Monthly Mean Global Solar Radiation Research Articles

Evaluation of global solar radiation, cloudiness index and sky view factor as potential indicators of Ghana's solar energy resource

The increasing demand of Ghana's renewable energy sector requires a comprehensive evaluation of sky conditions that may be favourable for the efficient harnessing of solar energy resources. However, data for such evaluation are typically scarce due to instrumentation challenges. Empirical estimates using sunshine duration and cloud cover as input data were used to evaluate the solar energy resources’ potential at 22 synoptic stations distributed across the four climatic zones of Ghana (savannah, transition, forest and coastal) for the period 2015 – 2018. Sunshine duration was recorded using the Campbell-Stokes sunshine recorder while the cloud amount was done manually through visual observation. The modified Ångström-Prescot sunshine model was used to estimate the monthly mean global solar radiation. The cloudiness index was then computed as the ratio of the mean cloud cover to the global solar radiation. A modified Steyn-method, based on sunshine duration, was used to estimate the sky view factor. Ordinary kriging was used to interpolate mean quarterly stationary values of the sky condition parameters into spatial distribution over Ghana. Results show that the dry periods (November to April) are characterised by high solar radiations, clear clouds and visible skies as a result of high temperatures, less cloud cover, and low relative humidity, whereas the opposite conditions are observed in the wet periods (May to October). The savannah and coastal zones recorded higher solar radiations with up to 22 MJ m−2 day−1 in the dry season compared to the forest and transition zones. Cloudiness index was above 0.3 and below 0.2 in the wet and dry seasons respectively, for the forest, transition and coastal zones, whereas the index was between 0.15 and 0.3 in the wet season and below 0.1 in the dry season for the savannah zone. Sky view factor was consistently between 0.7 and 0.8 in the dry season, with low values below 0.7 in August for all the climatic zones. Spatial interpolation of the sky condition parameters revealed that the savannah, coastal and eastern transition climatic zones observe high solar radiations, clear and visible skies and are therefore more favourable for harnessing solar energy. These locations may be more appropriate for setting up large-scale solar farms, though dust associated with the Harmattan dry season can be a hinderance.

Modeling the spatial distribution of Global Solar Radiation (GSR) over Ghana using the Ångström-Prescott sunshine duration model

Abstract Solar radiation is an important geological and meteorological parameter. In most developing countries, data is readily unavailable owing to lack of instrumentation and skilled personnel. In this study, Global solar radiation (GSR) over Ghana has been quantified using the Angstrom–Prescott sunshine model with sunshine duration data from 22 synoptic stations distributed across the country's ecological zones. The simulated data was gridded at 10 km by 10 km, establishing the spatial distribution of solar radiation over the country. Comparison with satellite data showed good results with root mean square error (RMSE) values of 1–5 MJm−2day−1 and correlation coefficient of 60 - 66%. Meanwhile, the estimated total GSR over the country was found to be 412.82 MJm−2day−1. The savanna zone had the maximum estimated total monthly mean GSR for the year, with the highest value of 20.76 MJm−2day−1 in Navrongo. The forest zone had the minimum estimated total annual mean GSR, with the lowest radiation level in Oda (17.11 MJm−2day−1). A maximum and minimum mean clearness index of 0.59 and 0.48 respectively are estimated, implying that about 53% of solar radiation at the top of the atmosphere reaches the study area after attenuation. The satellite data has a total monthly mean horizontal Global Solar irradiance of 366.62 MJm−2day−1. The study shows that the region is a potential field to harness and optimize solar energy for the operation of photovoltaic systems and solar collectors for industrial and domestic applications.

Calculated the diffuse and direct parts of global solar radiation in Baghdad city for the period (1983-2005) depending on clearness index by applying the two world models of Liu -Jordan

In this paper solar radiation was studied over a region of Baghdad (Latitude 33.3o and longitude 44.4o). The two parts of global solar radiation: diffuse and direct solar radiation were estimated depending on the clearance index of measured data (Average Monthly mean global solar radiation). Metrological data of measured (average monthly mean diffuse and direct solar radiation) were used to comparison the results and show the agreement between them. Results are determined by applying Liu and Jordan two models (1960). Excel 2007program is used in calculation, graphics and comparison the results.

Assessing the performance of global solar radiation empirical equations in Sokoto, Nigeria using meteorological parameters

In this study, the meteorological parameters measured in Sokoto (12.55 o N, 5.15 o E) for a period of 10 years (2005-2014) were obtained from the Nigerian Meteorological Agency Sultan Abubakar III International Airport, Sokoto state. The data used include Gunn-Bellani solar radiation, sunshine hour duration, maximum and minimum temperatures, which were analyzed using modified Angstrom models to estimate the monthly mean global solar radiation in Sokoto. Four statistical methods have been used in order to evaluate the results namely; Mean Bias Error (MBE), Mean Percentage Error (MPE), Root Mean Square Error (RMSE) and T-statistic. The standard error (SE) and coefficient of determination (R 2 ) were also obtained for each model. Based on the result obtained models 4 & 5 gave a lower RMSE, and R 2 approaches unity, which indicates that there is a good agreement between measured and estimated global solar radiation. http://dx.doi.org/10.4314/njt.v37i2.10

Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm

The demand for more efficient and environmentally benign, non-conventional sources of energy came into picture due to increasing demands for human comforts. Solar energy is now the ultimate option. In this paper, the instruments used to measure the solar radiation at Innovation Centre, MIT Manipal were connected to a Raspberry Pi to access the data remotely. Genetic Algorithms were formulated, so that the monthly mean global solar radiation in Manipal can be effectively estimated. Meteorological data such as humidity, temperature, wind speed, etc. were used as inputs to train the networks. A successful network was made between the data loggers and the Raspberry Pi. The data collected by the data loggers from the devices are transmitted to the Raspberry Pi which in turn sends the data to an internal server. The Raspberry Pi can be accessed using any SSH client such as PuTTY. The meteorological data was collected for the years 2010-2014 in order to formulate the Artificial Intelligence models. The validity of the formulated models were checked by comparing the measured data with the estimated data using tools such as RMSE, correlation coefficient, etc. The modelling of solar radiation using GA was carried out in GeneXpro tools version 5.0.

Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China

Solar radiation plays important roles in energy application, vegetation growth and climate change. Empirical relations and machine-learning methods have been widely used to estimate global solar radiation (GSR) in recent years. An artificial neural network (ANN) based on spatial interpolation is developed to estimate GSR in southeast China. The improved Bristow–Campbell (IBC) model and the improved Ångström–Prescott (IA–P) model are compared with the ANN model to explore the best model in solar radiation modeling. Daily meteorological parameters, such as sunshine duration hours, mean temperature, maximum temperature, minimum temperature, relative humidity, precipitation, air pressure, water vapor pressure, and wind speed, along with station-measured GSR and a daily surface GSR dataset over China obtained from the Data Assimilation and Modeling Center for Tibetan Multi-spheres (DAM), are used to predict GSR and to validate the models in this work. The ANN model with the network of 9-17-1 provides better accuracy than the two improved empirical models in GSR estimation. The root-mean-square error (RMSE), mean bias error (MBE), and determination coefficient (R2) are 2.65MJm−2, −0.94MJm−2, and 0.68 in the IA−P model; 2.19MJm−2, 1.11MJm−2, and 0.83 in the IBC model; 1.34MJm−2, −0.11MJm−2, and 0.91 in the ANN model, respectively. The regional monthly mean GSR in the measured dataset, DAM dataset, and ANN model is analyzed. The RMSE (RMSE %) is 1.07MJm−2 (8.91%) and the MBE (MBE %) is −0.62MJm−2 (−5.21%) between the measured and ANN-estimated GSR. The statistical errors of RMSE (RMSE %) are 0.91MJm−2 (7.28%) and those of MBE (MBE %) are −0.15MJm−2 (−1.20%) between DAM and ANN-modeled GSR. The correlation coefficients and R2 are larger than 0.95. The regional mean GSR is 12.58MJm−2. The lowest GSR is observed in the northwest area, and it increases from northwest to southeast. The annual mean GSR decreases by 0.02MJm−2decade−1 over the entire southeast China. The GSR in 52 stations experiences a decreasing trend, and 21% of the stations are significant at the 95% level.

Correlation of Global Solar Radiation of Eight Synoptic Stations in Burkina Faso Based on Linear and Multiple Linear Regression Methods

We utilize the multiple linear regression method to analyse meteorological data for eight cities in Burkina Faso. A correlation between the monthly mean daily global solar radiation on a horizontal surface and five meteorological and geographical parameters, which are the mean daily extraterrestrial solar radiation intensity, the average daily ratio of sunshine duration, the mean daily relative humidity, the mean daily maximum air temperature, and the sine of the solar declination angle, was examined. A second correlation is established for the entire country, using, this time, the monthly mean global solar radiation on a horizontal surface and the following climatic variables: the average daily ratio of sunshine duration, the latitude, and the longitude. The results show that the coefficients of correlation vary between 0.96 and 0.99 depending on the station while the relative errors spread between −3.16% (Pô) and 3.65% (Dédougou). The maximum value of the RMSD which is 312.36 kJ/m2 is obtained at Dori, which receives the strongest radiation. For the entire cities, the values of the MBD are found to be in the acceptable margin.

Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria)

Abstract In this study, 11 empirical models are developed correlating the monthly mean daily global solar radiation on a horizontal surface with monthly mean sunshine records and air temperature data for six Algerian cities (Algiers, Oran, Batna, Ghardaia, Bechar, and Tamanrasset). In order to indicate their performance, seven statistical parameters were introduced; coefficient of determination ( R 2 ), mean percent error (MPE), mean absolute percent error (MAPE), mean bias error (MBE), mean absolute bias error (MABE), and root mean square error (RMSE). The results obtained in this study confirm the previous studies, which have indicated that the sunshine based models are generally more accurate than air temperature based models. According to the results, the best performances are obtained by the cubic and the quadratic regression models for the six Algerian stations. Moreover, these two regression models can be used for the proposed generalized models for predicting the monthly mean global solar radiation in other Algerian locations in the absence of the measured solar radiation data.

Sunshine-based global radiation models: A review and case study

Many empirical correlations and models have been developed to estimate solar radiation around the world. The most commonly used parameter for estimating global solar radiation is sunshine duration because it can be easily and reliably measured, and data are widely available. In this paper, we report the performance of 52 sunshine models for the estimation of the monthly mean global solar radiation on horizontal surfaces in Jouf, Saudi Arabia (29°47′N, 40°06′E, altitude 670m). Some models are totally unsuitable for use in this region, and others vary in performance. The best models are identified.

Estimation of Monthly Mean Daily Global Solar Radiation over Bangkok, Thailand Using Artificial Neural Networks

Thailand is located in an equatorial belt that receives abundant solar energy. In order to achieve the optimum utilization of solar energy available, it is necessary to evaluate the incident solar radiation over the region of interest. Solar radiation can be assessed by means of measurements or mathematical modeling. Accurate measurements, using sophisticated and costly equipment, is available and has indeed been used extensively to assess solar radiation. This paper concentrates on an alternative approach to assess global solar radiation (GSR) by using Artificial Neural Networks (ANNs) together with classical observed meteorological data. The model is applied to the region of Bangkok, Thailand, using meteorological data, along with solar radiation measurements, for the period 2001-2010 from the Thai Meteorological Department (TMD). More precisely, three combinations of observed monthly mean meteorological data, i.e. maximum, minimum, and mean temperatures; relative humidity; rainfall amount; and sunshine hours were used with 3, 5 and 6 parameters as the model input for the ANN training to predict the solar radiation over the territory. A feed-forward back-propagation ANNs were trained based on three algorithms, i.e. the Quasi-Newton, the conjugate gradient with Polak-Ribiére updates and the Bayesian regularization. The root mean square error (RMSE) and the mean bias error (MBE) between the observed and the predicted solar radiations in 2011-2012 were computed in order to investigate the performance of the ANNs. Results showed that, for monthly mean number of sunshine hours in the range of 3.58 to 9.55 hr/day, the monthly mean GSR above the atmosphere of Bangkok was in the range of 5.64 to 22.53MJ/m2/day. The RMSE and the MBE were 0.0031 - 0.3632 and -0.0203 - 0.003, respectively, thus indicating that the ANN modeling has sufficient performance to predict the monthly mean GSR over an area where classical meteorological data are measured.

Introducing the best model for predicting the monthly mean global solar radiation over six major cities of Iran

In this study, by using long-term global solar radiation data and other meteorological parameters, 11 empirical models taken from the literature were tested for prediction of monthly mean daily global solar radiation over six major cities of Iran, named Isfahan, Karaj, Mashhad, Shiraz, Tabriz and Tehran. The models are from 3 categories: (1) Only function of sunshine duration; (2) Function of sunshine duration as well as relative humidity and ambient temperature; (3) Independent of sunshine duration and function of relative humidity, ambient temperature and its maximum and minimum. The models were established using statistical regression technique and their accuracies evaluated using the statistical indicators of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE); hence the best model in each category was recognized. Although, 5 out of the 6 nominated cities fell in climatic classification of BSk, it was not possible to introduce a single model even for the cities having the same climate type. However, for all of the cities the best model was either from categories (1) or (2). In line with increased tendency toward installation of solar systems in Iran, the proposed models are useful for simulations and incorporation in design procedures.

A Statistical Approach on the Estimation of Solar Radiation in Bishkek, Kyrgyzstan Using Geographical and Meteorological Data

In this study, first, the usability of clear sky radiation for predicting the average solar radiation, and the effect of geographical and meteorological parameters on the monthly mean global solar radiation in Bishkek, Kyrgyzstan were investigated. A multiple linear regression was applied to nine geographical and meteorological data sets, which were monthly mean clear sky and extraterrestrial radiation, the ratio of bright sunshine hours to day length, ambient and soil temperatures, humidity, sine of declination angle, the ratio of and . The solar radiation estimated from the models was compared with the meteorological values by using t-test and F-statistic, mean bias error, and root mean square error.

A Systematic Study on the Monthly Mean Global Solar Radiation between Latitudes 65S and 65N

In this work, the monthly average daily global solar radiation reaching the earth on a horizontal surface was analyzed and compared. We used data of 115 surface stations, in a wide region of latitudes between 65S and 65N and having various climate types. The aim of this work is to investigate the climate types effects on the solar energy budgets and characterize the basic geographical and climatological parameters.

Parameterization of a simple model to estimate monthly global solar radiation based on meteorological variables, and evaluation of existing solar radiation models for Tabouk, Saudi Arabia

Using 9 years of solar radiation data, we established a simple model to calculate the monthly mean global solar radiation on a horizontal surface in Tabouk (28.38° N, 36.6° E, Saudi Arabia). The model correlates the global solar radiation with five meteorological parameters. These parameters are the perceptible water vapor, air temperature, relative humidity, atmospheric pressure, and the mean monthly daily fraction of possible sunshine hours. The estimated global radiation from the model was compared with the measured values using the mean bias error (MBE), coefficient of correlation ( R), root mean square error (RMSE), and mean percentage error (MPE). The t statistics were also applied as another indication of suitability. The model has a high coefficient of correlation ( R = 0.99), MBE = −14 × 10 −4 kW h/m 2, RMSE = 0.10 kW h/m 2, and MPE = −0.03%. It is believed that the model developed in this work is applicable for estimating, with great accuracy. The monthly mean daily global radiation at any site having similar conditions to those found in Tabouk. Furthermore, 29 regression models available in the literature were used to estimate the global solar radiation data for Tabouk. The selected models were different in terms of the variables they use and in the number of the variables they contained. The models were compared on the basis of the statistical errors considered above. Apart from Abdall’s model, which showed a reasonable estimate (MPE = −2.04%, MBE = −0.22 kW h/m 2, and RMSE = 0.59 kW h/m 2), all the models under or overestimate the measured solar radiation values. Comparisons between these models and the produced model, from this study, were also considered. According to the statistical results, the model of Abdall showed the prediction closest to those estimated using the developed model.

General Formula for Estimation of Monthly Mean Global Solar Radiation in Different Climates on the South and North Coasts of Iran

Using sunshine duration, cloud cover, relative humidity, average of maximum temperature, and ground albedo as the input of several radiation models, the monthly average daily solar radiation on horizontal surface in various coastal cities of the South (25.23∘N) and the North (38.42∘N) of Iran are estimated. Several radiation models are tested and further are revised by taking into consideration the effects of relative humidity, ground albedo, and Sun-Earth distance. Model validation is performed by using up to 13 years (1988–2000) of daily solar observations. Errors are calculated using MBE, MABE, MPE, and RMSE statistical criteria (see nomenclature) and further a general formula which estimates the global radiation in different climates of coastal regions is suggested. The proposed method shows a good agreement (less than7%deviation) with the long-term pyranometric data. In comparison with other works done so far, the suggested method performs a higher degree of accuracy for those of two regions. The model results can be extended to other locations in coastal regions where solar data are not available.

Change of Dissolved Gaseous Mercury Concentrations in a Southern Reservoir Lake (Tennessee) Following Seasonal Variation of Solar Radiation

A 12-month field study was conducted consecutively from June 2003 to May 2004 to quantify temporal variations of dissolved gaseous mercury (DGM) concentrations in Cane Creek Lake, a southern reservoir lake (Cookeville, TN). Diurnal changes of DGM concentrations in two periods (morning increase vs afternoon decrease with an around-noon peak) were observed, and the changes closely followed daily solar radiation variation trends. The diurnal patterns prevailed in the late spring and summer, but became vague in the late fall and winter. The monthly mean DGM concentrations peaked at 40.8 pg L(-1) in July and reached the lowest at 14.2 pg L(-1) in December and 21.9 pg L(-1) in January; this DGM concentration change closely followed the monthly mean solar radiation variation trend. The increase of the lake DGM concentration from January to July and its decrease from July to December mirror the typical daily rhythm of DGM concentration variations in the two periods. This finding supports the following hypothesis: The natural phenomenon of daily oscillation of freshwater DGM concentrations that follows diurnal solar radiation variation would manifest on a seasonal scale. High DGM concentrations were found in the spring and summer and low in the fall and winter (seasonal mean: 34.2, 37.5, 20.0, 24.4 pg L(-1), respectively). This seems to suggest an annual occurrence of two periods of the seasonal DGM level fluctuation (spring and summer high vs fall and winter low DGM levels). Linear relationships of the monthly mean DGM concentrations were found with the monthly mean global solar radiation (R2 = 0.82, P < 0.05) and UVA radiation (R2 = 0.84, P < 0.05). Linear relationships of the seasonal mean DGM concentrations were also found with the seasonal mean global solar radiation (R2 = 0.85, P = 0.08) and UVA radiation (R2 = 0.93, P < 0.05).

04/00857 Light trapping in spherical silicon solar cell module: Toshiro, M. and Hiroshi, M. Solar Energy Materials and Solar Cells, 2003, 79, (2), 113–124

In this study, 11 empirical models are developed correlating the monthly mean daily global solar radiation on a horizontal surface with monthly mean sunshine records and air temperature data for six Algerian cities (Algiers, Oran, Batna, Ghardaia, Bechar, and Tamanrasset). In order to indicate their performance, seven statistical parameters were introduced; coefficient of determination (R2), mean percent error (MPE), mean absolute percent error (MAPE), mean bias error (MBE), mean absolute bias error (MABE), and root mean square error (RMSE).The results obtained in this study confirm the previous studies, which have indicated that the sunshine based models are generally more accurate than air temperature based models. According to the results, the best performances are obtained by the cubic and the quadratic regression models for the six Algerian stations. Moreover, these two regression models can be used for the proposed generalized models for predicting the monthly mean global solar radiation in other Algerian locations in the absence of the measured solar radiation data.

A study for estimating solar radiation in Elaziğ using geographical and meteorological data

In this study, the effect of geographical and meteorological parameters on the monthly mean global solar radiation in Elaziğ was investigated. A multiple linear regression was applied to six geographical and meteorological data sets, which were monthly mean extraterrestrial radiation, the ratio of ‘bright sunshine hours to daylength’, ambient and soil temperatures, humidity and sine of declination angle. The global solar radiation estimated from the models was compared with the values measured by the Kipp–Zonen pyranometer between April 1994 and March 1995. It has been determined that these equations which express the 99th percentile of the incident solar radiation, have a −9% deviation from our measured values.