In many applications of solar energy, the most important parameters that are often needed are the average globalsolar irradiation and its components. This estimates the amounts of monthly average solarradiation from more readily available meteorological parameters such as the sunshine duration, extraterrestrial radiation. Several empirical models have been developed to calculate globalsolarradiationusing various parameters. Angstrom (1924) developed the earliest model used for estimatingglobalradiation, in which the sunshine duration data and clear sky radiation data were used. The solarradiation reaching the Earth’s surface depends upon climatic conditions of a location, which is essential to the prediction, and design of a solar energy system. GSR has been the focus of many studies due to its importance in providing energy for Earth’s climate system (Falayi et al., 2011). The other method to estimate solarradiation was obtained by using atmospheric transmittance model (Cambell and Norman, 1998) while other authors have used diffuse fraction (Reindl et al., 1990) and clearness index models (Battles et al., 2000) Parametric or atmospheric transmittance model requires details atmospheric characteristic information (Wong and chow, 2001). This model gives high-accuracy for clear sky/cloudless conditions, which is leading some author to use this model to evaluate the performance of an empirical model under cloudless conditions (Battles et al., 2000). There are numerous authors proposed this kind of model as mentioned in (Gueymard, 2003). However, pure parametric model was not used in this study, since there is no detail atmospheric condition data for the site. Meteorological parameters frequently used as predictors of atmospheric parameters since acquiring detail atmospheric conditions require advance measurement. Meteorological parameters such as sunshine duration, cloud cover, ambient temperature, relative humidity, and precipitation data have been used to estimate atmospheric transmittance coefficient in parametric model. This kind of model is called meteorological model (WMO, 1981).
Twelve parametric models related to the sunshine hour, relative humidity, and temperature parameters were estimated and their predictive ability was evaluated by employing the test-train approach for the cross-validation and error analysis. Monthly minimum and maximum temperature; mean hourly solarradiation, sunshine hour, and relative humidity data for a long period, 25 years obtained from observatory station in Nigeria Meteorology Agency, Oshodi, Lagos Nigeria were used in this study. 20 years of data (1980-2000) was used to train the model while 5 years of data (2001-2005) was used for testing the performance of the models. Model 1 shows the original linear sunshine hour based model proposed by Angstrom and Prescott ([Ang24]; [Pre40]) Models 2 and 3 are the quadratic and polynomial form of the latter respectively. Models 4-7 shows the proposed exponential and logarithmic form of the sunshine hour parameter by Ampratwum, Togrul and others ([AD99]; [TT02]), while Models 7-10 are the linear temperature-based models proposed by Hargreaves and Samani, Garcia and others ([HS82]; [Gar40]). We presented the multiple regression models, which involves the combination of the climatic variable are presented in Models 11 and 12. The extraterrestrial solarradiation ( ) was obtained using the site-specific input such as latitude ( ) and sunset hour angle ( ) of the location of study. The extraterrestrial daily solarradiation ) is determined by the sets of equations given below.
(surface azimuth, surface tilt angle, solar altitude, solar azimuth); physical factors (albedo, scattering of air molecules, water vapor content, scattering of dust and other atmospheric constituents); and meteorological factors (atmospheric pressure, cloudiness, temperature, sunshine duration, air temperature, soil temperature, relative humidity, evaporation, precipitation, number of rainy days, total perceptible water, etc.) (Alsamamra, 2013; Ruiz-Arias et al., 2010; Menges et al., 2006; Almorox et al., 2008 and Angstr¨om, 1924). However, the reliability and usability of these models depend largely on the strength of the correlation between the estimated and measured variables. Parameters that have been most frequently investigated are sunshine, cloud cover; temperature and/or precipitation variables. Solarradiation can be easily estimated from sunshine duration; the Angstrom-Prescottmodels are sunshine-based and have widely applied to estimate globalsolarradiation (Prescott, 1940; Hargreaves and Samani, 1982 and Annandale et al., 2002). However, sunshine and cloud observations are data that are not available at most of the meteorological stations. In this context, globalsolarradiation estimation models based on air temperature and precipitation are attractive and viable options. It is necessary to develop a precise solarradiation model which utilizes commonly available parameters such as maximum and minimum temperatures, precipitation and geographical location. These parameters are the only daily variables available at a great majority of meteorological stations. Some of these approaches make use of basic meteorological data only (Bristow and Campbell, 1984; Donatelli and Campbell, 1998; Goodin et al., 1999; Winslow et al., 2001; Mahmood and Hubbar, 2002; McCaskill, 1990; Hunt et al., 1990; Liu and Scott, 2001; Richardson and Reddy, 2004; Chen et al., 2006; Skeiker, 2009 and Wu et al., 2007).
meteorological parameters as model input. Furthermore, in order to evaluate the accuracy and applicability of the models reported in the literature for computing the monthly average globalsolarradiation on a horizontal surface, the geographical and mete- orological data of Yazd city, Iran was used. The developed models were then evaluated and compared on the basis of sta- tistical error indices and the most accurate model was chosen in each category. Results showed that all the proposed corre- lations have a good estimation of the monthly average daily globalsolarradiation on a horizontal surface in Yazd city. However, they reported that the El-Metwally sunshine-based model predicts the monthly averaged globalsolarradiation with higher accuracy. Quansah et al. (2014) proposed a sun- shine hour-based empirical model and an air temperature-based empirical model for the globalsolarradiation estimation in the Ashanti region of Ghana. Seven models were used for the evaluation process, by exploiting the Angstrom–Prescott model and Hargreaves–Samani model. The experimental anal- ysis showed that the suggested Angstrom–Prescott model underestimated the globalsolarradiation in April–June and October–November. However, it overestimated the globalsolarradiation in August, September and December. As the input data for the suggested models were site specific, the estima- tion of the globalsolarradiation was complex. Further, the suggested models were not suited for measuring the long- term solarradiation. Because of this, most of the researchers attempted to develop numerous approaches, using the meteo- rological parameters for solarradiation prediction. One of the prediction or forecasting approaches being followed in recent times is the artificial intelligent techniques to predict the solarradiation.
Angstrom  proposed first theoretical model for estimatingglobalsolarradiation based on sunshine duration. Page  and Prescott  reconsidered this model in order to make it possible to calculate monthly average of the daily globalsolarradiation on a horizontal surface from monthly average daily total insolation on an extraterrestrial horizontal surface. Tiris et al.  for Turkey, Bahel et al.  for Bahrain, Zabara  for Greece, Almorox et al.  for Spain, Samuel  for Sri Lanka, Newland  and others have developed the modified versions of fundamental Angstroms empirical relations based on sunshine duration. Allen , Hargreaves , Bristow and Campbell , Chen et al.  and others have proposed the estimate model based on temperatures. Multi parameter models (MPM) were given by Trabea et al.  for Egypt, Ojosu et al.  for Nigeria, Garg and Garg  for India, Lewis  for Zimabwe, El-Metwally  for Egypt and Inci Togrul et al.  Elazig for Turkey and  for Krygyzstan, for etc., for estimating the globalsolarradiation based on longitude, latitude, altitude and routinely available metrological parameters such as minimum and maximum temperature, relative humidity, rainfall, cloudiness and wind speed data. Iranna et al. [21-25] have explored the estimation model for India, Asia, Africa, World and observed usefulness of these meteorological parameters for GSR estimation.
Also, Fuzzy based techniques (Abdelouhab Zeroual and based on Takagi-Sugeno models provides a better forecast accuracy. A comparative study of prediction using Fully Recurrent Neural Network (FRNN) and RBF 2014) proved that a better accuracy Several algorithms (Abdelaziz cham El Badoui, 2013) like Gradient Descent back propagation, Gradient Descent with Adaptive back propagation, Gradient Descent with momentum back propagation and LM back propagation algorithms are used for networks and their performance are compared. All the above algorithms have been proposed cannot be used
It is known that the atmosphere exerts a redistribution effect of the radiation that receives from the sun, for example, on a very clear day; a relatively small part becomes diffuse radiation, whereas most of it remains as direct. On the other hand, on a cloudy day, the redistribution of radiation is much more noti- ceable. Dense clouds have a very high albedo (reflected energy fraction), which makes a large part of the solarradiation reflected in the outer space on a densely cloudy day. In addition, the energy that manages to pass through the clouds is only diffuse radiation.
The solarradiation has temporal and spatial variations. To collect this information, a network of solar monitoring stations equipped with pyranometers and data acquisition systems are generally established in the desired locations. However, the number of such stations in the network is usually not sufficient to provide solarradiation data of the desired areas, especially in developing countries. This is mainly due to because of not being able to afford the measuring equipments and techniques involved. Therefore, it is necessary to develop methods to estimate the solarradiation on the basis of the more readily available meteorological data.
reproductive age group women at Goba and Robetown of Bale zone; Oromia Region, South East Ethiopia. A cross sectional study with Simple Random sampling was employed to include 340 eligible subjects. The WHO self reporting questionnaire with 20 items with a cut off point 6 and above was used to separate non-cases/cases of perinatal depression. Data were collected by trained data collectors. Descriptive analysis was done using SPSS Version 16. Multivariate logistic regression was used to identify independent predictors of perinatal depression at 95% CI and P value of ≤ 0.05.
landscape scales. Considerable insolation variation exists between different landscape positions. Such variations have a significant effect on the ground energy balance, water balance, and nutrient cycles, which directly or indirectly affect natural processes and human activities. As an example of its application, we are using the Solar Analyst and meteorologic measurements in models of spatial patterns of microclimate factors, including air temperature, soil temperature, and soil moisture. This topoclimatic modeling approach can lead us to a better understanding of the relationship between microclimate and vegetation distribution patterns, as well as potential habitat shifts under climate change scenarios. Preliminary analyses demonstrate that commonly used techniques (generalization from nearby insolation monitoring, prediction by slope and aspect categories, estimating direct radiation from direct duration, etc.) are not sufficiently accurate for generalization of insolation patterns to a landscape scales. The Solar Analyst serves as a powerful tool for analyzing spatial and temporal patterns of insolation at local and landscape scales. Applications span a broad range of fields, including forestry, agriculture, hydrology, micrometeorology, environmental assessment, and ecological research. The Solar Analyst also promises to be useful in engineering and design fields, for such applications such as site assessment, building design, solar collector design, and topographic radiometric correction for remote sensing.
In addition, the values of the average daily globalradiation in the solar energy applications are the most important parameter, measurements of which are not available at every location due to cost, maintenance, and calibration requirements of the measuring equipment. In places where no measured values are available, a common application has been to determine this parameter by appropriate correlations which are empirically established using the measured data . Several empirical models have been used to calculate solarradiation, utilizing available
In Yucatán Peninsula, many areas of agricultural importance have no solarradiation records, and the lack of solarradiation data restricts the design of photovoltaic and irrigation systems, both of which are fundamental aspects for economic development and sustainable use of natural resources. Recently, Quej et al. (2016) calibrated 13 empirical models to estimate daily solarradiation in six sites of Yucatán Peninsula, However, these models require several input parameters such as minimum air temperature, maximum air temperature, precipitation and relative humidity. There are many regions in the Yucatán Peninsula where there are no weather stations, so such input parameters are not available. In this context, the use of DYB models to estimate solarradiation provides an effective alternative to estimate daily solarradiation. In this study, four DYB models from the literature and a newly developed model that estimate daily globalsolarradiation over the Yucatán Peninsula were evaluated. The overall result of this investigation indicated that the new model, in which a sum of two Gaussian correlation formulas was used, performed best for six locations on the Yucatán Peninsula. Moreover, according to seasonal analysis, despite the existence of rain events or persistent cloud cover during the summer and autumn, daily solarradiation was estimated with acceptable accuracy with only day of the year as an input parameter.
With the rapid depletion of fossil fuel reserves, it is feared that the world will soon run out of its energy resources. This is a matter of concern for the developing countries whose economy heavily depends on imported petroleum products. Under these circumstances it is highly desirable that alternate energy resources should be utilized with maximum conversion efficiency to cope with the ever increasing energy demand. Among the non-conventional energy resources, solar energy, wind energy and biomass has emerged as most prospective option for the future 6 . In addition that, aggressive consumption rate of fossil fuels has created unacceptable environmental problems such as greenhouse effects, which may lead to disastrous climatic consequences. Thus, renewable and clean energy such as that obtained by usingsolar cells is required to maintain the quality of human life as well as the environment 7 . Detailed information about the availability of solarradiation on horizontal surface is essential for the optimum design and study of solar energy conversion system. The best way of knowing the amount of GlobalSolarRadiation (GSR) at any given site is to install solarimeter or other instrument at many locations in the given region and look after their day to day maintenance and recording which is a very costly exercise.
This has led to the emergence of many theoretical models with the major aim of estimating the amount of global SR on various surfaces, especially for those areas with differing climatic variables. Sunshine duration, ambient temperatures, humidity, cloud cover and wind speed are some of the variables that are normally considered [12 - 19]. Other notable studies include those of Jamil and Akhtar  and Guillou et al . Jamil and Akhtar  conducted a global and diffuse SR comparison in India. In particular, the case of a typical subtropical climatic region between Aligarh and the neighboring capital city of New Delhi was undertaken. The average annual globalradiation of Aligarh was given as 22.12 MJ/m 2 per day, while the average annual diffuse radiation was 7.92 MJ/m 2 per day. The result indicates that good solar energy and energy utilization potentials exist in the area. Guillou et al  investigated the accuracy of two commonly used clear sky models based on wet season in South Africa and compared it with experimental data using Fluent software. The average global irradiance for the fair weather condition and measured data correlated appreciably well.
Some of the method for deriving solarradiation from satellite observations employed meteorological geostationary satellite images. The geostationary satellites which are orbiting at about 36,000 km can offer a temporal resolution of up to 15 min and a spatial resolution up to 1 km but the satellites are not able to measurements accurately near mountains, oceans or other large bodies of water. Some limitations of satellite images limit the coverage and applicability of forecasting models (Inman, Pedro, and Coimbra 2013). In current days, more number of weather forecast models is used with spatial resolutions of a few kilometers. Some scientist and researchers are trying to concentrate on implement models with even higher spatial resolution to minimize the forecasting errors. To achieve the goal various soft computing models are reviewed, among this an artificial neutral network technique are selected to provide a large potential to improve the forecasts with high accuracy.
Abstract: Assessment of GlobalSolarRadiation Absorbed in Maiduguri, Nigeria was carried out in order to assist researchers in the field of solarradiation especially in developing countries that are increasingly faced with a serious data constraint. This will prove useful to many fields of study that rely on atmospheric energy input. The irradiation data was measured with the aid of constructed solar cell-based Pyranometer called the Reliable Model Pyranometer (RMP 002) at promising site. Insolation data at 1 min intervals were recorded for Maiduguri, Nigeria, using data loggers. The data were stored in a propriety binary format and later saved as text files that were imported into excel. From the data obtained, it was observed that maximum values of insolation occurred during the winter period while the minimum occurred during the summer period.
Energy is important to all development processes. The rate of energy consumption today is taken as indicator civilization which really helps to improve the quality of life of the people. The energy consumption is directly linked with all round development of the people. In the contest of our country Nepal, about 75% of the total populations still live in rural areas and they don’t have access to modern forms of energy like petroleum product, hydroelectricity and solar photo voltaic and so on. For lighting purpose, they should use the kerosene lamp. Living standard of remote rural people is difficult. At the remote areas of our country the cost of one litre kerosene is more than NRs 100 to NRs 200 in 2010. According to this data, the prices of the petroleum product are beyond the capacity of the rural
This research work focused on the variability of globalsolarradiation over the area of Abakaliki,Ebonyi State (6 o 20’N, 8 o 06’E) located in South Eastern part of Nigeria for the rainy and dry seasons. The Pyranometer used for this measurement was locally developed and calibrated against a standard pyranometer, it competed favorably with the standard Einstrain Lungs Sensor. The globalsolarradiation was measured every five minutes from 08:00hours to 18:00hours during the dry season 2011 and rainy season in 2012. The measurements were carried out near the New Physics Laboratory Complex Ebonyi State University Abakaliki, Nigeria. Maximum Irradiances of 1095.10Wm -2 and 689.48Wm -2 recorded in Abakaliki during dry and rainy seasons respectively occurred between 12:00 – 14:00hours local time, whereas the minimum values of 9.20Wm -2 and 9.86Wm -2 respectively are recorded during the sunrise and sunset. Partly cloudy conditions in Abakaliki cause conspicuous oscillations in globalsolarradiation. This can be attributed to multiple reflections by nearby cloudy layers.The seasonal difference in the observed globalsolarradiation is 405.62Wm -2 . Therefore solar energy devices can operate continuously in Abakaliki for up to 10 hours in a solar day from 8:00hours to 18:00hours which was the period covered during this investigation.
The main objective of this work is to predict average daily globalsolarradiation (GSR) without any measuring instruments , in future time domain for Madurai city, located in Tamilnadu (India) by using Standard multilayered feed-forward, back-propagation neural network with Levenberg- Marquardt (LM) training algorithm and Gradient descent back propagation (GD) algorithm. In order to train and test the neural network, three different artificial neural network models are developed, based on daily average meteorological data like maximum ambient air temperature, minimum ambient air temperature and minimum relative humidity values for predicting globalsolarradiation. The measured data were randomly selected for training, validation and testing the neural network. The results from the three artificial neural network models shows that using the minimum air temperature and day of the year outperforms the other cases with absolute mean percentage error of 5.36% and mean square error of 0.006 when training was done by using LM back propagation learning algorithm. From the results it is very clear that neural network is well capable of estimating GSR from simple and available meteorological data. It is expected that the models developed for daily globalsolarradiation will be useful to the designers of energy-related systems as well as to those who need to estimate the daily variation of globalsolarradiation for the specific location in Tamilnadu (India).
The global monitoring network of ground-based ozone measurements consists mainly of Dobson and Brewer spectrophotometers. These instruments provide total ozone column amounts derived from measurements of direct solar UV radiation. Analysis of total ozone measurements from the global monitoring network shows a nearly linear downward trend from 1980 to mid 1990s. Data after mid 1990s indicate that global ozone is no longer decreasing. UV monitoring stations report erythemal UV irradiance levels in terms of UV indices. UV index levels up to 20 have been measured at high altitude stations located at low latitudes. Continuous reliable spectral UV measurements started in the late 1980ies, and, therefore, the existing time series for UV radiation are too short for estimations of global trends.