter fitted surfaces permit estimation of monthly mean values of total daily solar radiation at any point on the continent with a root mean square predictive error of no more than 1.25 MJ m⁻² day⁻¹ (5.2 per cent of the network mean) in summer and 0.74 MJ m⁻² day⁻¹ (5.5 per cent of the network mean) in winter.     

Modelling of solar energy potential in Nigeria using an artificial neural network model

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4–14°N, log. 2–15°E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983–1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01–5.62 to 5.43–3.54 kW h/m² day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.     

Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models

An artificial neural network (ANN) model for estimating monthly mean daily diffuse solar radiation is presented in this paper. Solar radiation data from 9 stations having different climatic conditions all over China during 1995–2004 are used for training and testing the ANN. Solar radiation data from eight typical cities are used for training the neural networks and data from the remaining one location are used for testing the estimated values. Estimated values are compared with measured values in terms of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE). The results of the ANN model have been compared with other empirical regression models. The solar radiation estimations by ANN are in good agreement with the actual values and are superior to those of other available models. In addition, ANN model is tested to predict the same components for Zhengzhou station over the same period. Results indicate that ANN model predicts the actual values for Zhengzhou with a good accuracy of 94.81%. Data for Zhengzhou are not included as a part of ANN training set. Hence, these results demonstrate the generalization capability of this approach and its ability to produce accurate estimates in China.     

Mapping daily global solar irradiation over Spain: A comparative study of selected approaches

Three methods to estimate the daily global solar irradiation are compared: the Bristow-Campbell (BC), Artificial Neural Network (ANN) and Kernel Ridge Regression (KRR). BC is an empirical approach based on air maximum and minimum temperature. ANN and KRR are non-linear approaches that use temperature and precipitation data (which have been selected as the best combination of input data from a gamma test). The experimental dataset includes 4 years (2005-2008) of daily irradiation collected at 40 stations and temperature and precipitation data collected at 400 stations over Spain. Results show that the ANN method produces the best global solar irradiation estimates, with a mean absolute error 2.33 MJ m⁻² day⁻¹. Daily maps of solar irradiation over Spain at 1-km spatial resolution are produced by applying the ANN method to temperature and precipitation maps generated from ordinary kriging.     

Computation of beam solar radiation at normal incidence using artificial neural network

In this paper, an artificial neural network (ANN) model is developed for estimating beam solar radiation. Introducing a newly defined parameter, known as reference clearness index (RCI), computation of monthly mean daily beam solar radiation at normal incidence has been carried out. This RCI is defined as the ratio of measured beam solar radiation at normal incidence to the beam solar radiation as computed by Hottel's clear day model. Solar radiation data from 11 stations having different climatic conditions all over India have been used for training and testing the ANN. The feedforward back-propagation algorithm is used in this analysis. The results of ANN model have been compared with measured data on the basis of root mean square error (RMSE) and mean bias error (MBE). It is found that RMSE in the ANN model varies 1.65–2.79% for Indian region.     

Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks

Four variables (total cloud cover, skin temperature, total column water vapour and total column ozone) from meteorological reanalysis were used to generate synthetic daily global solar radiation via artificial neural network (ANN) techniques. The goal of our study was to predict solar radiation values in locations without ground measurements, by using the reanalysis data as an alternative to the use of satellite imagery. The model was validated in Andalusia (Spain), using measured data for nine years from 83 ground stations spread over the region. The geographical location (latitude, longitude), the day of the year, the daily clear sky global radiation, and the four meteorological variables were used as input data, while the daily global solar radiation was the only output of the ANN. Sixty five ground stations were used as training dataset and eighteen stations as independent dataset. The optimum network architecture yielded a root mean square error of 16.4% and a correlation coefficient of 94% for the testing stations. Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.     

Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models

In this paper, an artificial neural network (ANN) model is developed for estimating monthly mean daily global solar radiation of 8 typical cities in China. The feed-forward back-propagation algorithm is applied in this analysis. The results of the ANN model and other empirical regression models have been compared with measured data on the basis of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE). It is found that the solar radiation estimations by ANN are in good agreement with the measured values and are superior to those of other available empirical models. In addition, ANN model is tested to predict the same components for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou stations over the same period. Data for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou are not used in the training of the networks. Results obtained indicate that the ANN model can successfully be used for the estimation of monthly mean daily global solar radiation for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou. These results testify the generalization capability of the ANN model and its ability to produce accurate estimates in China.     

Simulation model of ANN based maximum power point tracking controller for solar PV system

In this paper the simulation model of an artificial neural network (ANN) based maximum power point tracking controller has been developed. The controller consists of an ANN tracker and the optimal control unit. The ANN tracker estimates the voltages and currents corresponding to a maximum power delivered by solar PV (photovoltaic) array for variable cell temperature and solar radiation. The cell temperature is considered as a function of ambient air temperature, wind speed and solar radiation. The tracker is trained employing a set of 124 patterns using the back propagation algorithm. The mean square error of tracker output and target values is set to be of the order of 10⁻⁵ and the successful convergent of learning process takes 1281 epochs. The accuracy of the ANN tracker has been validated by employing different test data sets. The control unit uses the estimates of the ANN tracker to adjust the duty cycle of the chopper to optimum value needed for maximum power transfer to the specified load.     

Solar-driven ejector-absorption cooling system

The usage possibility of ejector-absorption cooling systems (EACSs) in Turkey using meteorological data has been investigated. This study also determines whether or not the required heat for the generator of an EACS can be obtained from solar energy in Turkey. There are two important reasons for the usage of EACSs in Turkey. One of them is that the production and use of the CFCs and HCFCs will be phased out a few years according to the Montreal Protocol, signed in 1987. The other is that Turkey is located between 36° and 42°N latitudes and has a typical Mediterranean climate. Therefore, Turkey has a high solar-energy potential, and the yearly average solar-radiation and the total yearly radiation period are 3.6 kW h/m² day and ∼2610 h, respectively. Sixteen cities (Ordu, Tekirdağ, Sakarya, Çorum, Erzincan, Bursa, Balıkesir, Afyon, Bingöl, Burdur, Konya, Niğde, Adıyaman, Hakkari, Anamur, Finike) were selected in Turkey for which the radiation data and sunshine-duration information have been collected since 2000. The required optimum collector-surface area was identified by using the meteorological data for maximum coefficient-of-performance (COP_max) conditions of the EACS operated with aqua-ammonia. In addition, the required minimum energy for the auxiliary heater was also calculated so that the system can be used throughout the year. It is shown that the heat-gain factor (HGF) varies in the range from 1.34 to 2.85 for all the seasons in the selected cities. The maximum HGF is 2.85 for Finike. According to the results obtained in this study, for 8 → 9 months (March–October), it is sufficient to have a collector surface-area of 4 m² with high-performance refrigeration all over of Turkey. This study will provide guidance for the efficient utilisation of renewable energy sources in Turkey, which is heavily dependent upon imported energy sources, i.e. natural gas.     

The solar radiation climate of Thailand

Geographical, seasonal, and diurnal variations of global solar radiation in Thailand are surveyed. Seasonal effects are shown by separate studies for eight 1.5 month periods of the year defined by standard solar declination values. Detailed maps are given of the geographical distribution of solar radiation prepared from data on cloudiness at 44 stations, duration of sunshine at 18 stations, and linear regressions relating radiation to sunshine at Chiang Mai and Bangkok. The highest mean values are above 19.5 MJ m⁻² d⁻¹ and are widespread in spring. The lowest values are below 15.0 MJ m⁻² d⁻¹ in restricted localities with heavy rainfall in autumn.Rough estimates of diffuse solar radiation and atmospheric turbidity are made from the radiation-sunshine regression parameters. Diffuse radiation averages 8.4 MJ m⁻² d⁻¹. Turbidity at Chiang Mai is high in spring and low in summer and autumn; at Bangkok it is high throughout the year.The diurnal variation of global solar radiation determined from hourly measurements at Chiang Mai and Bangkok is analysed. The mean midday radiation fluxes range from 0.80 kW m⁻² in spring to 0.60 kW m⁻² in autumn. On the average the radiation received in the afternoon is slightly less than that received in the morning.     

Measurement of solar-energy (direct beam radiation) in Abu Dhabi, UAE

This paper presents actual measurements of direct solar radiation in Abu Dhabi (24.43°N, 54.45°E) with the existing meteorological conditions encountered during the measurement throughout the year. High resolution, real-time solar radiation and other meteorological data were collected and processed. Daily and monthly statistics of direct solar radiation were calculated from the one-minute average recorded by a Middleton Solar DN5-E Pyroheliometer. The highest daily and monthly mean solar radiation values were recorded as 730 and 493.5 W/m², respectively. The highest one-minute average daily solar radiation was recorded as 937 W/m². In addition to direct beam radiation, the daily average clearness indexes, surface temperature variations, wind speeds and relative humidity variations are discussed. When possible, direct beam radiation and some meteorological data are compared with corresponding data of the 22-year average of NASA's surface meteorology and solar-energy model. The measured data (direct beam radiation and meteorological) are in close agreement with the NASA SSE model with some discrepancy.     

Technical note On the development of spatialtemporal solar radiation maps: a Brazilian case study

This work summarizes recently published information on the solar resource of Brazil. We describe the spatial distribution of solar radiation and its relationship with climatic and geographical conditions. In order to harmonize the information in terms of type of instruments, time recording period and data processing methods, a careful selection of records from the data base was made. Density of recording stations is reasonable in the south, southeast and northeast regions of the country, while in the west center and north regions the density of stations is rather poor. The procedure to elaborate the maps of daily solar radiation, monthly and annual average is described. Consideration of the measuring period of the monthly averages, used to elaborate the contour maps, shows that they meet the requirement that 90% of averages are inside the strip of ±7.5%, centralized on the average of very long period measurements. We present one map with the localization of the recording stations and one annual and 12 monthly contour maps, describing daily solar radiation levels over the whole territory. Spacing among the contour lines is (±2 MJm² day). Annual average of solar radiation lies within the interval of ((18±2) MJm² day), except in the northeast region where values higher than (20 MJm² day) are found. Two regions with levels of (16 MJm² day) are also observed. The highest monthly average values (24 MJm² day) are observed in the state of Rio Grande do Sul, southern end of the country, in the summer season (December and January). The lowest values in the country (8 MJm² day) are observed in June and July (winter in the southern hemisphere), on the extreme south coastline of the same state, Rio Grande do Sul, below 32° south latitude.     

Quantifying spatio-temporal dynamics of solar radiation exergy over Turkey

The insensitivity to energy quality is one of the disadvantages of an energy analysis when compared to an exergy analysis. It is only the exergy analysis that clearly reveals the degradation of energy quality in the processes of absorption and emission of solar radiation. The national spatial distribution of mean monthly exergy values of solar radiation over Turkey was mapped at 500-m resolution using universal kriging based on solar radiation data from 152 geo-referenced locations. Mean exergy value of solar radiation in Turkey was estimated at 13.5 ± 1.74MJm^?2day^?1, with a mean annual exergy-to-energy ratio of 0.93.