Average Global Solar Radiation Research Articles

An experimental study of the effects of using cool materials at different building heights on the indoor and outdoor environment

Four building arrays, each comprising 25 building models, were utilized to investigate the effects of cool materials on building surface temperatures, indoor temperatures, and street microclimates in Guangzhou, China. The experiments are divided into three groups. The first group of the experimental models is used to investigate the cooling effect of the roof using cool materials on the roof surface and indoor environment. The results show that using cool materials on roofs can significantly reduce roof surface and indoor temperatures of the top floor by 13.5 °C and 3.9 °C at noon under an average global solar radiation (GSR) of 727 W/m2. The second group of experiments included four arrays: one control group without cool materials and three other arrays applying cool materials on the third, second, and first floors, respectively. Using cool materials on the façades of the third, second, and first floors reduced the indoor temperature by an average of 1.9 °C, 0.8 °C, and 0.5 °C (average GSR = 404 W/m2), respectively. As a result, higher floors receive more solar radiation, and cool materials show better cooling capacity. The third group is used to investigate the effect on street air temperatures when cool materials are used on all building façades. Overall, the effect of cool materials on the street air temperature is very small, with a maximum reduction of only 0.2 °C in the street air temperature.

Calibration of the Ångström–Prescott Model for Accurately Estimating Solar Radiation Spatial Distribution in Areas with Few Global Solar Radiation Stations: A Case Study of the China Tropical Zone

The Ångström–Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Aiming at the characteristics of a vast area, few meteorological stations, and uneven distribution in the tropical regions of China, in order to obtain the optimal parameters of the global solar radiation calculation model, this study proposes a suitable monthly global solar radiation model based on the single-station approach and the between-groups linkage of the A–P model, which utilizes monthly measured meteorological data from 80 meteorological stations spanning the period from 1996 to 2016 in the tropical zone of China, considering the similarity in changes of monthly sunshine percentage between stations. The applicability and accuracy of the correction parameters (a and b coefficients) were tested and evaluated, and then the modified parameters were extended to conventional meteorological stations through Thiessen polygons. Finally, the spatial distribution of solar radiation in the tropical region of China was simulated by kriging, IDW, and spline interpolation techniques. The results show the following: (1) The single-station model exhibited the highest accuracy in simulating the average annual global solar radiation, followed by the model based on the between-groups linkage. After optimizing the a and b coefficients, the simulation accuracy of the average annual global solar radiation increased by 5.3%, 8.1%, and 4.4% for the whole year, dry season, and wet season, respectively. (2) Through cross-validation, the most suitable spatial interpolation methods for the whole year, dry season, and wet season in the tropical zone of China were IDW, Kriging, and Spline, respectively. This research has positive implications for improving the accuracy of solar radiation prediction and guiding regional agricultural production.

Estimation of Global Solar Radiation using Angstrom and Gopinathan Model on Sunshine Hour and Temperature in Highland, Nepal

In developing countries like Nepal, the direct measurement of global solar radiation (GSR) is difficult. So, the estimation of GSR is carried out at Jumla (29°16′ N, 82° 11′ E and about 2347 m altitude) for the year 2015 and 2017 using regression technique to the meteorological parameters on Angstrom model, Gopinathan and Olomiyesan- Oyedum models. Among three models, Olomiyesan and Oyedum model is better than other models. Its empirical constants a=0.38, b=0.10 and c=0.09 are found. The values of statistical errors MBE, MPE and RMSE are smaller than other models. Similarly, the coefficient of determination (R2=0.89) is greater than other models. Finally, the finding empirical constants and meteorological parameters sunshine hour, and temperature are used to estimate the GSR for the year 2017. In addition to this, the annual average GSR for the year 2015 and 2017 are found to be 18.86 MJ/m2/day and 17.50 MJ/m2/day respectively. It is concluded that the finding empirical constants are used to estimate the GSR and solar energy at similar geographical location of Nepal.

Smart energy monitoring and power quality performance based evaluation of 100-kW grid tied PV system

Globally, the demand for energy from renewable sources is growing due to the increasing electricity consumption and the pollution of fossil fuels. The government has framed various policies to facilitate green energy generation, encouraging renewable energy source usage through PV installations in multiple sectors, including educational institutions. The primary objective of this paper is to propose a methodological approach for analysing the performance of the installed PV system on the rooftop of a university building in Tamil Nadu, India. The site selected is favourable for electricity generation from PV systems with an average global solar radiation of 5.82 kWh/m2day. Solar energy changes periodically with annual and daily variations and is not steady due to seasonal changes. The step-by-step performance assessment and the annual performance of the 100-kW solar PV system, which was instituted in 2019, with the forecasted parameters, are presented in this paper. Therefore, the assessment analysis is carried out in four phases: feasibility assessment, Energy yield assessment, Life cycle assessment, and Power quality assessment. To improve the solar PV output and efficiency, considering the solar irradiation, temperature, wind velocity, etc., PV yield is measured to evaluate the PV system's energy metrics. This paper also considers the carbon credits earned, solar power generated in the location, and the payback period. The power quality assessment is carried out in this paper to test the PV plant's compliance with effective grid integration.

Comparative Study of Solar Radiation Models for the Estimation of Solar Radiation Using Short-Term Meteorological Data in Lawra, Ghana

Monthly average daily global solar radiation data are essential for the design and study of solar energy systems. The performance and accuracy of eleven models for the estimation of monthly average global solar radiation were compared in this study. Nineteen months (Nov 2020 – May 2022) ground measurement data consisting of monthly mean daily sunshine duration, relative humidity, minimum and maximum temperatures, and global solar radiation collected from the Lawra Solar Plant were used. The models were compared using statistical indices. According to the indices, most of the models were in reasonably good agreement with the measured data. Two model equations, however, were found to have the highest accuracy and can thus be used to estimate monthly average global solar radiation in Lawra and other places with similar climatic conditions where radiation data is unavailable.

Empirical Model for Estimating Measured Monthly Average Global Solar Radiation in Lawra using TMY Data

In this study, simple and multiple regression models were developed to estimate the monthly average daily global solar radiation in Lawra, Ghana using ground measurement of global horizontal irradiance (Nov 2020–May 2022) and typical meteorological year (TMY) data (Jan 2017–Dec 2019). Various predictor variables such as sunshine ratio, minimum relative humidity and maximum relative humidity ratio, minimum and maximum temperature ratio, etc. were correlated from the TMY data. Many model equations were developed with the variables ranging from one to eight. The best model from each category was chosen and compared using statistical indices to determine the overall best model. We used the JMP statistical software’s ‘All Possible Models’ functionality to select the best model from each category. The selected models where then compared using the adjusted R-squared, mean absolute percentage error, and the root mean square error statistical indices. The best model equation correlated with eight independent variables with adjusted R-squared of 0.99. The equation can be used to estimate monthly global solar radiation in Lawra and in locations with similar climatic conditions where ground measurement of radiation data is unavailable but have access to the National Solar Radiation Database’s (NSRDB) TMY data.

Estimation of Global Solar Radiation at Mid Hill Region, Okhaldhunga

Global solar radiation knowledge can be valuable in fulfilling energy demands and exploring numerous applications of solar technology. Since only a few meteorological stations are available and the installation of additional stations is complicated because the cost is very high, a comparative study of various models is essential for accurate global solar radiation prediction rather than measurement. In the present study, four models based on sunshine hour, temperature, and relative humidity for estimating daily global solar radiation were compared using the regression technique for Okhaldhunga (Lat.27.30810 N, Lon.86.50420 E, Alt.1720m.a.s.l.). These models were evaluated and compared based on four statistical error tests. The basis of analysis, model M4, which uses sunshine hour, maximum temperature, and relative humidity, was used for estimating daily global solar radiation with acceptable accuracy. The empirical constants obtained for M4 models were a=0.62, b=0.47, c= -0.003, and d= -0.003. The site's annual average global solar radiation was 4.16 kWh/m2 /day. The results of this study help estimate global solar radiation for similar geographical locations in Nepal.

Estimation of Global Solar Radiation (GSR) From the Sunshine Hour of Guranshe, Surkhet, Nepal

The variation in Global Solar Radiation (GSR) was studied by using experimental data via an automatic weather station (CMP3 pyranometer) located at Guranshe (Surkhet), a hilly area with elevation 2194m from the sea level. Also the estimated sunshine hour data of the area were used to calculate the GSR for the purpose of comparison with the measurements. Our study finds a good agreement between the measured (15.65 MJ/m2/day) and estimated/calculated (19.18 MJ/m2/day) values of annual average GSR. The estimated GSR uses Angstrom-Prescott type model for calculating sunshine hours, and the coefficients (a=0.28) and (b=0.57) were obtained using the Sangeetas and Tiwari model. The annual average estimated sunshine hours was found to be 6.86hr which is suitable for GSR based applications. The measured data were used to study the seasonal, monthly and diurnal variations of GSR. The maximum and minimum values of GSR were found during the spring and summer seasons respectively. The minimum GSR in summer is because of heavy rainfall, high relative humidity, and cloud formation in the sky during the time. The coefficient of regression(R) and the coefficient of determination (R2) were obtained as R=0.881 and R2=0.776 respectively. Here the high value of R (88.1%) indicates that of dependent variable (GSR) is nicely predicted by the independent variables. Also the coefficient of determination (R2) tells us the strength of the relationship between two variables. BIBECHANA 19 (2022) 150-159

Best Combinations of Inputs for ANN-Based Solar Radiation Forecasting in Morocco

In this paper, for forecasting, with the high possible accuracy, the solar radiation intensity, an approach for identifying the optimum set of input data from large sets of input parameters is elaborated, assessed, and proposed. This approach belongs to techniques of Input Variable Selection (IVS) and was established using artificial neural networks (ANNs) as an effective means for numerically approximating the computed objective function: Monthly Average Global Solar Radiation (MAGSR). Based on the approach suggested, we could determine the best combinations of inputs that can reveal great possible correlation and approximation with the output considered parameter. Recorded data from 35 stations of different climatic zones were used for both training and testing purposes. Several new linear formulas between the MAGSR and other climatological and meteorological parameters were developed and evaluated. Based on these relationships, we can forecast other climatological and meteorological parameters like temperature, wind speed, and humidity. The statistical analysis was done, and the best performance of the proposed approach has been well checked and duly validated.

Estimation of Daily Average Global Solar Radiation with Nonlinear Regression Models Developed Using Some Meteorological and Geographical Parameters

In this study, it is aimed to develop empirical models that can be used in estimation of daily average solar radiation (RS) based on some meteorological and geographical parameters. Seven estimation models were developed by nonlinear regression analysis method using various combinations of air temperature (T), relative humidity (RH), extraterrestrial radiation (Ra), saturated (es) and actual vapour pressure (ea) parameters. The models were created using the long-term average daily meteorological data of Kahramanmaraş province (1938 – 2020). The models were tested both these long-term average data and daily meteorological data measured at Kahramanmaraş Sütçü İmam University (KSU) in 2019 and 2020. Long-term average daily actual RS data varied between 4.99 – 32.56 MJ m-2 day-1. The estimated solar radiation values (("RS" ) ̂) with the highest correlation (r = 0.99) with actual RS data were obtained with the RS_7 model, in which the parameters es, ea, T, RH and Ra were used together. The ("RS" ) ̂ values obtained using this model varied between 6.45 to 33.99 MJ m-2 day-1. For the RS_7, which showed the best performance among the seven models, mean absolute percentage error (MAPE) and root mean square error (RMSE) were determined as 4.17% and 0.69 MJ m-2 day-1, respectively. The daily RS values measured in KSU varied between 7.75 – 33.48 MJ m-2 day-1 and 10.51 – 30.23 MJ m-2 day-1 for 2019 and 2020. The ("RS" ) ̂ values closest to the measured RS values were estimated with the RS_7 model. The estimated ("RS" ) ̂ values by this model varied between 11.74 – 33.93 MJ m-2 day-1 and 13.93 – 31.57 MJ m-2 day-1 for 2019 and 2020, respectively. MAPE values were determined as 11.33% and 7.54%, respectively. It is concluded that this model can be used to estimates daily average solar radiation and will be an excellent alternative since it is compatible with the Kahramanmaraş conditions.

Spatial analysis of soybean yield in the western mesoregion of Paraná using agrometeorological variables

This paper aimed to analyze the spatial autocorrelation of soybean yield and its bivariate spatial correlation with theagrometeorological variables rainfall, mean temperature, and mean global solar radiation in 2014/2015, 2015/2016, and 2016/2017 crop years in the West of the State of Paraná – Brazil. To achieve this objective, techniques of spatial statistics of areas were used, which, through autocorrelation and spatial correlation indices, sought to identify patterns of association between soybean yield and agrometeorological variables. This research is justified because in addition to the soybean crop being the main source of food protein and vegetable oil in the world and the agrometeorological variables being the factors that most influence it, the western mesoregion of Paraná stands out with the highest production values in the state. Thus, it is important to monitor its development through spatial analysis to obtain information that will support decision making. The global and local Moran’s indices showed that soybean yield is self-correlated in the municipalities of Western Paraná, identifying clusters to the west and east of the mesoregion. The significance of the bivariate spatial correlation indices confirmed the influence of the variables rainfall, mean temperature, and average global solar radiation on soybean yield.

Comparative Assessment of Angstrom Sunshine-Base Models for Estimation of Global Solar Radiation in Owerri, Nigeria

Comparative assessment of the average monthly global solar radiation on horizontal surface for Owerri has been carried out with the use of five Angstrom sunshine base models. Thirty years (1992 - 2022) of average monthly global solar radiation and sunshine hours are sourced from NASA and NIMET, respectively. The sunshine-base models are evaluated with the use of geographical and metrological data of Owerri and comparison is made based on estimated statistical error indices of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Coefficient of Determination (R2) and percentage error to assess the applicability accuracy of the models. Results obtained shows that regression equations of Fagbenle and Page models satisfied viability criterion of MBE, RMSE, and MPE being close to zero, the R2 values are reasonably close to unity and the percentage error agreed to within ± 10% of the measured average monthly global solar radiation on a horizontal surface in Owerri. Rietveld model closely followed with less prediction accuracy to those of Fagbenle and Page models, while, Glower and McCulloh and Turton models yield poor estimation of the average monthly global solar radiation on horizontal surface.

ESTIMATION OF GLOBAL SOLAR RADIATION USING SUNSHINE HOURS IN SOKOTO NIGERIA

Inadequate installations of materials such as solar water heater, solar still, solar dryer, and solar cooker etc. Which required solar radiation and maximally, in the area location of is a major problem and this hinders the abundant utilization of solar energy as it can be harnessed directly without any requirement of external energy such as electricity. It is the lack of the information that limits the number of installations in the study area. Therefore, it is compulsory to study and analyzed the solar radiation of our environment to get better view of monitoring the performance of solar energy conversion system in Sokoto State, so that companies and government should know what period or seasons can therefore utilize the abundant solar energy. A new model was developed for estimation the monthly average daily global solar radiation. In this research a modified angstrom model for estimation of global solar radiation in Sokoto, Nigeria using sunshine duration based on meteorological data were used. However, solar radiation estimates are too local since they rely on weather stations or have a resolution that is too satellites. In this work, a model was developed for estimation of global solar radiation on horizontal surface for Sokoto at latitude of m13 13.00E and longitude of 5.250N. Global solar radiation and maximum temperature data for Sokoto were used to fit the Angstrom model, Regression coefficient a, b, and c. 1.12, 0.65, and 0.32 respectively were obtained from the models base on sunshine duration respectively. In order to evaluate the results, three statistical methods have been used namely: mean bias, root mean square, and percentage errors of values -6.47, 6.47 and 41.20% respectively. It was found from statistical model’s performance indicators that the model provides reasonably high degree of precision in the estimation of average global solar radiation on horizontal surfaces.

Estimating Daily Dew Point Temperature Based on Local and Cross-Station Meteorological Data Using CatBoost Algorithm

Accurate estimation of dew point temperature (T_dew) plays a very important role in the fields of water resource management, agricultural engineering, climatology and energy utilization. However, there are few studies on the applicability of local T_dew algorithms at regional scales. This study evaluated the performance of a new machine learning algorithm, i.e., gradient boosting on decision trees with categorical features support (CatBoost) to estimate daily T_dew using limited local and cross-station meteorological data. The random forests (RF) algorithm was also assessed for comparison. Daily meteorological data from 2016 to 2019, including maximum, minimum and average temperature (T_max, T_min and T_mean), maximum, minimum and average relative humidity (RH_max, RH_min and RH_mean), maximum, minimum and average global solar radiation (Rs_max, Rs_min and Rs_mean) from three weather stations in Hunan of China were used to evaluate the CatBoost and RF algorithms. The results showed that both algorithms achieved satisfactory estimation accuracy at the target stations (on average RMSE = 1.020°C, R²=0.969, MAE = 0.718°C and NRMSE = 0.087) in the absence of complete meteorological parameters (with only temperature data as input). The CatBoost algorithm (on average RMSE = 1.900°C and R²=0.835) was better than the RF algorithm (on average RMSE = 2.214°C and R²=0.828). The accuracy and stability of the CatBoost and RF algorithms were positively correlated with the number of input parameters, and the three-parameter algorithms achieved higher estimation accuracy than the two-parameter algorithms. The developed methodology is helpful to predict T_dew at regional scale.

Comparative Valuation of Global Solar Radiation on Horizontal and Tilt Surfaces in Owerri, Nigeria

Comparative valuation of global solar radiation on horizontal and tilt surfaces with the use of Angstrom-Page and four isotropic models for Owerri has been carried out in this paper. Angstrom linear regression model is developed from thirty years (1992-2022) of sunshine hour and average monthly global solar radiation sourced from NIMET and NASA, respectively. The Angstrom regression coefficients 𝑎 and 𝑏determined for Owerri is 0.210 and 0.696, respectively. The developed model is used to estimate average monthly global solar radiation on horizontal surface and consequently, the amount of incident solar radiation received on tilt surface by the isotropic models. The MBE, RMSE and MPE values are estimated to be 0.0083, 0.0029 and -0.0041%, respectively, for the developed Angstrom-Page model. A strong coefficient of determination of 76.6% was obtained and Analysis of Variance (ANOVA) significant of 100% was observed, which shows the goodness of fitness of the model. Among the isentropic models studied, one of the models poorly estimated solar radiation on horizontal and tilt surfaces for the study location

Empirical Modelling of Global Solar Radiation in Hatay (Turkey) Province

There are many models used in global solar radiation prediction. However, these prediction models vary according to the climate and geographic characteristics of the region involved. When the prediction model determined for a region is used in a different region, the prediction model may not work. In order to gain absolute and stable results from these models, the studies should be either custom-made or tested. In this study, 7 different models were examined, comparison charts were created and statistical interpretations were made in order to determine the monthly average global solar radiation coming to the horizontal plane taking into account the climatic characteristics of Hatay province, located in the eastern 39.92º latitude and northern longitude 36.16º, covering an area of 5403 km, and has a humid climate with a value of 41.3, based on De Martonne-Gottmann’s drought index and 50.6 based on Erinç's rainfall efficiency index. Under the scope of this study, the most ideal global solar radiation prediction model was selected by making a statistical evaluation according to the parameters of determination coefficient (R²), mean percentage error (MPE), mean deviation error (MBE), root mean square error (RMSE). A general assessment of the graphics and statistical parameters of the predictions obtained by using the models and the measured data has indicated Model 4 as the most successful model by an RMSE value of 0.17306, and the least successful model was Model 3 by an RMSE value of 2.11851. When the models are interpreted according to the determination coefficient, it has been observed that other models are generally successful. However, this situation does not coincide with the comparison charts of the predicted and measured data. For this reason, it was seen that the determination coefficient was not sufficient in the statistical evaluation of the prediction models. In the interpretation of the models, it was observed that the interpretation of root mean square error, mean percentage error and mean deviation error values together gave more accurate results. In conclusion, it has been suggested that Model 4 should be preferred for the global solar radiation forecast of Hatay province.

Effect of climate change to solar energy potential: a case study in the Eastern Anatolia Region of Turkey.

Architects, hydrologists, agriculturists, and solar engineers require the data of solar radiation for solar technologies such as solar drying, cooking, heating, and building illuminations. The aim of this study is to evaluate the effect of climate change on the potential of solar energy in theEastern Anatolia Region (EAR) of Turkey. The global warming problem causedby greenhouse gases is increasing due to the increase in the use of fossil origin fuels in our world, and climate change iscoming out. In this content, the values of monthly sunshine duration and globalsolar radiation are provided by the Meteorological Service in Turkish for the EAR of Turkey in the periods between 1987 and 2010. Thus, in a period of 24 years, it is investigated how much change took place in meteorological data. These changes are examined in the two different periods. In conclusion, it is observed that important changes occur in some meteorological data. The highest decrease in the variation amount of yearly average global solar radiation is seen in Erzurum, while the highest increase is seen in Erzincan. The highest increase in the variation amount of yearly average sunshine duration is seen in Erzincan, while the highest decrease is seen in Bitlis. In addition, the statistical analysis (t-test) is made to determine whether the difference between two periods is statistically significant for a considered region.

Off-grid solar photovoltaic/hydrogen fuel cell system for renewable energy generation: An investigation based on techno-economic feasibility assessment for the application of end-user load demand in North-East India

In this paper, an optimal off-grid solar photovoltaic (PV)/hydrogen fuel cell (FC) (HFC) based energy system is proposed for renewable energy generation to supply electricity to the end-user load demand in north-east (NE) Indian states. The energy system is modeled and simulated in the HOMER software. The monthly average global horizontal solar radiation (GHSR) and ambient temperature of the NE states are found to be 3.95–4.79 kWh/m2 and 12.9–25.3 °C, respectively. The synthesized typical AC electrical load profile of 10 kWp is assumed to have a scaled daily annual mean value equal to 138 kWh/d and an average base demand value equal to 5.75 kW. With the considered connected load, the available GHSR and ambient temperature, the optimal sizes of renewable energy components for the considered eight geographical locations (latitude: 23° 30′-27° 3′ North, longitude: 88° 37' – 94° 7′ East) are determined based on the minimum levelized cost of energy (COE) and lowest net present cost (NPC). The optimal system architecture range obtained is 110–120 kW PV array, 10–15 kW FC, 30–60 kW electrolyzer, 40–60 kg hydrogen tank capacity and 15 kW converter. Based on optimization results, the levelized COE and NPC range obtained are 0.509–0.689 $/kWh and $ 3,27,557–4,43,004, respectively, which confirm the cost-effectiveness of the proposed optimal energy system. Besides, with a renewable fraction of 100%, the electricity generated by the PV/HFC system satisfied the load demand and produced excess electricity with capacity factor found to be 16.4–31.2% in all the NE Indian states. Moreover, the optimized electrical profile, PV profile, inverter profile, and hydrogen production from an FC are compared and analyzed in terms of monthly average and annualized values for all the NE states. Also, the range of net hydrogen produced by the electrolyzer over the year obtained is 1538–1856 kg/yr.

An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

Assessment of Solar Energy Potential of East Gojjam Zone Ethiopia Using Angestrom-Prescott Model

Only a small number of Ethiopia’s rural population has access to electricity. These rural areas can be electrified using different electrification approaches. One of the prime challenges in the decision making process is to obtain reliable information about the energy potential of the region under consideration. In this paper, an assessment of the solar energy potential of East Gojjam Zone in Ethiopia is presented. In the study area, there are only four meteorological measurement stations, namely: Debre Markos, Debrewerk, Mota, and Yetnora. The data for the hours of sunshine for these sites is collected from the National Meteorological Agency, Bahir Dar Branch Office. The data is prepared and used to calculate the mean global horizontal solar radiation using Angestrom-Prescott (AP) Model. An AP model for the four study sites was developed. The model regression coefficients, a and b, are estimated for each study site for respective latitudes, average hours of sunshine and day length. According to the models, the annual mean daily global horizontal radiation for Debre Markos, Debrewerk, Mota, and Yetnora is 5.88, 6.52, 6.28, and 6.31 kWh/m2/day, respectively, and 6.30kWh/m2/day for East Gojjam Zone, which is actually a very good solar energy potential. According to the monthly average global horizontal solar radiation profiles, the study sites receive the highest solar radiation in April and lowest solar radiation in July and August. The radiation in Debre Markos varies between 3.97 and 5.88kWh/m2/day, but Debrewerk receives more uniform solar radiation throughout the year. By considering intermittency and average solar energy potential of the four sites, Debrewerq and Debre Markos are the best and the worst sites, respectively, for large solar energy projects.