Daily Global Solar Radiation Research Articles

Prediction of Global Solar Radiation across the Region of Northern Sudan

Available data for solar resources indicates that northern Sudan has solar potentials among the highest in world. However, in this vast area (about 471×103 km2) only two stations, at Hudeiba and Dongola, have records for global solar radiation. Four other stations (at Atbara, Karima, Abu-Hamed and Wadi-Halfa) in this region have records for sunshine duration. In this study, recorded data at Hudeiba and Dongola stations have been used to develop 15 models for estimation of monthly average daily global solar radiation on a horizontal surface in northern Sudan. The developed models have been extensively evaluated by using 14 statistical indicators. All developed models revealed good performance (percentage errors within ±4.5%). Then, the most accurate regional model (Model M3) has been used to predict the global solar radiation at Atbara, Karima, Abu-Hamed and Wadi-Halfa stations. Results showed that the whole region receives abundant solar energy throughout the year. The yearly average of daily global solar radiation over the entire region is 23.16 MJ/m2. Furthermore, the developed models can be used to predict global radiation at adjacent places that lack such data.

Temporal Evolution, Oscillation and Coherence Characteristics Analysis of Global Solar Radiation Distribution in Major Cities in China’s Solar-Energy-Available Region Based on Continuous Wavelet Transform

The majority of the energy required for human survival is derived either directly or indirectly from solar radiation, thus it is important to investigate the periodic fluctuations in global solar radiation over time. In this study, six cities—Harbin, Shenyang, Beijing, Shanghai, Wuhan, and Guangzhou—located in the utilizable areas of solar energy in China, were selected, and the periodicity of the daily global solar radiation of these six cities with time were investigated by means of wavelet power spectrum analysis. Furthermore, Harbin, which has the lowest monthly average of global solar radiation in the cold of winter, was selected to explore the temporal evolution relationship between global solar radiation and other meteorological factors, and air quality factors by wavelet coherence analysis. Among the meteorological factors, the correlation between global solar radiation and sunshine duration exhibits the highest level of consistency. Global solar radiation demonstrates a positive association with atmospheric temperature/wind speed/precipitation over the annual cycle. Conversely, it displays a negative correlation with atmospheric pressure during this time frame. Additionally, on shorter timescales, global solar radiation is negatively correlated with air humidity and precipitation. Among air quality factors, the relationship between global solar radiation and CO/NO2/O3/PM2.5/PM10/SO2 primarily manifests in the broader annual cycle time scale. Within this context, global solar radiation shows a positive correlation with O3, while displaying negative associations with the other five air quality indicators. These findings contribute to urban environmental planning and the effective management and utilization of solar radiation, thereby providing valuable insights to guide the future development of cross-regional comprehensive energy utilization projects under diverse climatic and geographical conditions. Additionally, they serve as a reference for subsequent research aimed at enhancing the accuracy of global solar radiation prediction models.

Prediction of daily global solar radiation in different climatic conditions using metaheuristic search algorithms: a case study from Türkiye

Today’s many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Ağrı) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Ağrı province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.

Correction to: Estimation of evapotranspiration through an improved daily global solar radiation in SEBAL model: a case study of the middle Heihe River Basin

Correction to: Estimation of evapotranspiration through an improved daily global solar radiation in SEBAL model: a case study of the middle Heihe River Basin

NEW MODELS FOR ESTIMATION OF DIFFUSE SOLAR RADIATION USING METEOROLOGICAL PARAMETERS FOR BENIN, NIGERIA

In this comprehensive study, an extensive 22-year dataset (2001-2022) for Benin (Latitude 6.32 oN, Longitude 5.10 oE and 77.80 m above sea level) were obtained from the National Aeronautic Space Administration (NASA) website. The datasets comprises of the monthly average daily global solar radiation, diffuse solar radiation, relative humidity, atmospheric pressure, wind speed, and mean temperature, was utilized to develop 19 new models for estimating diffuse solar radiation. These models were categorized into five distinct groups: modified Page, Liu and Jordan models; clearness index and one-variable models; two-variable models; three-variable models, and a four-variable model. These models were statistically evaluated using a set of five validation indices—Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, and the coefficient of determination (R²). The study identified the most effective models in each category. Equation 28b from the modified Page, Liu and Jordan category, Equation 28f from the clearness index and one-variable models, Equation 28j from the two-variable models, and Equation 28o from the three-variable models category were found to be the best-performing models. A comparative assessment of these performed models revealed that the quadratic regression model (Equation 28b) stood out as the most suitable for accurately estimating diffuse solar radiation in Benin. This implies that the developed model equation 28b can be used to estimate the diffuse solar radiation for Benin and locations with similar climatic conditions

New independent daily global solar radiation estimation models based on the day number of the year

ABSTRACT Solar radiation data are necessary information for the analysis and prediction of certain environmental processes and the planning of related energy projects. Many models have been proposed in the literature for their estimation due to their unavailability in some locations, especially in developing countries. However, peoples are currently finding more accurate and simple models. One type of these models is the day of the year-based (DYB) model, which has as merit over others, the fact that it does not depend on any environmental parameter. In this work, nine existing DYB models are calibrated with Levenberg–Marquardt nonlinear curve fitting technique for the estimation of solar radiation in the regional cities of Cameroon. In addition, two new DYB models are proposed. The results show that one of the two proposed models (M11) best estimates the solar radiation in nine sites and is ranked second in one site with the root mean square error in the range [0.2365; 0.3039], the mean absolute percentage error in the range [0.1873; 0.2348], and the correlation coefficient in the range [0.8332; 0.9164]. The new model M11 is therefore recommended for estimating solar radiation of some sites of Cameroon and in regions with similar climate.

Estimation of Daily Global Solar Radiation with Different Sunshine-Based Models for Some Burundian Stations

Estimation of Daily Global Solar Radiation with Different Sunshine-Based Models for Some Burundian Stations

Enhanced Daily Global Solar Radiation Prediction Through Hybrid Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System with Meta-Heuristic Algorithm Integration

Enhanced Daily Global Solar Radiation Prediction Through Hybrid Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System with Meta-Heuristic Algorithm Integration

Estimation of evapotranspiration through an improved daily global solar radiation in SEBAL model: a case study of the middle Heihe River Basin

Estimation of evapotranspiration through an improved daily global solar radiation in SEBAL model: a case study of the middle Heihe River Basin

Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm

Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N).

Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model

Solar energy, as a clean energy source, has tremendous potential for utilization. The advancement of solar energy utilization technology has led to an increasing demand for solar energy, resulting in a growing need for the accurate prediction of solar radiation. The main objective of this study is to develop a novel model for predicting solar radiation intervals, in order to obtain accurate and high-quality predictions. In this study, the daily sunshine duration (SD), average relative humidity (RHU), and daily average temperature (AT) were selected as the indicators affecting the daily global solar radiation (DGSR). The empirical study conducted in this research utilized daily solar radiation data and daily meteorological data collected at the Hami station in Xinjiang from January 2009 to December 2016. In this study, a novel solar radiation interval prediction model was developed based on the concept of “point prediction + interval prediction”. The Conformer model was employed for the point prediction of solar radiation, while the Generalized Laplace (GLaplace) distribution was chosen as the prior distribution to account for the prediction error. Furthermore, the Solar DeepAR Forecasting (SDAR) model was utilized to estimate parameters of the fitted residual distribution and achieve the interval prediction of solar radiation. The results showed that both models performed well, with the Conformer model achieving a Mean Squared Error (MSE) of 0.8645, a Mean Absolute Error (MAE) of 0.7033 and the fitting coefficient R2 of 0.7751, while the SDAR model demonstrated a Coverage Width-based Criterion (CWC) value of 0.068. Compared to other conventional interval prediction methods, our study’s model exhibited superior accuracy and provided a more reliable solar radiation prediction interval, offering valuable information for ensuring power system safety and stability.

Prediction of the daily global solar Irradiation of the great Maghreb region using the complex-valued neural networks

In this paper, the prediction of the daily global solar irradiation of the great Maghreb’s region using the complex-valued neural networks (CVNN) is presented. This paper is an extension of our recent published work which is the complex-valued forecasting of the global solar irradiation. Both multi-input single-output (MISO) and multi-input multi-output (MIMO) strategies are considered. The data of the capitals of the great Maghreb, which are Tripoli (Libya), Tunis (Tunisia), Algiers (Algeria), Rabat (Morocco), El Aaiun (Western Sahara) and Nouakchott (Mauritania), are used like sample from each country. To test the applicability and the feasibility of the CVNN to predict the daily global irradiation for the great Maghreb case, several models are presented. Results obtained throughout this paper show that the CVNN technique is suitable for prediction of the daily solar irradiation of the region of region of the great Maghreb.

Evaluation of the global solar irradiation from the artificial neural network technique

In this study, many experiments were carried out to assess the influence of the some input parameters on performance of the multilayer perceptron techniques, which is architectures one of the artificial neural network (ANN). To estimate the daily global solar irradiation (GSI) on horizontal surface, we have developed eight models by using four weather input parameters collected from a radiometric station installed at Ghardaia site (southern of Algeria), such as sunshine duration, daily mean temperature, daily relative humidity, and Solar declination. In order to select the best configuration among the chooses combinations which provides a good accuracy, three statistical formulas (or statistical indicators) have been evaluated, such as the normalized root mean square errors, (nRMSE), normalized mean bias error (nMBE, and coefficient of determination R²). We noted that the ANN model provides best performance compared to the developed empirical models. results of the nMBE, nRMSE, and R² are about 0.048%, 4.422%, and 98.00%, for ANN and 0.0772%, 5.5381%, and 97.22% for the quadratic model. Moreover, it was proved that the sunshine duration is important parameter, for predicting the GSI.

Climate risks and vulnerabilities of the Arabica coffee in Brazil under current and future climates considering new CMIP6 models

The susceptibility to climate change concerns the coffee market worldwide due to possible severe productivity losses. Brazil is the world's largest Arabica coffee producer and has crops in regions considered persistent climate change hotspots. Our study analyzed risks, vulnerabilities, and susceptibilities to pests and diseases in these regions under current and future climates and outlined adaptive measures to reduce future vulnerabilities. Ten risk indicators based on Arabica coffee requirements were proposed: water supply (Iw), base (TIB) and maximum temperature stresses (TImax), which delimit the temperature range where Arabica coffee grows and productivity is penalized outside both ranges, frost stress (TIfrost), diseases such as rust (DIrust), brown eye spot (DIbrown), and Phoma leaf spot (DIphoma), pests such as coffee berry borer (PIberry), coffee leaf miner (PIminer), and yield loss due to water stress (Iyg). Daily near-surface air temperature (minimum, mean, and maximum), relative humidity, precipitation, and global solar radiation were used from 16 General Circulation Models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in three Shared Socioeconomic Pathways scenarios (SSP245, SSP370 and SSP585). All risk indicators were calculated for the current climate (1995–2014) and projected for the near (2041–2060), intermediate (2061–2080), and far future (2081–2100) in three SSPs and then classified into five risk classes (very low, low, moderate, high and very high). Our results indicated that due to increases in TImax and Iyg indicators, with high to very high risk in area and magnitude, Arabica coffee plantations will be negatively affected and economically unfeasible for about 35 % to 75 % of the studied area throughout the 21st century. Furthermore, the rust and the leaf miner will remain a concern in future climates due to increased temperatures and reduced relative humidity. The future of Arabica coffee crops in Brazil will depend on adopting effective adaptive measures and prudent agricultural strategies to address anticipated risks, including shifting crops to higher altitude areas, introducing more climate-resilient coffee cultivars/varieties, using agroforestry or intercropping systems, planting in closer spacing or higher density planting, and employing dripper or partial root-zone irrigation techniques.

Estimation of daily global solar radiation based on different whitening applications using temperature in Mediterranean type greenhouses

The study aimed to estimate the daily global solar radiation (Rs) in Mediterranean-type greenhouses. Five different temperature-based Rs estimation models developed for open-field conditions were calibrated and validated in Mediterranean-type greenhouses in Almeria, Spain and Antalya, Türkiye, between August 26, 2013, and January 1, 2023, and between October 1, 2018, and 1 January 2023, respectively. Whitening applications were categorized according to greenhouse light transmissivity and classified as follows: without whitening or light-whitening, medium-whitening, and severe-whitening. Additionally, the best-performing model were compared with greenhouse plastic light transmissivity method. The estimation performance of the models was evaluated using the statistical indicators of the p-value of the slope, determination coefficient (R2), Nash–Sutcliffe model efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE), relative error (RE), and Willmott Index (d). Compared with the other models, the Bristow and Campbell model showed a slightly higher performance in all whitening applications. Although the light transmissivity coefficient method performed slightly better than the temperature-based Rs estimation model, there was no statistical difference in the performances of the estimation models. Temperature-based estimation models offer a highly viable alternative for individuals who rely on the light transmittance approach to estimate Rs in greenhouses. This method can prove particularly useful in areas where measuring Rs outside the greenhouse is not possible or where partial time measurements cannot be taken owing to equipment malfunctions. All calibrated models can be used to estimate solar radiation using temperature data from various Mediterranean countries with similar climates and greenhouse cultivation.

Hybrid Models for Daily Global Solar Radiation Assessment

Daily solar radiation forecasting has recently become critical in developing solar energy and its integration into grid systems. Despite the huge number of proposed forecasting techniques, an accurate estimation remains a significant challenge because of the non-stationary variation of solar radiation components due to the continuously changing climatic conditions. Usually, several input data predictors are used for the forecasting process, which can cause redundancy and correlation between data features. This work assesses a set of feature selection techniques to check their ability to select the relevant predictors and reduce redundant and irrelevant information. An Artificial Neural Network is used to fit the measured solar radiation based on the selected features. The developed model is evaluated through various objective evaluation metrics using historical data of three years measured at the Ghardaiaregion inAlgeria. Results show the effectiveness of the proposed method, where values of 0,0189, 0.0286, 5.4387, and 98.28% have been found as MABE, RMSE, nRMSE and r, respectively.

Sunshine and Temperature Based Models for Estimating Global Solar Radiation in Maiduguri, Nigeria

Nine existing sunshine based models and three existing temperature based models were evaluated and compared to ascertain the most suitable models for estimating global solar radiation in Maiduguri; the most suitable sunshine based and most suitable temperature based models were also compared. The measured monthly average daily global solar radiation, sunshine hours, maximum and minimum temperature meteorological parameters during the period of thirty one years (1980 – 2010) was utilized and the evaluated models were tested statistically using validation indicators of coefficient of determination, Mean Bias Error, Root Mean Square Error, Mean Percentage Error, t – test, Nash – Sutcliffe Equation and Index of Agreement. The results indicated that the linear exponential sunshine based model and the logarithmic temperature based model were found more accurate for global solar radiation estimation in Maiduguri as compared to other evaluated models. Furthermore, the logarithmic temperature based model was found more accurate for estimating global solar radiation as compared to the linear exponential sunshine based model, and this was testified from the figure showing the comparison between the recommended sunshine based and temperature based models in which the recommended temperature based model depicts the best fitting with the measured global solar radiation data.

Short–term global solar radiation forecasting based on an improved method for sunshine duration prediction and public weather forecasts

Accurate forecasting of daily global solar radiation (Rs) is important for photovoltaic power and other sectors. Numerical models coupled with public weather forecasts information is a feasible method to predict short–term daily Rs. Here, we propose a novel sunshine duration converting method (n_new) based on forecasted air temperature and weather types data, which we validated using measurements from 86 radiation stations. A widely-used, generalized sunshine–based Rs model (Rs_n) was then coupled with the n_new method (Rs_n new) for forecasting daily Rs. This was further compared to Rs_n incorporated with the common sunshine duration converting method (n_com) using only weather types data (Rs_n com) and a recently developed generalized temperature–based model (Rs_T). The results indicated that the n_new method produced better estimates than the n_com method, as indicated by increased mean correlation coefficient (R; 13.0%–24.5%) and index of agreement (dIA; 2.9%–9.5%) and decreased mean root mean squared error (RMSE; 12.8%–14.8%) for the 1–7 days lead time over 86 sites. The Rs_n new model improved the accuracy for 98% of sites when compared to the Rs_n com model, with mean values of R and dIA increasing by 7.7%–11.0% and 2.1%–4.8% and that of RMSE decreasing by 9.7%–12.5% for the 1–7 days lead time. The results suggest that the Rs_n new model is advantageous in short–term forecasts. The Rs_n new model ranked first for 52.3%–74.4% of sites for the 1–7 days lead time, followed by the Rs_T model (25.6%–47.7%). Moreover, there was generally a better performance for the Rs_n new model to forecast daily Rs at a longer lead time. Therefore, the Rs_n new model using weather forecasts information is highly recommended to forecast short–term daily Rs.

Development criterion of estimating hourly global solar radiation for all sky conditions in China

The high quality hourly global solar radiation data becomes more and more important under the trend of low building energy consumption and carbon emission. The models based on daily global solar radiation and hourly meteorological parameters are two kinds of efficient methods to compensate for measured data deficiency. However, the applicable climate and sky conditions of two models are needed to be clarified. Based on 45 stations in China, the coupling correlations between hourly global solar radiation and meteorological parameters are obtained by correlation analysis. The eight decomposition models and four models based on the main hourly meteorological parameters are developed and evaluated for climates and sky conditions using the mean absolute bias error, relative root mean squared error, and Nash-Sutcliffe efficiency coefficient. Considering the advantages of two kinds of models, the criterion of estimating hourly global solar radiation data is developed. The results show that hourly sunshine duration and hourly surface temperature are the main factors reflecting the change of hourly global solar radiation. In humid and sub-humid climates, the hourly relative humidity can further characterize the fluctuation of hourly global solar radiation. CPR model performs best among decomposition models, especially in sunny sky conditions. The meteorological parameters models own higher accuracy in cloudy conditions. In regions with hourly sunshine duration measurement, CPR model is recommended when daily clearness index is higher than 0.7 and lower than 0.2. When daily clearness index varies from 0.2 to 0.7, the model should be established by selecting hourly sunshine duration and hourly surface temperature in arid and semi-arid climates and hourly sunshine duration, hourly surface temperature and hourly relative humidity in humid and subhumid climates, respectively. In regions without hourly sunshine duration measurement, CPR model should be used regardless of weather conditions and climate types.

Comparison of Models to Evaluate Daily Available Energy with Photovoltaic Array at Sirinka, Ethiopia

Abstract: Background: Energy utilization in Ethiopia is mainly based on traditional biomass, which causes indoor air pollution, especially for women and children (causing acute lower respiratory infection) living in rural areas. Objective: This research was conducted to assess the performance of six empirical models for estimation of daily global solar radiation (DGSR) at Sirinka sites to evaluate daily available energy with photovoltaic (PV) array and energy available to the load (energy demand) and battery (energy-storage device). Materials and Methods: In this study, sunshine hours, minimum and maximum temperatures were obtained from Kombolcha Meteorological Agency. Ms-Excel 2010 was employed as a descriptive statistics tool and MATLAB 2013a was used as software to plot the analyzed data. Results: According to the statistical performance evaluation metrics, such as normalized mean bias error (NMBE) and normalized root mean square error (NRMSE), Angstrom Prescott model (AP), Louche model (LO) and Hargreaves model (H) performed best in order. The smallest daily mean power delivered to PV and the daily mean energy available to the load (energy demand) and battery (energy-storage device) were 288.11 W/m2 and 248.92 W/m2 sequenced and the largest mean power delivered to the PV and the mean energy available to the load and battery were 851.57 W/m2 and 735.76 W/m2 in order on April-10 for the study period of 2014–2018. Conclusion The result of this work showed high solar energy potential and therefore, rural electrification using PV system is possible in the study area and can reduce health problems due to indoor pollution in the future. Keywords: Daily global solar radiation, Empirical Model, environmentally friendly, Renewable Energy, Solar Power, Sunshine, Temperature, Photovoltaic.