Mean Daily Global Solar Radiation Research Articles

MULTIVARIATE MODELS FOR PREDICTING GLOBAL SOLAR RADIATION IN JOS, NIGERIA

This study developed two to six multivariate regression equations that reliably predict global radiation in Jos (Latitude 9.87 °𝑁 and Longitude 8.75 °𝐸). Thirty-one years (1980 – 2010) observed monthly mean daily global solar radiation, sunshine hours, maximum and minimum temperatures, cloud cover, rainfall, relative humidity and wind speed data were used in this study with the clearness index as the response variable and other variables as predictors. The seven validation indices employed are the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) to determine the reliability, suitability and applicability of the developed models. The results in this study revealed that all the developed multivariate models were found reliable for global solar radiation estimation in Jos depending on the obtainable meteorological data measured in the location. The correlation between the measured and predicted (developed) global solar radiation shows a perfect correlation as depicted from the figures.

Statistical Modelling Of Global Solar Radiation On Horizontal Surface Using Monthly Means Daily Sunshine Hours And Some Climatic Variables For Zamfara State, Nigeria

In this study, four empirical models on the basis of the linear Angstrom- Prescotte model for predicting monthly mean daily global solar radiation on the horizontal surface were developed, correlating solar radiation to climatic parameters of sunshine hours, temperature and relative humidity. The models were used to predict Global solar radiation, an average solar radiation of 16.9690 Mjm-2 day-1 and maximum and a minimum of 19.2647 and 13.5222 C 0 were respectively predicted. For validation and the accuracy of the models, statistical coefficient of determination, R2, mean percentage error, (MPE), mean bias error, (MBE), and root mean square error (RMSE) were used. The result showed that, model correlating solar radiation with Sunshine hours, temperature and relative humidity gives the best fit for the study location with R2= 83.89%.

One month-ahead forecasting of mean daily global solar radiation using time series models

Accurate forecasting’s of solar radiation are crucial to quantifying future solar power production or integration. The integration of solar energy into the electricity grid has become predominant among researchers and scientists due to the current demand for energy as well as the depletion of fossil fuel reserves and environmental impacts. This paper aims to introduce a method for multi month-ahead forecasting (one, two and three month ahead) of monthly mean daily global solar radiation time series and forecasting of a large scale solar radiation data driven. ARMA and ARIMA models are used to predict the coming value of the global solar radiation time series. Both models are applied to stationary and non-stationary time series of the solar irradiation data. Taken into account the advantage of ARMA and ARIMA models, the two models are compared in terms of goodness-of-fit value determining by the AIC and BIC criterion. As a result, the ARMA (2, 1) and ARIMA (0, 2, 1) are selected as optimal models due to the minimize values of AIC and BIC criterion. Based on the statistical error value, ARIMA (0, 2, 1) models give good accuracy in three period ahead forecasting consecutive in comparison with ARMA (2, 1). The lower value of the optimum model indicates that ARIMA (0, 2,1) is more suitable to forecast monthly mean daily global solar radiation for Tetouan city and may be for other locations of similar weather conditions.

Models for forecasting monthly mean daily global solar radiation from in-situ measurements: Application in Tropical Climate, India

Radiation availability varies with reference to the geometrical location. It is necessary to develop the models to predict the daily global solar radiation (GSR) for the locations where there is a huge potential of radiation exists and to improve the utilization of energy. This study aimed to develop the solar radiation models with readily available input parameters for Tiruchirappalli, which covers an annual mean daily global solar radiation of 4.50kWh/m2-day.Exponential model (EM), modified exponential model (MEM), power series model (PSM), Hargreaves and Samani model (HSM), Hargreaves-model (HGM) and Hunt models (HUM) which uses air temperature and extra-terrestrial radiation on a horizontal surface as input parameters were developed and validated. Dumas model which uses air temperature and maximum possible sunshine duration (So) as input parameters was also verified. Dumas model was modified as hybrid models to check for their applicability. The performance of the models was analyzed by using the statistical tools. Out of all the models, HSM and PSM had shown an accurate estimation of daily GSR (errors: 6.62% & 7.95% respectively) for the selected location. Dumas model's performance was also found satisfactory (error: 11.02%).

Development and Analysis of a New Solar Radiation Atlas for Argentina from Ground-Based Measurements and CERES_SYN1deg data

Currently, quantifying global solar radiation at surface in Argentina is crucial for the development of projects related to solar energy, calculation of evapotranspiration and eco-sustainability architecture, among other environmental issues. In recent years, several models have been developed to estimate the solar energy resources by means of various techniques, e.g. satellite imaging, kriging, or Artificial Neural Networks. The use of satellite data allows for a better spatial representation, being of great relevance in areas with lack of terrain measurements. In this paper, we use the CERES_SYN1deg to develop a new Global Solar Radiation Atlas for Argentina. In this study, we developed maps of annual and monthly mean daily global solar radiation using CERES_SYN1deg data between 2000 and 2016. In order to validate the global solar radiation data provided by CERES_SYN1deg, they were compared with ground-based measurements in the time overlap of both instruments, in four monitoring sites of the SAVER-Net project and an additional site in Tandil, which belongs to the Remote Sensing Group of IHLLA. The maps show the spatial and temporal variation of global solar radiation in Argentina. Comparisons with ground-based pyranometers reveal relative differences of around 3% at a monthly scale for all sites, while the biases can be neglected. Therefore, it is possible to conclude that the maps could be very useful for different technical and scientific purposes, and the comparison with ground-based data demonstrates CERES_SYN1deg’s reliability.

New horizontal global solar radiation estimation models for Turkey based on robust coplot supported genetic programming technique

Renewable energy sources have been attracting more and more attention of researchers due to the diminishing and harmful nature of fossil energy sources. Because of the importance of solar energy as a renewable energy source, an accurate determination of significant covariates and their relationships with the amount of global solar radiation reaching the Earth is a critical research problem. There are numerous meteorological and terrestrial covariates that can be used in the analysis of horizontal global solar radiation. Some of these covariates are highly correlated with each other. It is possible to find a large variety of linear or non-linear models to explain the amount of horizontal global solar radiation. However, models that explain the amount of global solar radiation with the smallest set of covariates should be obtained. In this study, use of the robust coplot technique to reduce the number of covariates before going forward with advanced modelling techniques is considered. After reducing the dimensionality of model space, yearly and monthly mean daily horizontal global solar radiation estimation models for Turkey are built by using the genetic programming technique. It is observed that application of robust coplot analysis is helpful for building precise models that explain the amount of global solar radiation with the minimum number of covariates without suffering from outlier observations and the multicollinearity problem. Consequently, over a dataset of Turkey, precise yearly and monthly mean daily global solar radiation estimation models are introduced using the model spaces obtained by robust coplot technique and inferences on the sensitivity of the amount of global solar radiation to covariates and the magnitude and direction of effect of covariates on the global solar radiation are drawn.

A statistical comparative study to demonstrate the merit of day of the year-based models for estimation of horizontal global solar radiation

This study deals with identifying whether the accuracy of the day of the year-based (DYB) models for prediction of the horizontal daily global solar radiation is as competitive as the frequently utilized models from the sunshine duration-based (SDB) and temperature-based (TB) categories. The city of Birjand, located in the sunny belt region of Iran and the capital of South Khorasan province, has been considered as a sole case study. As of the nominated categories, 12 models have been selected from the literature; then by utilizing the long-term measured data and via statistical regression techniques the models have been established and their performances evaluated through several statistical indicators. Furthermore, to identify the suitability of the DYB models for monthly mean daily estimation, new regression constants have been developed for all of the nominated models and their performances compared. The results showed the superiority of the DYB models compared to TB models, but with a minor difference the best model was from the SDB category. However, owing to accurate estimation, simplicity and more or less similar performance as SDB models, DYB models seem qualified as proper alternatives of SDB models. Although in this study the best DYB models for daily and monthly mean daily horizontal global solar radiation estimations have been recommended for utilization in Birjand, due to similar climate conditions the results are applicable for the whole South Khorasan province and its vast neighboring region.

ARIMA and regression models for prediction of daily and monthly clearness index

Hourly and daily measurements of total and diffuse solar radiation and sunshine duration are analyzed. Three-year measurements of the available meteorological data in Mosul (latitude 36° 19′ N, longitude 43° 09′ E and 223 m above mean sea level) are used in this study. The present work involves two parts; in the first one, monthly mean daily and hourly of total and diffuse solar radiation are analyzed. The results show that the annual mean of the daily total and diffuse solar radiation is 5.11 and 1.6 kWh/m2 respectively. 57% of the days of the year are clear, while only 11.5% of the days are cloudy. Several empirical equations for estimating monthly mean daily global and diffuse solar radiation have been developed and compared with other available models. Ratio of average hourly to daily total solar radiation, for each month of the year is studied and compared with the theoretical results. In the second part, time-series model building using Box–Jenkins procedure to the daily clearness index is performed. The ARIMA(2,1,1) is developed for predication of daily clearness index.

Time-Series Regression Model for Prediction of Mean Daily Global Solar Radiation in Al-Ain, UAE

The availability of short-term forecast weather model for a particular country or region is essential for operation planning of energy systems. This paper presents the first step by a group of researchers at UAE University to establish a weather model for the UAE using the weather data for at least 10 years and employing various models such as classical empirical models, artificial neural network (ANN) models, and time-series regression models with autoregressive integrated moving-average (ARIMA). This work uses time-series regression with ARIMA modeling to establish a model for the mean daily and monthly global solar radiation (GSR) for the city of Al-Ain, United Arab Emirates. Time-series analysis of solar radiation has shown to yield accurate average long-term prediction performance of solar radiation in Al-Ain. The model was built using data for 10 years (1995–2004) and was validated using data of three years (2005–2007), yielding deterministic coefficients (R2) of 92.6% and 99.98% for mean daily and monthly GSR data, respectively. The low corresponding values of mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) confirm the adequacy of the obtained model for long-term prediction of GSR data in Al-Ain, UAE.

Estimation of global solar radiation using ANN over Turkey

The main objective of the present study is to develop an artificial neural network (ANN) model based on multi-nonlinear regression (MNLR) method for estimating the monthly mean daily sum global solar radiation at any place of Turkey. For this purpose, the meteorological data of 31 stations spread over Turkey along the years 2000–2006 were used as training (27 stations) and testing (4 stations) data. Firstly, all independent variables (latitude, longitude, altitude, month, monthly minimum atmospheric temperature, maximum atmospheric temperature, mean atmospheric temperature, soil temperature, relative humidity, wind speed, rainfall, atmospheric pressure, vapor pressure, cloudiness and sunshine duration) were added to the Enter regression model. Then, the Stepwise MNLR method was applied to determine the most suitable independent (input) variables. With the use of these input variables, the results obtained by the ANN model were compared with the actual data, and error values were found within acceptable limits. The mean absolute percentage error (MAPE) was found to be 5.34% and correlation coefficient ( R) value was obtained to be about 0.9936 for the testing data set.

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.

The relationship between global solar radiation and sunshine duration in Vietnam

Two approaches of the well-known modified Angstrom formula were developed from long term records of measured monthly mean daily global solar radiation and sunshine hour values obtained from 12 meteorological stations across Vietnam. These formulae were then used to estimate solar radiation for stations where only sunshine records were available. Three other commonly used correlations between solar irradiation and sunshine duration were also used and their results compared with those of two developed models. The procedure of measurement in Vietnamese weather stations was also indicated.

Performance of Sayigh's universal formula for the estimation of global solar radiation in Ghana

The performance of Sayigh's universal formula for the estimation of global solar radiation is tested against that of Angstrom-Black model for 13 stations in Ghana, using monthly mean daily global solar radiation averaged over the years 1957–1981. Sayigh's model is found not to perform as creditably as the Angstrom-Black model in the estimation of monthly global solar radiation in Ghana. Of the 156 values of monthly global solar radiation estimated by Sayigh's model, 123 (or 78.8%) had discrepancies of more than 10% with the measured values. The corresponding value for the Angstrom-Black model was 7 (or 4.5%).

Solar radiation mapping from satellite image using a low cost system

An operational low cost system is developed for mapping daily and monthly mean daily global solar radiation reaching the ground over a big geographical area from APT (Automatic Picture Transmission) images of NOAA polar orbiting satellite. The system is suitable for use in south and southeast Asia and is said to be acceptable for engineering, agricultural, and climatological applications requiring the knowledge of geographical and temporal variation of solar radiation. It is alterable for use in other locations. The system comprises a personal computer attached with a small APT receiver along with other peripherals such as monochrome monitors and simple printer. An improved empirical bispectral method which applies visible and far infrared APT images is used to calculate the cloudiness of the sky. The empirical coefficients obtained by correlating cloudiness with ground measured solar radiation are used to estimate solar radiation at a target on the ground knowing the cloudiness of its sky from a satellite image. The geographical coverage of the map can be as big as 15° latitude by 20° longitude near the Equator. The spatial resolution of the radiation map is 111 km. The mean standard error of estimation of daily global radiation is 12.9% with a range of 6–19% of the measured mean. This accuracy is acceptable and comparable to other similar methods developed so far.

NEW CORRELATIONS OF GLOBAL SOLAR RADIATION WITH METEOROLOGICAL PARAMETERS FOR BAHRAIN

Measurements of global solar radiation on a horizontal surface, hours of bright sunshine, mean daily maximum temperature, mean daily relative humidity, mean daily sea level pressure (M.S.L), and mean daily vapour pressure are presented. A modified empirical correlation for estimating monthly mean daily global solar radiation on a horizontal surface is obtained by using meteorological parameters, viz hours of bright sunshine, monthly mean daily maximum temperature, relative humidity, and daily M.S.L and vapour pressure. The suggested modified equations (11) and (12) are tested for their applicability to State of Bahrain. The results show an excellent agreement between the measured values of monthly global solar radiation and the estimated values obtained from these equations. The average percentage error for the two equations is 4.3% and 4.6% respectively. The two modified models are recommended to be used for Bahrain.

A general formula for computing the coefficients of the correlation connecting global solar radiation to sunshine duration

Correlations are developed to estimate the regression coefficients a and b of Ångström type correlations for predicting monthly mean daily global solar radiation on a horizontal surface. The suggested equations express the regression coefficients in terms of the latitude, elevation, and percent of possible sunshine and are applicable to any location around the world to compute global solar radiation on horizontal surfaces.

Global radiation derived from sunshine data

Some empirical formulae have been attempted in the past to compute mean daily global solar radiation from sunshine or cloud data. The instruments measuring solar radiation, depending on the method used measure either direct or diffuse radiation. It is possible to correlate the sunshine hours measured and the direct solar radiation received. An empirical formula has been attempted and tested using one year(1971) data for Delhi. The correlation is found to be high except in the month of April.