Daily Global Solar Radiation Research Articles

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.

Improved estimation of global solar radiation over rugged terrains by the disaggregation of Satellite Applications Facility on Land Surface Analysis data (LSA SAF)

AbstractThis paper presents a new method to predict global solar radiation over irregular terrain, named Estimation of global solar RADiation (ERAD). The method is based on the disaggregation of Satellite Applications Facility on Land Surface Analysis (LSA SAF) data using a digital elevation model and is applied in Italy with a time step of 1 min and a spatial resolution of 200 m. A quantitative assessment of ERAD is performed in comparison with three other standard methods (Mountain Microclimate Simulation Model [MTCLIM], LSA SAF and Copernicus Atmosphere Monitoring Service [CAMS]) using measurements taken in 43 stations located in Italy or in the surrounding countries, in the years 2005–2016. Such assessment concerns the irradiance incoming on a horizontal surface, which is measured by ground radiation sensors and is summarized by means of four accuracy statistics (i.e. mean absolute error [MAE], root mean square error [RMSE], coefficient of determination [R2] and mean bias error [MBE]). Overall, the average daily global solar radiation estimates obtained by ERAD have RMSE and R2 about 25 W·m−2 and 0.943, respectively. These statistics are similar to those of LSA SAF and better than those of CAMS and, above all, MTCLIM. The bias analysis by elevation ranges shows a slight ERAD overestimation over plains and hills and a slight underestimation over mountains. An additional qualitative assessment shows how the ERAD radiation estimates are more spatially detailed than those of the other methods and are redistributed on inclined surfaces consistently with expectations.

New input selection procedure for machine learning methods in estimating daily global solar radiation

Selection of optimal model inputs is a challenging issue particularly for non-linear and dynamic systems. In this study, a new input selection method, procrustes analysis (PA), was implemented and compared with gamma test (GT) for estimating daily global solar radiation (Rs). The PA and GT were applied for modeling with the non-linear models of artificial neural networks (ANNs) and support vector machines (SVMs). Goodness-of-fit of the models was evaluated by the coefficient of correlation (CC), root-mean-square error (RMSE), and Nash-Sutcliffe model efficiency coefficient (NS). The uncertainty of the model outputs was determined using 95PPU% (p-factor) and d-factor. In this study, we used maximum wind speed, mean wind speed, maximum temperature, minimum temperature, mean temperature, maximum sea surface pressure, minimum sea surface pressure, mean sea surface pressure, mean vapor pressure, total rainfall, maximum cloudiness, mean cloudiness, maximum humidity, minimum humidity, mean humidity, sunshine hours, evaporation, mean dew point temperature, mean wet point temperature, maximum air pressure, minimum air pressure, mean air pressure, and mean vapor saturation as input variables. Maximum and mean temperature; maximum wind speed; maximum, minimum, and mean sea surface pressure; maximum, minimum, and mean air pressure; mean vapor pressure; mean cloudiness; mean humidity; sunshine hours; mean dew point temperature; mean wet point temperature; and mean vapor saturation pressure were identified as significant input variables by GT in five or more of the eight studied stations. Also, mean air pressure, mean cloudiness, and mean temperature were identified as significant input variables for Rs modeling by the PA method for more than four stations. Results indicated that although ANN-GT and SVM-GT showed better goodness-of-fit metrics, ANN-PA and SVM-PA had lower uncertainties for estimating Rs. According to the obtained results, almost all models showed that the higher the bandwidth (95PPU or P-factor), the greater the d-factor, and the lower the bandwidth, the lower the d-factor, SVM-PA has the lowest uncertainty among the four models. So, it can be seen that the lowest bandwidth also belonged to the SVM-PA model for Kiashahr with a P-factor of 0.8% and a d-factor of 0.06, although the Aliabad-E-Katoul had the lowest d-factor of 0.017 and a p-factor of 1%. The highest d-factor belonged to the ANN-GT model for a Bandar-E-Torkman with a d-factor of 0.817 and a p-factor of 76%. One reason for the high uncertainty in this model might be due to the number of input variables selected by the GT. Lower uncertainty is a major scale for choosing the optimal model for solving a given problem, suggesting results of the SVM-PA model with lower uncertainty are more reliable.

Bias in modeled solar radiation by non-resolved intra-daily AOD variability

Attenuation of broadband solar irradiance by aerosols has been traditionally evaluated in wide areas using aerosol optical depth (AOD) discretized at monthly time steps. This fact is known to induce a bias in the long-term solar irradiance evaluations. Nowadays weather models allow for a spatially-continuous description of AOD at sub-daily steps (e.g., hourly or 3-hourly). But considering the still high uncertainty of their modeled AODs in relation with the natural sub-daily AOD variability, a key question is whether the attenuation of solar irradiance by aerosols could simply be accounted for from daily AOD means. Hence, the bias induced by the non-resolved sub-daily AOD variability should be investigated. This work proposes a mathematical methodology to understand the roots of such a bias and uses AOD observations from 213 ground sites to investigate it. Overall, the mean and standard deviation of the bias at evaluating the daily mean global horizontal solar irradiance stay at about −0.2 and 1.2 W/m2, respectively, or −2.0 and 5.5 W/m2 for direct normal irradiance. The mean of the daily bias nearly vanishes using the proposed methodology while the standard deviation reduces to only 1.0 (3.3) W/m2 in global horizontal (direct normal) solar irradiance.

Empirical estimation of daily global solar radiation with contrasting seasons of rain and drought characterize over tropical China

Despite a number of publications which have used readily available meteorological data to estimate daily Rs in a tropical environment with distinct dry and wet seasons, relatively few studies have focused on the dry and wet seasons independently. In this study, we compared 11 widely used empirical Rs estimation models using routinely measured meteorological data from the period 1957–2013 between the wet season and the dry season at 11 locations in tropical China. In terms of 4 statistical evaluation indicators, the top 3 performing sunshine-based models were, in rank order, M4, M5, and M2 over the entire year and M5, M2, and M1 (A-P model) during the dry and wet seasons. Meanwhile, the top 3 performing temperature-based models were, in rank order, M11, M10, and M7 over the entire year, M9, M10, and M11 in the dry season, and M11, M9, and M8 in the wet season. A comparison of the reported results revealed that the empirical models calibrated in the dry season performed better than in other seasons. For the temperature-based models, it was essential to develop the M6 (H-S), M7, and M9 models in the dry and wet seasons independently, eventhough their coefficients exhibited little significant difference between the dry and wet seasons according to ANOVA (P ≤ 0.5). This study revealed that daily Rs estimations could be developed with high accuracy during the dry and wet seasons independently in regions with contrasting seasons of rain and drought using only temperature data.

Evaluation and Comparison of Temperature-Based Models for The Prediction of The Monthly Average of Daily Global Solar Radiation for Baghdad City- Iraq

The solar energy is the major source of power for the future and an important source of renewable energy in Iraq and the world. Suitable climate conditions for solar energy are available in Iraq, especially the high temperature in the summer season which extends for more than six months in the year. Hence, the global solar radiation is abundant with high intensity, which is very essential in applicable models for researchers and solar applications. Therefore, nine first-order regression empirical equations of Angstrom-type correlations were used to estimate the more appropriate global solar radiation model for Baghdad city. Two equations were developed empirically in this work, using the most available and easy to get meteorological data, which is the temperature value in various forms. The results of the comparison between the real and calculated values showed reasonable agreement for most equations (including the development of R2- at 99%) as well as the least values of types of errors RMSE and MBE. As an exception, two equations of models failed in this study because of inability to apply to such climate conditions of Baghdad city.

Potential of Global Solar Radiation as Future Energy Source in Southern Region of Peninsular Malaysia

Energy sector in Malaysia is still adopting conventional method in electricity generation by using fossil fuel. The effect of fossil fuel utilization in electricity generation is releasing of greenhouse gases (GHGs) in the atmosphere specifically carbon dioxide (CO2). Releasing of uncontrolled GHGs may cause the earth to suffer from global warming. This paper is conducted to study the solar radiation potential at Southern Region of Peninsular Malaysia specifically in Iskandar Malaysia (IM) Region. The daily and annual means of solar radiation is analysed in order to examine the potential of solar energy to be energy source for electricity generation in the future. The results in this study indicate that the annual average of global solar radiation is higher in year 2010 with value of 13.31 MJ/m2/day whilst the lowest value of global solar radiation is recorded in year 2013 at 12.48 MJ/m2/day. The daily maximum global solar radiation values was collected on month of May and September during Southeast Monsoon. The daily minimum global solar radiation values was collected on January and December during Southeast Monsoon. The results also indicate that the maximum and minimum solar radiation captured 694 kWh/day and 651 kWh/day respectively. The results achieved in this study show that the potential to adopt solar energy as future energy source is very encouraging for energy sector.

Evaluation and estimation of daily global solar radiation from the estimated direct and diffuse solar radiation

There are various empirical models used in the estimation of global solar radiation; however, knowledge of direct and diffuse solar radiation is insufficient. Global solar radiation is the sum of direct and diffuse solar radiation, and a method that calculates global solar radiation from the estimated direct and diffuse solar radiation was further proposed in this study. The observed daily solar radiation and meteorological data from 97 stations during 1993–2016 were used for the analysis, and the results indicated that the concave-shaped relationship with relative sunshine duration was more obvious for direct solar radiation than for global solar radiation, while an inverted u-shaped relationship was found for diffuse solar radiation. Generally, the performances of empirical models in estimating direct and diffuse solar radiation were worse than the estimation of global solar radiation. However, because the bias of estimated direct and diffuse solar radiation was partially offset, the results in this study indicated that global solar radiation can be better calculated from the estimated direct and diffuse solar radiation when compared with the best performed empirical model, especially in data-scarce regions. The results of this study will aid in better estimations and understanding of the variations in global solar radiation, as well as direct and diffuse solar radiation.

Prediction of Daily Global Solar Radiation using Resilient-propagation Artificial Neural Network and Historical Data: A Case Study of Hail, Saudi Arabia

Prediction of Daily Global Solar Radiation using Resilient-propagation Artificial Neural Network and Historical Data: A Case Study of Hail, Saudi Arabia

Prediction of Daily Global Solar Radiation using Resilient-propagation Artificial Neural Network and Historical Data: A Case Study of Hail, Saudi Arabia

In this paper, several different Feed Forward Artificial Neural Networks (FFANNs) were used for forecasting the one-day-ahead Global Horizontal Irradiation (GHI) in Hail region, Saudi Arabia. The main motivation behind predicting GHI is that it is a critical parameter in sizing and planning photovoltaic water pumping systems. The novelty of the proposed approach is that it employs only the historical values of the GHI itself as explanatory variables and a fast training algorithm (resilient-propagation). In terms of performance metrics, the rp-trained FFANNs provided better results than Quasi-Newton (bfg) algorithm trained FFANNs for almost all the studied combinations of the FFANN structure. It has been also shown that increasing the number of neurons per layer didn’t improve the performance. Medium structures with fast training algorithms are recommended.

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

AbstractAccurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

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.

Measurement of global and direct normal solar energy radiation in Seri Iskandar and comparison with other cities of Malaysia

Potential solar data are an essential tool for successful solar design and application. However, because of the limited availability of solar radiation stations, spatial resolution is affected whenever an attempt to construct a solar radiation map is made. In this paper, actual solar data were acquired in Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Malaysia (4°24′latitude, 100°58′E longitude, 24 m altitude). The measurements of global solar radiation and direct normal radiation were gathered and analyzed for the whole of 2018. In addition to solar data collection, real-time solar radiation, high accuracy, and related meteorological data were also obtained. With 1-min recorded average values the everyday and monthly solar radiation averages were determined. A record of 1068.10 W/m2 as maximum daily global solar radiation and 915 W/m2 for direct normal radiation was observed on 9 September 2018. Discussions on daily and monthly average clearness index differences are also elaborated in this paper. The acquired data were compared with corresponding data obtained from other selected Malaysian cities and the widely useable data resource, the NASA solar energy model and surface meteorology. Investigation of the data indicated that Seri Iskandar obtains an ample amount of global solar radiation, indicating the strong potential for the use of solar energy.

Prediction of Horizontal Daily Global Solar Irradiation Using Artificial Neural Networks (ANNs) in the Castile and León Region, Spain

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.

New temperature-based predicting model for global solar radiation using support vector regression

ABSTRACT This paper proposes a new approach for daily horizontal global solar radiation prediction using only air temperature as an input. The proposed approach is based on the support vector machine (SVM) by fusing the outcomes of two SVM models. The first one is used to estimate the horizontal global solar radiation, whereas the second one is used to estimate the error produced by the first model. Finally, a weighted sum is applied to predict the global solar radiation. The proposed approach (Corrected-SVM) has been evaluated for Ghardaïa site (Algeria) which is characterised by semi-arid climate conditions, using the measurement of three years (2013–2015). It was found that the proposed Corrected-SVM model improves the performance of the conventional SVM-model. The obtained statistical parameters (mean absolute bias error (MABE), root mean square error (RMSE), relative square error and correlation coefficient (r)) of the C-SVM and conventional SVM model are 1.623, 2.286 MJ/m2, 11.35, 94.20% and 1.713, 2.575 MJ/m2, 12.61, and 93.06%, respectively.

The Solar Cell Parameters as a Function of Its Temperature in Relation to Its Diurnal Efficiency

The variation of the temperature of the solar cell subjected to the incident global solar radiation along the local daytime in relation to its efficiency is studied. The heat balance equation is solved. The solution revealed that the cell temperature is a function of the maximum value of the daily incident global solar radiation qmax, the convection heat transfer coefficient (h), the optical, physical and the geometrical parameters of the cell. The temperature dependence of the short circuit current Isc, the dark saturation current Io, the open circuit voltage Voc, and the energy band gap Eg characterizing a Silicon solar cell is considered in evaluating the cell efficiency. Computations of the efficiency concerning operating conditions and astronomical locations (Egypt) as illustrative examples are given.

Empirical Models for the Evaluation of Global Solar Radiation in the Different Sites of Nepal

To operate many phenomenon’s on the earth surface such as physical, chemical and biological process solar radiation plays vital role. A common practice is to estimate average daily global solar radiation (GSR) using appropriate empirical models for the areas lacking the actual measured values. In this context several single and multiple meteorological parameters were selected to estimate the GSR for Jumla, Nepalgunj and Kathmandu. In order to validate the selected models various statistical test were employed. The selected models were compared on the basis of statistical errors. In the statistical analysis the value of root mean square error (RMSE) and coefficient o determination R2 is found to 0.15.0.23, 0.26 and 0.98, 0.96, 0.96 respectively for Jumla, Nepalgunj and Kathmandu in Samuel model. These values were comparatively better than other models. It is concluded that Samuel model (order three) is the best among the used models. The established result uncover that there is a good possibility of solar energy as a alternative energy source in Nepal.

Solar Energy Potential and Its Utilization in Perovskite-Based Solar Cells for Selected Sites in Nepal

The measurements of solar radiation for Biratnagar (BRT), Kathmandu (KTM), Pokhara (PKR) and Jumla (JML) have been undertaken using CMP 6 pyranometers from SAHR/IOE/TU, Nepal. Solar radiation and the other meteorological data have been collected from the archives of Department of Hydrology and Meteorology, Government of Nepal (DHM/GoN) to analyze the daily Global Solar Radiation (GSR). In this study, perovskite-based solar cells with the configuration Au/SpiroOMETAD/MAPbI3/TiO2/FTO have been simulated using Solar Cell Capacitance Simulator (SCAPS). The power conversion efficiency (PCE) of the cell is found to be 22.67, 22.69, 22.77 and 22.80% in BRT, KTM, PKR and JML respectively, almost similar and better performance, whereas the solar cell performs better in JML due to the high solar irradiance.

Approximation of Empirical and ANN Based Solar Radiation Models for Four Smart Cities in Tamil Nadu with Most Prompting Input Parameters

The abundant source of Solar Radiation is possible in different latitude of Tamil Nadu. The several Empirical models have developed and accuracy of the model is further validated with Artificial Neural Network models. The ANN and Empirical model is developed with different combination of input attributes. The Approximate Sunshine Solar Radiation, Temperature and Relative Humidity are the input attributes. In this paper, it has been evaluated that combination of Approximate Sunshine Solar radiation and Temperature ANN based model is best for prediction of Daily Global Solar Radiation when compared to ST based Empirical model. The Correlation analysis is performedfor selection of most relevant input attributes. And hence the estimation of daily global solar radiation is performed with minimum number of input attributes. The training and testing of each model is performed by ANN Simulink model. And from this trainingofinputvariables,theoverfittingofthemodelisreduced. The estimated GSR is validated with experimental data which is collected from Tamil Nadu AgricultureUniversity.

National-scale development and calibration of empirical models for predicting daily global solar radiation in China

Accurate global solar radiation (Rs) information is pivotal to the design and management of solar energy systems. Nevertheless, the expensive devices for Rs measurements make Rs data always unavailable in many regions around the world. The empirical models that predict Rs using other widely available climatic variables are feasible alternatives when Rs measurements are unavailable. However, the parameters of empirical models are site-specific and always need local calibration. In this context, the present study firstly developed a novel empirical model for accurately predicting Rs at the national scale in China, and then compared the new model with nineteen locally calibrated empirical models that have been largely reported in prior studies, including seven sunshine-based, nine temperature-based, and three complex empirical models. Daily Rs and other meteorological data during 1994–2016 from 96 weather stations in China were used to calibrate/develop and assess the models. The results showed that the newly proposed C4 model generally offered the best prediction accuracy among the models, with average MAE of 1.69 MJ m−2 d−1, RRMSE of 16.2% and NS of 0.895, which can be recommended as the optimal model for predicting Rs in China. The models reviewed and developed in this study improved the prediction accuracy of Rs, which can provide crucial information for the design and implementation of solar photovoltaic and thermal systems.