Daily Global Solar Radiation Data Research Articles

Climate change on Eucalyptus plantations and adaptive measures for sustainable forestry development across Brazil

The main challenges facing the Eucalyptus plantation industry are employing sustainable projects in suitable areas, and using safer forestry strategies, given climate variability and climate change. Brazil is the world’s largest Eucalyptus producer, and more areas have been allocated for Eucalyptus plantations, even in areas that are considered persistent climate change hotspots. Researchers can combine adequate agroclimatic zoning methods with Earth System Model (ESMs) outputs to analyze changes to climate variables, to map suitable areas, and to outline adaptive measures for Eucalyptus plantations. Despite these facts, studies on the Eucalyptus species are still scarce. This study analyzed the impacts of climate change to suitable areas for planting Eucalyptus in Brazil, using proper Agroclimatic Zoning (AZ) method, and outlining effective adaptive measures for sustainable Eucalyptus cultivation under future climate scenarios. Daily precipitation, air temperature (mean and maximum), and global solar radiation data were used from a set of simulations and projections of nine ESMs, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), in two Representative Concentration Pathway scenarios (RCP 4.5 and 8.5). The AZ method was simulated for the current climate (1980–2005), and projected for the near (2021–2050), and far future (2071–2100) climates, in two RCPs. Given intense and persistent changes to precipitation, air temperature, evapotranspiration, and water deficits, i.e., prolonged and continued changes to climate variables resulting from climate change, Eucalyptus plantations will be restricted and economically unfeasible for industrial purposes for about 15 % (RCP 4.5), to 47 % (RCP 8.5), of Brazil in the far future, covering a large part of the North, the north of the Center-West, and the northwestern part of the Northeast, including the new Brazilian agroforestry frontier (MATOPIBA). Only ∼12 % of Brazil, encompassing small areas of the South, the South-Southeast, and a small portion in the Northwest region of the North, will be suitable for Eucalyptus plantations, since these regions are less vulnerable to climate change. As for the rest of Brazil (between ∼42 % and ∼80 %), regular suitability is projected, and the future of Eucalyptus plantations in these places will depend on adopting ‘effective adaptive measures’ and making ‘safe silvicultural decisions’ in order to deal with projected temperature increases and water deficits, including genotypes selection, improvements in hybridization techniques or breeding programs, planting in wider spacings (or lower stockings), and thinning and pruning at shorter intervals.

Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries

The prediction of global solar radiation in a region is of great importance as it provides investors and politicians with more detailed knowledge about the solar resource of that region, which can be very beneficial for large-scale solar energy development. In this sense, the main objective of this study is to predict the daily global solar radiation data of 27 cities (Brussels, Paris, Lisbon, Madrid…), located in 27 countries, which have mostly different solar radiation distributions in Europe. In this research, six different machine-learning algorithms (Linear model (LM), Decision Tree (DT), Support Vector Machine (SVM), Deep Learning (DL), Random Forest (RF) and Gradient Boosted Trees (GBT)) are used. In the training of these algorithms, daily air temperature(Ta), wind speed(Va), relative humidity(RH) and solar radiation of these cities are used. The data is supplied from the Meteonorm tool and cover the last years grouped in two periods (1960–1990; 2000–2019). To decide on the success of these algorithms, four different statistical metrics (Average Relative Error (ARE), Average absolute Error (AAE), Root Mean Squared Error (RMSE), and R2 (R-Squared)) are discussed in the study. In addition, the forecasting of air temperature and global solar radiation of these cities in 2050 and 2100 were made using three of the most recent Intergovernmental Panel on Climate Change (IPCC) scenarios (RCP2.6; RCP 4.5, and RCP 8.5). The results show that ARE, R,2 and RMSE values of all algorithms are ranging from 0.114 to 6.321, from 0.382 to 0.985, from 0.145 to 2.126 MJ/m2, respectively. By analysing all the algorithms, it is noticed that the Decision tree exhibited the worst result in terms of R,2 and RMSE metrics. Among the six prediction algorithms, the DL was recognized as the only algorithm that exceeded the t-critical value (The t-critical value is the cutoff between retaining or rejecting the null hypothesis). Globally, all the six machine learning algorithms used in this research can be applied to predict the daily global solar radiation data with good accuracy. Despite this, the SVM model is the best model among all the six models used. It is followed by the DL, LM, GB, RF and DT, respectively.

A Time Series Forecasting of Global Horizontal Irradiance on Geographical Data of Najran Saudi Arabia

Environment-friendly and renewable energy resources are the need of each developed and undeveloped country. Solar energy is one of them, thus accurate forecasting of it can be useful for electricity supply companies. This research focuses on analyzing the daily global solar radiation (GSR) data of Najran province located in Saudi Arabia and proposed a model for the prediction of global horizontal irradiance (GHI). The weather data is collected from Najran University. After inspecting the data, I we found the dependent and independent variables for calculating the GHI. A dataset model has been trained by creating tensor of variables belonging to air, wind, peak wind, relative humidity, and barometric pressure. Furthermore, six machine learning algorithms convolutional neural networks (CNN), K-nearest neighbors (KNN), support vector machines (SVM), logistic regression (LR), random forest classifier (RFC), and support vector classifier (SVC) techniques are used on dataset model to predict the GHI. The evaluation metrics determination coefficients (R2), root mean square error (RMSE), relative root mean square error (rRMSE), mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and T-statistic (t-stat) are used for the result verification of proposed models. Finally, the current work reports that all methods examined in this work may be utilized to accurately predict GHI; however, the SVC technique is the most suitable method amongst all techniques by claiming the precise results using the evaluation metrics.

Study of Clearness Index of Atmosphere over Eastern City of Nepal, Biratnagar

Solar radiation data are of great significance for energy harvesting. This paper’s main objective is to study clearness index of atmosphere over Biratnagar (latitude: 26.45° N, longitude: 87.27° E, altitude: 72 m a. s. l.), an eastern city of Nepal. Daily global solar radiation data are derived by using CMP6 pyranometer for one year period (2015). The monthly, annual and seasonal variations of global solar radiation, clearness index and visibility were analyzed. The annual average of global solar radiation (GSR), clearness index (KT) and visibility indices are found to be 11.37 ± 3.76 MJ/m2 /day, 0.37 ± 0.15 and 7.06 ± 5.56 km are respectively. In the study period, number of clear days (KT >0.65) and number of cloudy days (KT <0.34) are found to be 26 and 172 respectively. Study of clearness index of atmosphere and its dependence on meteorological parameters (such as maximum temperature, minimum temperature, rainfall, relative humidity, total ozone column and dew point) are used on agriculture, hydrology, and climate change. Finding of GSR and visibility helps for energy harvesting and aviation.

Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

The prediction of global solar radiation for the regions is of great importance in terms of giving directions of solar energy conversion systems (design, modeling, and operation), selection of proper regions, and even future investment policies of the decision-makers. With this viewpoint, the objective of this paper is to predict daily global solar radiation data of four provinces (Kırklareli, Tokat, Nevşehir and Karaman) which have different solar radiation distribution in Turkey. In the study, four different machine learning algorithms (support vector machine (SVM), artificial neural network (ANN), kernel and nearest-neighbor (k-NN), and deep learning (DL)) are used. In the training of these algorithms, daily minimum and maximum ambient temperature, cloud cover, daily extraterrestrial solar radiation, day length and solar radiation of these provinces are used. The data is supplied from the Turkish State Meteorological Service and cover the last two years (01.01.2018–31.12.2019). To decide on the success of these algorithms, seven different statistical metrics (R2, RMSE, rRMSE, MBE, MABE, t-stat, and MAPE) are discussed in the study. The results shows that R2, MABE, and RMSE values of all algorithms are ranging from 0.855 to 0.936, from 1.870 to 2.328 MJ/m2, from 2.273 to 2.820 MJ/m2, respectively. At all cases, k-NN exhibited the worst result in terms of R2, RMSE, and MABE metrics. Of all the models, DL was the only model that exceeded the t-critic value. In conclusion, the present paper is reporting that all machine learning algorithms tested in this study can be used in the prediction of daily global solar radiation data with a high accuracy; however, the ANN algorithm is the best fitting algorithm among all algorithms. Then it is followed by DL, SVM and k-NN, respectively.

Estimating Daily Global Solar Radiation with No Meteorological Data in Poland

The aim of the study was to calibrate coefficients and evaluate performance of simple, day-of-the-year, global solar radiation (H) models nominated from the literature. Day-of-the-year models enable estimation of global solar radiation when no meteorological data is available. The study used 16-year-long data series of daily H, taken at 15 actinometric stations located in various parts of Poland. The goodness-of-fit of the models to the actual long-term monthly average daily global solar radiation data expressed by determination coefficient (R2) ranges from 0.94 to 0.97. Depending on statistical indicators analysis (root mean square error—RMSE, mean absolute bias error—MABE, mean average percentage error—MAPE) the best model was selected. The averaged values of H computed by the recommended model deviate from those measured by 4.16% to 8.71%. Locally calibrated, day-of-the-year model provides satisfactory accuracy and—where meteorological data is unavailable—can be used to estimate mean monthly daily global solar radiation in Poland and similar climate conditions.

Analysis of Daily Global Solar Radiation Data for Wuhan

In order to improve knowledge on solar resources, this paper analyses the models correlating daily global solar radiation with sunshine duration for Wuhan, China. Estimated values were compared with measured values in terms of statistical error tests such as mean percentage error (MPE), mean bias error (MBE), root mean square error (RMSE). The model performing best was selected. The global solar radiation estimated from the best model was compared with measured values. It was determined that the predicted values have good agreement with the measured values at high daily global solar radiation.

Optimistic / Normal / Pessimistic Limits of Global Solar Radiation in Urban Armidale NSW, Australia

The most common data for describing the local solar climate is through what is called Typical Meteorological Year data (TMY). Typical solar radiation data is very important for the calculations of many solar applications. In this study, typical solar radiation years for Armidale town in New South Wales in Australia are generated from the daily global solar radiation data measured for 23 years, utilising the Finkelstein-Schafer statistical method. The study outcome provides optimistic, normal and pessimistic expected global solar radiation in Armidale all over the year based on previous studies conducted based on meteorological historical measured data in Armidale's Airport Weather Automatic Weather Station. Such outcome presents a handy database of global solar radiation to be utilised by solar energy's specialists for detailed calculations and/or ordinary persons for initial assessment of solar energy in Armidale.

Diurnal variations of ground-based GPS-PWV under different solar radiation intensity in the Chengdu Plain

The estimated zenith total delays (ZTD) in Chengdu are calculated from the ground-based GPS data using the GPS analytic software package. The 30-min interval precipitable water vapor (PWV) derived from GPS is calculated based on meteorological data observed at automatic weather stations (AWS). Combining the daily global solar radiation and sunshine data, characteristics of GPS-PWV in both strong and weak solar radiation days are analyzed. The relationship between GPS-PWV and surface temperature is also investigated. The results show that the GPS-PWV under weak radiation is larger than that under strong radiation. dPWV (the difference between PWV under the weak and strong radiation days) and dQ (the difference of global radiation) are positively correlated. Without precipitation, dPWV is distinct in the daytime. During the daytime with strong radiation (sunny day), GPS-PWV is negatively correlated with temperature. The GPS-PWV declines mainly after sunrise. While temperature reaches its daily maximum, the GPS-PWV reaches its minimum. During the daytime of weak radiation (cloudy day), GPS-PWV and temperature are positively correlated. Accompanying the temperature rise after sunshine, GPS-PWV increases gradually. However, variation of GPS-PWV is lagged to the variation of temperature due to the greenhouse effect of water vapor.

Statistical Approach for Modeling of Daily Global Solar Radiation on Horizontal Surfaces Over Hebron City, Palestine

Understanding solar radiation data is essential for modeling solar energy systems because it is non-polluting, non-depletable, reliable, and pollution free. Four-years measured average daily global solar radiation data on horizontal surfaces at Hebron city in Palestine were used for the model assessment. Statistical results showed that all four suggested models can accurately predict the solar irradiance of a 31° 57' N (latitude angle of Hebron) on a horizontal oriented surface, indicating the good predictive ability for modeling a horizontal surface. The obtained models have a high value of regression coefficient and give best fit through the measured values. This work illustrates also the use of mathematical formula to develop a predictive model for the duration of sunshine using measured solar radiation data for Hebron city. The present work showed that the predicted results were in good agreement with the observed data, and the expected solar radiation behavior.

Correlation for the Average Daily Diffuse Fraction with Clearness Index and Estimation of Beam Solar Radiation and Possible Sunshine Hours Fraction in Sabha, Ghdames and Tripoli - Libya

The daily global solar radiation data obtained from three Libyan locations, every one of them, it's located in different region, (Sabha- desert region), (Ghdames- middle region) and (Tripoli- Mediterranean region) were used to establish a relationship between daily diffuse fraction and daily clearness index KT. This relationship was compared with that established by Liu & Jordan, Stanhill, Choudhury, Erbs, and used to derive a correlation for the average daily diffuse fraction. Solar radiation and sunshine duration are intimately related phenomena, which was used to study the equation related of average daily radiation to the extraterrestrial radiation for location and average fraction of possible sunshine hours. Moreover the daily clearness index were calculated and the daily clearness index was used to estimate the frequency of occurrence of days with different values of KT and the cumulative frequency of occurrence of those days. Finally, the relationships for estimating the beam and diffuse components of daily global radiation were obtained using the previous mentioned author's model.

Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks

Four variables (total cloud cover, skin temperature, total column water vapour and total column ozone) from meteorological reanalysis were used to generate synthetic daily global solar radiation via artificial neural network (ANN) techniques. The goal of our study was to predict solar radiation values in locations without ground measurements, by using the reanalysis data as an alternative to the use of satellite imagery. The model was validated in Andalusia (Spain), using measured data for nine years from 83 ground stations spread over the region. The geographical location (latitude, longitude), the day of the year, the daily clear sky global radiation, and the four meteorological variables were used as input data, while the daily global solar radiation was the only output of the ANN. Sixty five ground stations were used as training dataset and eighteen stations as independent dataset. The optimum network architecture yielded a root mean square error of 16.4% and a correlation coefficient of 94% for the testing stations. Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.

Generation of representative solar radiation data for Aegean Region of Turkey

Typical solar radiation data is needed in calculation and assessment of many solar energy applications. In this study, typical solar radiation years for seven provinces located in Aegean Region of Turkey were generated from the daily global solar radiation data measured during at least for 15 years, using the Finkelstein-Schafer statistical method. The typical daily global solar radiation data for the locations considered were presented throughout a year in a tabular form. The data obtained were also analyzed. It is expected that the typical data presented for Aegean Region will be useful to the designers of solar energy systems. Key words: Finkelstein-Schafer method, solar radiation, test reference year, Aegean region, Turkey.

Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia

Abstract In this paper, Radial Basis Function network (RBF) is used for modelling and predicting the daily global solar radiation data using other meteorological data such as air temperature, sunshine duration, and relative humidity. These data were recorded in the period 1998–2002 at Al-Madinah (Saudi Arabia) by the National Renewable Energy Laboratory. Four RBF-models have been developed for predicting the daily global solar radiation. It was found that the RBF-model which uses the sunshine duration and air temperature as input parameters, gives accurate results as the correlation coefficient in this case is 98.80%. A comparative study between developed RBF, Multilayer perceptron and conventional regression models are presented and discussed in this paper, In addition, an application for estimating the sizing of a stand-alone PV system at Al-Maidinah is presented in order to show the effectiveness of the developed RBF-model.

Reference Solar Radiation Year and Some Climatology Aspects of East Coast of West Malaysia

Problem statement: This study examined in detail some aspects of radia tion climatology for Terengganu state in Malaysia. The geographical co-ordinates of the site are 5°10'N latitude 103°06'E longitude and 5.2 m altitude. Approach: The data used in the present study were taken from a recording data station installed at site by the M alaysian Meteorology Department (2004). In addition to this, wind speed, solar radiation and air temper ature data taken from University Malaysia Terengganu Renewable Energy Station were also analyzed. The averaged solar radiation year for Terengganu is generated from mid-term daily global solar radiation data. The averaged data of the daily global solar radiation for the locations cons idered are presented in a tabular form for a comple te year. Results: The highest daily and monthly mean global solar irr adiation values were 7560 and 6566 Wh m -2 day -1 , respectively. The highest hourly average solar ir radiance intensity was 1139 W m -2 during this study period. Yearly average daily sol ar energy was 18.92 MJ m -2 day -1 . Besides the global solar radiation, the clearness indexes, wind speed and air temperature variation are discussed. Conclusion/Recommendations: The results in this study will fill the gap that c omplete and detailed reference solar radiation and climatologically data , which are not available for Terengganu state of Malaysia and will be useful to the designers of sol ar energy conversion and utilization devices.

ANN-based modelling and estimation of daily global solar radiation data: A case study

In this paper, an artificial neural network (ANN) models for estimating and modelling of daily global solar radiation have been developed. The data used in this work are the global irradiation H G , diffuse irradiation H D , air temperature T and relative humidity H u . These data are available from 1998 to 2002 at the National Renewable Energy Laboratory (NREL) website. We have developed six ANN-models by using different combination as inputs: the air temperature, relative humidity, sunshine duration and the day of year. For each model, the output is the daily global solar radiation. Firstly, a set of 4 × 365 points (4 years) has been used for training each networks, while a set of 365 points (1 year) has been used for testing and validating the ANN-models. It was found that the model using sunshine duration and air temperature as inputs, gives good accurate results since the correlation coefficient is 97.65%. A comparative study between developed ANN-models and conventional regression models is presented in this study.

Generation of Typical Solar Radiation Year for Mediterranean Region of Turkey

Typical solar radiation data is very important for the calculations concerning many solar applications. In this study, typical solar radiation years for six provinces located in the Mediterranean region of Turkey are generated from the daily global solar radiation data measured at least for 14 years, using the Finkelstein-Schafer statistical method. The typical daily global solar radiation data for the locations considered is presented throughout a year in a tabular form. The data obtained is also analyzed. It is expected that the typical data presented for the Mediterranean region, which is rich for solar energy potential in Turkey, will be useful to the designers of solar energy systems.

Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey

In this study, seven different empirical equations are employed to estimate the monthly average daily global solar radiation on a horizontal surface for provinces in the different regions of Turkey, using only the relative duration of sunshine. Daily global solar radiation and sunshine measurement data collected for the provinces of Turkey are obtained from the Turkish State Meteorological Service. The regression constants of the new models developed in this study are found for the provinces of Turkey, as well as that of some models given in the literature. In order to indicate the performance of the models, the statistical test methods of the mean bias error (MBE), mean absolute bias error (MABE), mean relative error (MRE), root mean square error (RMSE) and correlation coefficient (r) are used.

Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models.

The results of previous studies have suggested that estimated daily globalradiation (RG) values contain an error that could compromise the precision of subsequentcrop model applications. The following study presents a detailed site and spatial analysis ofthe RG error propagation in CERES and WOFOST crop growth models in Central Europeanclimate conditions. The research was conducted i) at the eight individual sites in Austria andthe Czech Republic where measured daily RG values were available as a reference, withseven methods for RG estimation being tested, and ii) for the agricultural areas of the CzechRepublic using daily data from 52 weather stations, with five RG estimation methods. In thelatter case the RG values estimated from the hours of sunshine using the ångström-Prescottformula were used as the standard method because of the lack of measured RG data. At thesite level we found that even the use of methods based on hours of sunshine, which showedthe lowest bias in RG estimates, led to a significant distortion of the key crop model outputs.When the ångström-Prescott method was used to estimate RG, for example, deviationsgreater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 percent of cases. The precision of the yield estimates and other crop model outputs was lowerwhen RG estimates based on the diurnal temperature range and cloud cover were used (mean bias error 2.0 to 4.1 per cent). The methods for estimating RG from the diurnal temperature range produced a wheat yield bias of more than 25 per cent in 12 to 16 per cent of the seasons. Such uncertainty in the crop model outputs makes the reliability of any seasonal yield forecasts or climate change impact assessments questionable if they are based on this type of data. The spatial assessment of the RG data uncertainty propagation over the winter wheat yields also revealed significant differences within the study area. We found that RG estimates based on diurnal temperature range or its combination with daily total precipitation produced a bias of to 30 per cent in the mean winter wheat grain yields in some regions compared with simulations in which RG values had been estimated using the ångström-Prescott formula. In contrast to the results at the individual sites, the methods based on the diurnal temperature range in combination with daily precipitation totals showed significantly poorer performance than the methods based on the diurnal temperature range only. This was due to the marked increase in the bias in RG estimates with altitude, longitude or latitude of given region. These findings in our view should act as an incentive for further research to develop more precise and generally applicable methods for estimating daily RG based more on the underlying physical principles and/or the remote sensing approach.

Generation of typical solar radiation year for China

Applying the daily global solar radiation data measured at least 10 years, the typical solar radiation year for 30 meteorological stations in China is generated using the Finkelstein–Schafer statistical method. Based on the typical solar radiation data obtained, the geographical difference of solar energy resource of these 30 stations was also analyzed. The results in this paper will fill this gap that complete and detailed typical solar radiation data are not available for China, and will be useful to the designers of solar energy conversion and utilization devices.