Mean Global Solar Radiation Research Articles

An empirical technique to predict monthly mean global solar radiation for PV applications in Indian context

AbstractGlobal radiation is a significant input in the design and simulation of solar PV as well as low temperature solar thermal systems. Moreover, meteorological conditions of any location also greatly influence the design, simulation, and output of the solar energy system. The considerable importance of global radiation leads to a dire need for global radiation models in the lack of measurement. Consequently, to enhance the predictability of solar resources over India, models for global radiation simulation through meteorological parameters (temperature, relative humidity, and wind speed) are proposed. Data of nine stations from NASA for 10 years (2008–2017) are used in the study to calibrate the models and performance of developed models is assessed through different statistical indicators (MPE, MBE, RMSE, R2, RMSD, NSE, and TS). The investigation reveals that out of the three proposed models, the model correlating total solar radiation with relative humidity, temperature, and wind speed is found to provide the highest accuracy in global radiation simulation with least statistical errors and is selected as best fit model. Moreover, validation of the proposed models is approved through monthly average daily total solar radiation predication for the remaining four testing cities of India and also through comparison with previously available models in the literature.

DEVELOPMENT OF A LOW-COST SOLAR PYRANOMETER

Effective solar projects require effective solar data collection, however with the high cost of pyranometers in the market, there arose the need to construct a low-cost device for the purpose of solar radiation measurements. A device was thus developed in the University of Port Harcourt, River’s state, Nigeria by utilizing a photodiode, a thermistor, a lcd among other readily available components. Using a reference pyranometer for calibration, the device was able to deliver a mean global solar radiation value of 16.00MJ/m2/day, while the maximum value obtained during the test period was 20.70MJ/m2/day. The performance of the locally developed device compares and competes favourably with what is available in the market.

Simplified Independent Model for Predicting Global Solar Radiation

In this investigation, five existing independent empirical models were calibrated and evaluated to calculate daily and monthly mean global solar radiation on a horizontal surface in Adrar city located in the south of Algeria, using meteorological data measured from 2013 to 2018. The measured data were divided into two periods; the first period (2015-2018) was used to calculate the empirical coefficients of the models, while the second period (2013-2014) was used to validate the correlations. Additionally, the best model (Al-Salaymeh model) was compared with five dependent empirical extreme air temperature models. In general, the results show that dependent models exhibited privileged performance than independent models. However, Al-Salaymeh regression independent model can contend with regression dependent models. Because of they use only the day number as a key input with smaller relative errors. It is found that daily statistical tests mean absolute bias error, root mean square error and coefficient of determination were equal to 2.0117 MJ/m², 2.4612 MJ/m² and 0.8014 respectively. The best independent empirical model was also compared with the modified Algerian solar atlas model to show their effectiveness. As a conclusion, the simple independent models can satisfactorily describe the horizontal global solar radiation for Adrar.

Modelling Soil Temperature by Tree-Based Machine Learning Methods in Different Climatic Regions of China

Accurate estimation of soil temperature (Ts) at a national scale under different climatic conditions is important for soil–plant–atmosphere interactions. This study estimated daily Ts at the 0 cm depth for 689 meteorological stations in seven different climate zones of China for the period 1966–2015 with the M5P model tree (M5P), random forests (RF), and the extreme gradient boosting (XGBoost). The results showed that the XGBoost model (averaged coefficient of determination (R2) = 0.964 and root mean square error (RMSE) = 2.066 °C) overall performed better than the RF (averaged R2 = 0.959 and RMSE = 2.130 °C) and M5P (averaged R2 = 0.954 and RMSE = 2.280 °C) models for estimating Ts with higher computational efficiency. With the combination of mean air temperature (Tmean) and global solar radiation (Rs) as inputs, the estimating accuracy of the models was considerably high (averaged R2 = 0.96–0.97 and RMSE = 1.73–1.99 °C). On the basis of Tmean, adding Rs to the model input had a greater degree of influence on model estimating accuracy than adding other climatic factors to the input. Principal component analysis indicated that soil organic matter, soil water content, Tmean, relative humidity (RH), Rs, and wind speed (U2) are the main factors that cause errors in estimating Ts, and the total error interpretation rate was 97.9%. Overall, XGBoost would be a suitable algorithm for estimating Ts in different climate zones of China, and the combination of Tmean and Rs as model inputs would be more practical than other input combinations.

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.

Solar Energy Potential Estimated by Mean Global Solar Radiation Using Sunshine-Based Models and Their Validations by Geospatial Data in Ethiopia

Solar Energy Potential Estimated by Mean Global Solar Radiation Using Sunshine-Based Models and Their Validations by Geospatial Data in Ethiopia

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.

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.

Global Solar Radiation Characteristics at Calabar and Port Harcourt Cities in Nigeria

This study analyzed the inter-annual variability in solar radiation at Port Harcourt and Calabar, aiming at improving knowledge of solar resources. For the investigation, monthly mean global solar radiation data for fifteen years (2000 – 2014) was collected from Nigerian Meteorological Agency (NIMET), and the monthly mean extraterrestrial solar radiation was determined using globally recognized standard relation. The clearness index parameter was employed for characterizing the spatial variability of solar radiation for Calabar and Port Harcourt. The statistics of the monthly mean solar radiation deviations of Port Harcourt and Calabar was tested using the Kolmogorov–Smirnov method. The test results showed that they are normally distributed random variables. Furthermore, the analysis of sequential properties showed that the coefficients of the auto-correlation with lag 1 are significant for both stations. The auto-correlation coefficients with lag 1, though usually not significant, are negative for both stations. The auto regression lag 1 (AR-1) is the recommended procedure (model equation) for generating monthly solar radiation synthetic time series, with auto-correlation coefficients varying from 0.30 to 0.47 for both stations in the South-South of Nigeria. Citation: Amadi, S. (2020). Global Solar Radiation Characteristics at Calabar and Port Harcourt Cities in Nigeria. Trends in Renewable Energy, 6, 101-120. DOI: 10.17737/tre.2020.6.2.00114

Retraction Note to: A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of peer review and authorship manipulation.

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.

Estimation of global solar radiation, sunshine hour distribution and clearness index in Enugu, Nigeria

The concept of solar energy and its applications in this present day world would come to be one of the solutions to our present Nigerian problem of instability and epileptic power supply. In the study, the baseline data for mean monthly sunshine hours ( S ) and global solar radiation ( H ) for Enugu (6.7 0 N, 7.6 0 E) were obtained from the Nigeria Metrological Agency (NIMET), Oshodi Lagos Nigeria and spanned 1996 to 2010. The data for global solar radiation were measured with a Gunn-Bellani radiometer. The average sunshine hour for the period of study was estimated to be 5.67 hours; the Angstrom constants a and b of Angstrom-type correlation used in estimating monthly average global solar radiation was estimated to be 0.27 and 0.58 respectively; a linear regression correlation model was developed for Enugu. The result of global solar radiation was found to be 14.44 MJm -2 day -1 thereafter subjected to statistical tests [ MBE , RMSE , MPE ] and proved to be good estimates with previous literatures reviewed. The value of clearness index ( K T ) was estimated to range from 0.43 to 0.61 showing Enugu as so a partly clear sky city. Keywords: Global solar radiation, Sunshine hour distribution, Clearness Index Enugu

Assessment of Decentralized Electricity Production from Hybrid Renewable Energy Sources for Sustainable Energy Development in Nigeria

Abstract This paper presents technical and economic assessment of a hybrid energy system for electricity generation in rural communities in the six geopolitical zones of Nigeria. The study was based on a 500 rural household model having an electric load of 493 kWh per day. To simulate long-term continuous implementation of the hybrid system, 21 years (1992 – 2012) hourly mean global solar radiation and wind speed data for the selected sites were used. The mean annual wind speed and solar radiation for the locations ranged from 2.31 m/s for Warri to 3.52 m/ s for Maiduguri and 4.53 kWh/m2 for Warri to 5.92 kWh/m2 for Maiduguri, respectively. These weather data were used for simulation with the Micro-power Optimization Model software HOMER. From the optimum results of the hybrid system,Warri has the highest NPC and COE of $2,441,222 and $0.721/kWh, respectively while Maiduguri has the least NPC and COE of $2,225,387 and $0.658/kWh, respectively for the 21 years project lifespan. The high value of COE for Warri is due to its low renewable energy resource while low COE for Maiduguri is due to its high renewable energy resource. The Northern part of the country has ample renewable energy resource availability and with a strong political will, optimal utilization of these renewable resources (solar and wind) can be actualized. Researchers, Industrialists, Policy Makers and the Nigerian government should therefore seize this opportunity in developing a sustainable energy through utilization of abundant renewable energy resources in the country.

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.

Support vector regression for predicting building energy consumption in southern China

It is increasingly significant to predict building energy consumption in energy-saving decision making. This paper presents the method of support vector regress (SVR) to forecast building energy consumption in southern China. To improve the reliability of SVR in building energy consumption prediction, multiple parameters including weather data such as yearly mean outdoor dry-bulb temperature, relative humidity and global solar radiation and economic factors such as the ratio of urbanization, gross domestic product, household consumption level and total area of structure are taken as inputs. The performance of SVR with respect to two parameters, c and g, is explored using the k-fold cross validation with grid searching method based on radial-basis function kernel. Finally, the performance of the proposed model is checked by comparing the prediction model with statistic data taken from Chinese National Bureau of Statistics in 4 provinces of southern China using statistical error tests such as the mean square error (MSE) and the squared correlation coefficient (r2). With the analysis based on statistical error tests, the results show that the SVR method can estimate the building energy consumption with good accuracy with MSE less than 0.001 and r2 more than 0.99.

Estimation of Global Solar Radiation and Clearness Index in Coast of Gulf of Guinea, Nigeria

The concept of solar energy and its applications in present day would come to be one of the solutions to our Nigerian problem of instability and epileptic power supply. In this study, data for mean monthly sunshine hours and global solar radiation for Ikeja, Lagos state capital (6.580N, 3.320E) were obtained from Nigeria Metrological Agency (NiMeT), Oshodi Lagos, Nigeria and spanned 1996 to 2010. The data for global solar radiation were measured using a Gunn-Bellani radiometer. A linear regression correlation model was developed for Ikeja and other surrounding area in south-western part of Nigeria with similar meteorological conditions. The results of estimated global solar radiation ranged from 5.1 MJm-2 day-1 on average for August and 13.1 MJm-2 day-1 for March for Lagos. The Angstrom constants a and b of Angstrom-type correlation to estimate monthly average global solar radiation was estimated to be 0.25 and 0.63 respectively. The result for global solar radiation were then subjected to statistical tests [MBE, RMSE, MPE] and proved to be good estimates. The value of clearness index was also estimated to range from 0.31 to 0.59 showing Lagos as a partly clear sky city.

Development of empirical models for estimation of global solar radiation exergy in India

In this work, exergy of incoming solar radiation is estimated using empirical models. Long-term meteorological data (1986–2000) consisting of monthly mean diffuse and global solar radiation on a horizontal plane, ambient temperature and sunshine hours for 23 selected stations were available from Indian Meteorological Department (Pune), India. Exergy of direct and diffuse radiations was calculated using Petela model and summed to obtain exergy of global solar radiation. Beam and diffuse exergy efficiencies were in the range of 0.9286–0.9365 and 0.7189–0.7499 respectively. Estimated global exergy efficiencies were found in the range of 0.7700–0.8912. Empirical models were developed by correlating global solar exergy (in the form of global exergy efficiency and global exergy efficiency factor) in terms of clearness index and relative sunshine period. Four categories of single predictor models were defined with each consisting of eight models. Statistical analysis was used to compare and evaluate the accuracy of the models. Further, Global Performance Indicator was calculated and used to rank the models within the individual categories of models as well as the overall group of the 32 developed models. Overall assessment proved that power model with the clearness index is the best one for estimation of global exergy efficiency factor.

Applying support vector machines to predict building energy consumption in China

It is increasingly significant to predict building energy consumption (BEC) in energy-saving decision making. This paper presents the method of support vector machines (SVM) to forecast building energy consumption in China. To improve the reliability of SVM in building energy consumption prediction, multiple parameters including weather data such as yearly mean outdoor dry-bulb temperature, relative humidity and global solar radiation and economic factors such as the ratio of urbanization, gross domestic product, household consumption level and total area of structure are taken as inputs. The performance of SVM with respect to two parameters, c and g, is explored using the k-fold cross validation with grid searching method based on radial-basis function kernel. Finally, the performance of the proposed model is checked by comparing the prediction model with statistic data taken from Chinese National Bureau of Statistics in 30 provinces (including autonomous regions and municipalities) of China using statistical error tests such as the mean square error (MSE) and the squared correlation coefficient (r2). With the analysis based on statistical error tests, the results show that the SVM method can estimate the building energy consumption with good accuracy with MSE less than 1E-3 and r2 more than 0.991.

Economic assessment of renewable power generation based on wind speed and solar radiation in urban regions

Economic evaluation of 12 MW grid-connected wind farms and PV power plants in two regions in Northern Cyprus for electricity generation was investigated. The wind speed, sunshine duration, and solar global radiation characteristics were analyzed using monthly data collected over 17 years (2000-2016) for Girne and nine years (2008-2016) for Lefkosa, which were measured at various heights. The result showed that during 2000-2016, the mean wind speed at Girne was 2.505 m/s and during 2008-2016, the mean wind speed at Lefkosa was 2.536 m/s. The result showed that both regions had annual mean wind speed greater than 2 m/s at 10 m height. Moreover, the annual mean sunshine duration and global solar radiation were higher than seven h/day and 15 MJ./m2/day at a height of 2 m for all studied regions, respectively. In this study, eight distribution functions were used to analyze the wind speeds and global solar radiation data in each region. The results indicated that Weibull and Logistic were the best distributions for analyzing the wind speeds and global solar radiation data of the studied regions, respectively. Furthermore, the capacity factors of the selected regions ranged between 1.92% and 48.53%. Based on the renewable energy cost results, it is found that the generation costs of the wind farm were between 0.023 and 0.04 Euro/kWh, while the PV plant was between 0.08 and 0.098 Euro/kWh.

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