Models For Global Solar Radiation Research Articles

Monthly Global Solar Radiation Model Based on Artificial Neural Network, Temperature Data and Geographical and Topographical Parameters: A Case Study in Spain

Solar energy plays an essential role in the current energy context to achieve sustainable development while supplying energy needs, creating jobs, and protecting the environment. Many solar radiation models have provided valid estimates at many different locations, using appropriate input variables for specific climatic conditions, but predictions are less accurate on a regional scale. Since radiometric weather stations are relatively dispersed, even in the most developed countries, it is interesting to develop indirect models based on measurements that are common in secondary network stations. This paper develops a monthly global solar radiation model based on a simple neural network structure, using temperature, geographical, and topographical data from 105 meteorological stations, representative of the whole of peninsular Spain. A hierarchical clustering procedure was employed to select the data used to train and validate the model. To avoid functional dependencies between parameters and variables, which hinder the generality of the model, all input and output variables are dimensionless. The estimates fit the 1260 monthly data with RRMSE values of about 6%, which improves results obtained previously, using regression models, and proves that simplicity is compatible with the generality and accuracy of a model, even in large regions with very varied characteristics.

Composition of feature selection techniques for improving the global horizontal irradiance estimation via machine learning models

The rise in photovoltaic capacity installed globally and the sporadic nature of solar resources underline the significance of solar radiation forecasting for grid integration. The choice of relevant climatic variables for estimating global horizontal irradiance (GHI) is crucial because it influences estimation accuracy. To keep this in mind, different feature selection techniques such as filter, wrapper, and embedded are computed and compared to identify a pertinent input variable for the prediction of GHI. In order to reduce the dimensionality of the input features, it is pertinent to remove unnecessary variables using feature selection techniques. This comprehensive study explores the use of tree-based ensemble machine learning (ML) models in the modeling of global solar radiation. Eighteen statistical and ML regressor algorithms, along with ten feature selection methods are evaluated based on statistical comparisons of their computational effectiveness and estimation errors. The lowest mean absolute error (MAE) and root mean squared error (RMSE) of 0.9938 and 2.5327 achieved by the extra-tree regressor algorithm reveals that it outperforms the other models on the test dataset. The integration of feature selection techniques improves predictive capabilities, reducing MAE and RMSE by 13.15% and 5.24%, respectively, for the Mutual Information method. This study also identifies influential features critical to accurate GHI estimation, such as solar zenith angle, diffuse horizontal irradiance, and direct normal irradiance, which provide valuable insights into the dynamics governing solar radiation estimation.

Evaluation and performance comparison of different models for global solar radiation forecasting: a case study on five cities

AbstractRecently, solar energy has emerged as the most promising renewable energy source to meet the world’s energy demands. However, to harness the potential of solar energy, accurate data on solar radiation are crucial. It is considered the first step in assessing solar resources for various applications and achieving energy sustainability goals. Due to the unavailability of solar radiation measurements in many parts of the world, several models have been developed to predict global solar radiation (GSR) at these locations. Thus, this study aims to evaluate the proficiency of several GSR models at five new locations and determine the most suitable one for GSR prediction. The study has further developed solar radiation models for these new locations, as well as general ones for the entire region, which does not have any GSR models despite the existence of many planned solar energy facilities in this area. Additionally, the study investigates the effect of changing the length of the validation dataset on models’ performance and accuracy, as well as assesses the introduced models’ generalization capability. To achieve these objectives, the observed data of GSR for approximately 37 years at studied locations are used to develop and validate the proposed models. The study’s findings reveal that Model 1 provides the best performance at all locations, with accuracy, coefficient of determination ($$R^{2}$$ R 2 ), ranging from 95 to 98%, except for the coastal location, where it is from 91 to 95%. The remaining performance indicators of the best models, such as RMSE, MABE, MAPE, and $$r$$ r , are good, and their values range from 0.7863 to 1.9097 (MJ m−2 day−1), from 0.6430 to 1.7060 (MJ m−2 day−1), from 3.4319 to 10.0890 (%), and from 0.9914 to 0.9981, respectively. The length of the validation dataset has a slight effect on the models’ performance, ranging from about 1% to 2%. Therefore, Model 1 is the recommended solar radiation model, which can provide precise and rapid estimates of global solar radiation. This approach could be used in the design and performance evaluation of many solar applications. The primary benefit of this approach in the current investigation is that temperature data are continuously and effortlessly recorded for various purposes.

Modeling global solar radiation using ambient temperature

Accurate information about solar radiation is regarded as the most important stage in determining the availability of solar energy. As well, it is considered the principal input for various applications of solar energy. Because solar radiation measurements are unavailable at many locations throughout the world, many solar radiation models have been developed to predict global solar radiation. In this regard, this study objects to develop accurate and quick Global Solar Radiation (GSR) models for new locations, which currently lack an accurate model. Additionally, assess the performance of a recently introduced model, one of the best temperature-based models for GSR estimation, in these five new sites. Moreover, the proposed model's generalization capacity is investigated over the whole zone, the Suez Canal Zone, and a comparative analysis of its performance is presented. Models’ estimation is compared to the observed values, and the most common performance indicators are obtained to assess models’ performance. The findings indicate that the developed models in this study can predict global solar radiation accurately. Where the Models’ performance, both the local and general models, are larger than 95 % at all sites except for the local one at Port Said City (coastal site), it is 91 %. Additionally, the developed models have good RMSE and MABE values which range from 0.8 to 1.8 (MJ/m2 day−1) and 0.7 to 1.7 (MJ/m2 day−1), successively. Besides, they have excellent performance with coefficient of determination (R2) values ranging from 0.95 to 0.98, whereas prior research's ranges are 0.884–0.895 for R2 and 2.69–3.367 (MJ/m2 day−1) for RMSE. Therefore, the developed models in this study can be utilized for accurate GSR forecast, with their high applicability which may be achieved by combining them with various long- or short-term weather forecasting approaches, that are mostly used to reliably anticipate weather temperature. As well, these models' precise and speedy computation of GSR can also be employed in the design and performance evaluation of various solar applications.

Calibration of the Ångström–Prescott Model for Accurately Estimating Solar Radiation Spatial Distribution in Areas with Few Global Solar Radiation Stations: A Case Study of the China Tropical Zone

The Ångström–Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Aiming at the characteristics of a vast area, few meteorological stations, and uneven distribution in the tropical regions of China, in order to obtain the optimal parameters of the global solar radiation calculation model, this study proposes a suitable monthly global solar radiation model based on the single-station approach and the between-groups linkage of the A–P model, which utilizes monthly measured meteorological data from 80 meteorological stations spanning the period from 1996 to 2016 in the tropical zone of China, considering the similarity in changes of monthly sunshine percentage between stations. The applicability and accuracy of the correction parameters (a and b coefficients) were tested and evaluated, and then the modified parameters were extended to conventional meteorological stations through Thiessen polygons. Finally, the spatial distribution of solar radiation in the tropical region of China was simulated by kriging, IDW, and spline interpolation techniques. The results show the following: (1) The single-station model exhibited the highest accuracy in simulating the average annual global solar radiation, followed by the model based on the between-groups linkage. After optimizing the a and b coefficients, the simulation accuracy of the average annual global solar radiation increased by 5.3%, 8.1%, and 4.4% for the whole year, dry season, and wet season, respectively. (2) Through cross-validation, the most suitable spatial interpolation methods for the whole year, dry season, and wet season in the tropical zone of China were IDW, Kriging, and Spline, respectively. This research has positive implications for improving the accuracy of solar radiation prediction and guiding regional agricultural production.

Using artificial intelligence for global solar radiation modeling from meteorological variables

Long-term quantification of solar energy variables at ground level is not easily achievable in many locations. In order to overcome this limitation, use of artificial intelligence such as the application of machine learning methods is commonly used for solar irradiance prediction.In this context, this study proposes the implementation of artificial neural networks as deep learning and the XGBoost algorithm as a machine learning method for modeling the hourly global solar radiation for a humid climate such as the Rabat region. For this purpose, hourly meteorological data from the city of Rabat in Morocco are chosen in order of importance using the random forests method, for training and testing the models, namely date and time, sunshine duration, temperature, relative humidity, wind speed/direction and pressure. Subsequently, models are selected after the validation phase for testing, whose performance is evaluated using relevant statistical indicators. As a result, we retain 2 ANN and 1 XGBoost models which are eventually very close in terms of performance with a coefficient of determination value equal to 98% and 97% respectively. However, statistical indicators have proven to be effective in assessing the accuracy and fidelity of each model.Ultimately, the intent of the modeling in terms of accuracy, simplicity or fidelity is a crucial factor in the selection of the model algorithm to adopt.

Temperature extremities-dependent global solar radiation models for country-wide applications in India

Calibration of models has been performed for estimating global solar radiation with input as daily temperature extremes. Measured data for 22 locations were obtained from the Indian Meteorological Department (India) for 15 years and ten global solar radiation model forms were selected. Data were then bifurcated, where sixteen locations were used for training and the remaining six for validation. Models were compared using the statistical tools-based error analysis. The ranking of models was introduced using a Global Performance Indicator. Period-based testing of models declared Chen et al. model as the superior one. Location-based validation of the models was further performed which indicated the best suitability of the Bristow and Campbell model for semi-arid climate, Hargreaves et al. model for Arid climate and montane climate, Samani model for Tropical wet climate, Goodin et al. model for Humid subtropical climate and wet-dry climate, with GPI values of 5.3965, 0.5325, 0.5174, 0.5182, 0.5333 and 0.5862, respectively.

The applicability of sunshine-based global solar radiation models modified with meteorological factors for different climate zones of China

With the development of renewable energy, the exploitation and utilization of solar energy resources also need continuous progress, but solar radiation data shortage has become a serious concern. A method for estimating global solar radiation has been developed to address this issue. The sunshine-based model is currently the most widely used model due to its high calculation accuracy and few input parameters. This paper will first review 13 subcategories (8 categories in total) of the global solar radiation prediction model based on sunshine. Subsequently, the astronomical factors were introduced to modify empirical coefficients, and 8 new categories of models based on sunshine rate were introduced. The radiation data from 83 meteorological stations in China was used to train and validate the model, and the performance of the model was evaluated by using evaluation indicators, such as coefficient of determination (R2), root mean square error (RMSE), mean absolute bias error (MABE), mean bias error (MBE), and global performance index (GPI). The results show that the R2 value of the unmodified empirical model is in a range of 0.82–0.99, and the RMSE value is in a range of 0.018–3.09. In contrast, with the introduction of the astronomical factor, the model accuracy improves significantly, and the modified power function model (N3) gains its best performance. The R2 of model N3 is in a range of 0.86–0.99, and the RMSE value is in a range of 0.018–2.62. The R2 increases by 0.49%, while the RMSE value 6.44%. Above all, it does not require the input of other meteorological parameters for predicting the value of global solar radiation.

New models for the influence of rainwater on the performance of photovoltaic modules under different rainfall conditions

Solar photovoltaic (PV) technology is considered one of the most promising clean energy technologies. Dust accumulation on the surface of photovoltaic modules is one of the important reasons for the decline of photovoltaic system performance, and the influence of rainfall is particularly significant. The purpose of this paper is to investigate the influence of rainwater on the output power of photovoltaic modules under different rainfall conditions. In this paper, a prediction model for dust density under different inclination angles is established based on outdoor test data, and the control variable method is used to design three different PV module performance testing schemes under different rainfall conditions, and a dimensionless model for output power is proposed. Empirical models of global solar radiation are combined, and thus output power prediction models under different rainfall conditions are developed. It is found that rainstorm conditions (Rainfall 50–100 mm) increased the peak PV module output power by 16.1%–28.2% compared to light rainfall conditions (Rainfall less than 10 mm), with the rain showing better cleaning effects for PV modules with higher dust densities. It is worth noting that PV module surface dust does not necessarily have only a negative effect on system performance. The influence mechanism of rainwater on the output power of photovoltaic modules and the prediction model established in this paper is helpful in improving the prediction accuracy of the output power of photovoltaic modules under rainfall conditions, and thus the efficient cleaning strategy is formulated.

Calibration and evaluation of estimation models of global solar radiation for the state of Goiás, Brazil

Despite the importance of global solar radiation (Rs), data of this variable are not available for many regions of Brazil due to low density of surface weather stations, mainly in the North and Central-West regions. The estimation of this variable for regions with no Rs data are based on mathematical models, which has shown to be a viable alternative. The objective of this work was to contribute to these studies through the calibration and evaluation of three traditional models: Angstrom-Prescott (AP), Hargreaves (HAR), and Bristow-Campbell (BC), and, in addition, propose a new model (PMo) for estimation of Rs from multiple regression. The models were adjusted and evaluated for six locations in the state of Goiás (Central-West of Brazil), using meteorological data from 2008 to 2016. The local adjustment process improved the predictive capacity of the models AP (r2 = 0.74 and MAE = 1.68), BC (r2 = 0.62 and MAE = 2.34) and HAR (r2 = 0.55 and MAE = 2.32). However, according to the Nash index, unsatisfactory performances were found for the HAR model for the municipalities of Mineiros and Rio Verde, and for the BC model for Rio Verde. Despite requiring more meteorological variables, the PMo model estimated Rs adequately for the evaluated regions (r2 = 0.70 and MAE = 1.98), with superior performance to BC and HAR methods.

Performance Evaluation and Statistical Analysis of Solar Energy Modeling: A Review and Case Study

The main target of this research is a quantitative review of literature on global solar radiation (GSR) models available for different stations around the world. The statistical analysis of 400 existing sunshine-based GSR models on a horizontal surface is compared using 40-year meteorological data in the selected locations in Egypt. The measured data is divided into two sets. The first sub-data set from 1980 to 2019 was used to develop empirical correlation models between the monthly average daily global solar radiation fraction (H/H0) and the monthly average of desired meteorological parameters. The second sub-data set from 2015–2019 was used to validate and evaluate the derived models and correlations. The developed models were compared with each other and with the experimental data of the second subset based on the statistical error indicators such as RMSE, MBE, MABE, MPE, and correlation coefficient (R). The statistical test of the correlation, coefficient (R), for all models gives very good results (above 0.92). The smallest values of t-Test occur around the models (M 272, M 261, M 251, and M 238). The accuracy of each model is tested using ten different statistical indicator tests. The Global Performance Indicator (GPI) is used to rank the selected GSR models. According to the results, the Rietveld model (Model 272) has shown the best capability to predict the GSR on horizontal surfaces, followed by the Katiyar et al. model (Model 251) and the Aras et al. model (Model 261).

Validation of Global Solar Radiation Models on a Horizontal Surface in the Climatic Zones of Burkina

The evaluation of the solar deposit is essential for the sizing of photovoltaic systems. This requires the availability of radiation data. In Burkina Faso, weather data doesn’t cover all the country. That makes that solar radiation measured are not available for the all country. Theorical methods can help about it. This paper is written to fill the gap of adapted solar models for the country. In this work, an analysis of the results provided by four models for the estimation of hourly values ​​of global radiation on a horizontal plane was made. The radiation data for the year 2017 comes from the Burkina weather forecast. The validation of the models is carried out by a comparison between the radiation measured and that given by the various models provided by MATLAB code. The different models have been validated by several statistical indicators (RMSE and normalized MAE) and graphs for a clear sky. Scoring criteria have been established to assess the relative quality of each model. The models retained for the study are the Bird and Hulstrom model, the Davy and Hay model, the Capderou model and the Liu and Jordan model. Three sites were affected: Ouagadougou, Dori and Gaoua. We found that for some models, there is a good agreement between the measured values ​​and those estimated by some models for the Dori site, while they are not with the values ​​measured for the Ouagadougou and Gaoua sites. In the town of Dori, the most appropriate model for estimating solar radiation is that of Bird and Hulstrom. For the cities of Ouagadougou and Gaoua, the different models are not suitable.

Novel Assessment and Classification of Monthly Average Daily Global Solar Radiation Models Through a Figure of Merit Called Irradiation Time Equivalence: Analysis of 70 Regression Models Based on Air Temperature and Sunshine Hours Predictors

Abstract Irradiation time equivalence pioneers the classification of models that predict monthly average daily global solar radiation on a horizontal surface based on their double cross-validation performances. By exploiting indigenous irradiation data, novel irradiation-based models can be created and used to classify prediction models, thereby facilitating a deeper understanding of model performance beyond routine summary statistics. The concept was demonstrated by formulating novel 1-hour and 2-hour irradiation-based models to predict monthly average daily global horizontal irradiation. Double cross-validations of the two irradiation-based models and 70 existing regression models were performed using a pair of 5-year subsets. The 70 models used the measured meteorological predictors of air temperature and sunshine hours, either alone or combined. The irradiation time equivalence of a model evaluated under double cross-validation has been defined as the minimum number of hours of measured irradiation needed to predict the monthly average daily irradiation in an average year, with a root mean square error less than or equal to that of the model. Despite their intracompetitiveness, all 44 temperature-based models had an irradiation time equivalence of 1 h, while the remaining 26 models that contained the sunshine-hours predictor were classified with a higher-performance rank of 2 h. An irradiation time equivalence scale extending to 13 h was also developed to cater to the classification of higher-performance models. This fresh perspective on model performance directs future investigations toward determining whether prediction models exhibit global classification constancy.

Modified temperature-based global solar radiation models for estimation in regions with scarce experimental data

Reliable data on the availability of solar energy is needed, as solar energy is an essential resource for sustainable development worldwide. However, ground-based radiometric stations are scarce, at least in large areas far from population and in developing countries, so there are difficulties in validating methods for estimating solar radiation. Indirect models mitigate the problem by providing radiation data from other meteorological variables, which can be measured with low-cost equipment and calibrated with data from secondary station networks. However, models’ accuracy decreases if estimations are required far away from the calibration stations. It is hoped that modified models that include the influence of geographical and topographical variables can attenuate this drawback in data-scarce regions. This paper evaluates the accuracy and generality of 14 existing models of monthly global solar radiation based on temperature, which is a routinely measured variable. At first, models are locally calibrated at 105 stations in three large areas in Spain. Then, from the local coefficients of eight stations selected in each area, general equations are derived for the coefficients of each model as function of the ratio between elevation and distance to the sea. The predictions of these modified models, i.e., using coefficients derived from general equations, are compared both for the eight base stations and the remaining ones used for validation. In the comparisons, not only errors averaged in groups of stations are considered, but also local results. Several models perform well in some areas, but a simple homogeneous model is the only one whose indicators are good in all areas and hardly vary when using general coefficients derived from the data measured at all available stations.

Comparative Analysis of Accuracy, Simplicity and Generality of Temperature-Based Global Solar Radiation Models: Application to the Solar Map of Asturias

The accuracy, complexity and generality of 13 temperature-based solar radiation models are compared using data measured during 2003–2016 at 21 weather stations in a large coastal area of northern Spain. The comparisons are based on dimensionless statistical indicators calculated for each model at each station, as well as on averages of errors calculated both for the group of eight stations located in the vicinity of the Principality of Asturias and for the set of all stations. Using site-calibrated coefficients, most models provide acceptable estimates, and no model outperforms the rest everywhere. The dispersion of the site-calibrated coefficients is analysed as a function of geographical variables, and general equations are obtained for each model, based on data from the group of eight stations. The results for the remaining stations allow the predictive capability of the models to be assessed in regions where radiometric measurements are not available. In general, models with a larger number of parameters perform worse, while a homogeneous single-parameter model achieves better results. Combined with GIS techniques, this model is used to update the Solar Map of Asturias, whose previous version was based on data from different time periods due to the scarcity of records at the time.

Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model

Optimal utilisation of the sun's freely available energy to generate electricity requires efficient predictive models of global solar radiation (GSR). These are necessary to provide solar energy companies an early and effective market entry to support renewable energy integration into electrical grids. We propose a hybrid deep learning CNN-REGST method where a Convolutional Neural Network is integrated with a dual-stage Stacked Regression (Level-O Learner and Level-O predictor) followed by a Support Vector Machine (Level-1 Learner) with its hyperparameters optimised using the HyperOpt function to predict the daily GSR with high accuracy. Six solar energy farms in Queensland, Australia, are selected as testing sites and the predictive features from Global Climate Models and observations, derived using marine predator algorithm, are employed to build the CNN-REGST prediction model. We include a feature selection process based on meta-heuristic methods to select the optimal predictors used as inputs for the resulting CNN-REGST model. Our hybrid model is rigorously evaluated to analyze its performance over a yearlong, and all four season data. We also compare the proposed CNN-REGST model with several deep learning (i.e., CNN, Long-term Short-term Memory Network LSTM, Deep Neural Network DNN) and conventional ML approaches (Extreme Learning Machine ELM, Stacked Regression REGST, Random Forest Regression RFR, Gradient Boosting Machine GBM, Multivariate Adaptive Regression Splines MARS) using the same test datasets. The simulations carried out show that the proposed hybrid model is significantly accurate in GSR predictions compared with the deep learning and the ML models as well as a commonly used persistence model. We conclude that the CNN-REGST prediction model could be a useful scientific ploy incorporated in modern solar energy monitoring technologies to utilize a greater proportion of sustainable energy resources captured from the sun into consumer electricity for conventional-renewable hybrid energy grid systems.

Site Calibration of a Temperature-based Model for Estimating the Global Solar Radiation

In this paper, a commonly used global solar radiation (GSR) model is locally calibrated and tested for Kartepe, Kocaeli station. The coefficients of the model are calibrated for monthly and yearly by performing a regression analysis using the measured temperatures. Regression analysis results for both models provide that correlating the clearness index with the second degree of maximum temperature divided by minimum temperature gives the best accuracy for the selected location. Besides, the estimation results for each month indicate that the monthly calibrated coefficients provide very accurate results in terms of statistical errors. Moreover, yearly calibration of the model gives less accurate predictions. The simple and accurate results by monthly calibrated models using this approach can be used in designing and evaluating solar energy applications in the absence of accurate sunshine data.

Prediction of global solar radiation potential for sustainable and cleaner energy generation using improved Angstrom-Prescott and Gumbel probabilistic models

Predicting and improving global solar radiation models is important in Nigeria because, most government meteorological stations are unable to continuously set-up or measure this radiometric parameter in most metropolitan cities and remote villages where there is a severe need for electricity. This is because most locations are not connected to the national grid due to high cost implications. Global solar radiation (H) prediction has a great deal of benefits in adapting and implementing clean and sustainable energy infrastructure, and much more in detecting and adapting climate mitigation measures in locations affected by climate change externalities. The new Gumbel (GP) probabilistic model applied in this paper is renowned for its high prediction power and low input requirements on the coefficients of the Angstrom-Prescott (AP) model used by researchers to predict global solar radiation since 1940. This study's foremost intention was to investigate ways of generating sustainable and cleaner energy by using coefficients of the AP model in order to accelerate the greener economy in Nigeria. As such, the accuracy and suitability of 36 existing and developed empirical AP models, as well as 11 fitted GP models, were evaluated to estimate H in different climatic regions of Nigeria. The results revealed that AP models fitted with generalized datasets outperformed the 10 location-based models considered in this work and the 7 models selected from literature, however, the GP model outperformed all empirically fitted models. The model established by GP (M13) was used to optimize AP models with poor performance and those selected from literature. This study suggests that it is more realistic to apply the generalized and GP model to predict global solar radiation and generate AP estimation coefficients across Nigeria than using empirical modeling and AP estimation coefficient. This is illustrated by the high prediction accuracy, little or no instrumentation network, and the computational effort for a GP model over an instrumentation network required to generate sunshine hour datasets required to implement AP model predictions. The proposed GP model is sufficient as a valid predictive model that will promote a holistic understanding of the available solar resources in Nigeria and disseminate the application of solar photovoltaic technologies within the country.

Modeling of global solar radiation in Algeria based on geographical and all climatic parameters

The design of photovoltaic or solar systems and estimating their performance require knowledge of the intensity of solar radiation. The measurement of this parameter for some sites in Algeria is unfortunately not obvious. However, researchers are moving towards the modeling, estimation and prediction. To model the global solar radiation in Algeria, we must take into account the geographical and climatic parameters such as sunshine duration, relative humidity, temperature, latitude site, etc... In this study, the modeling of daily global solar radiation on a horizontal plane according to the parameters mentioned above is based on the statistical linear regression technique. The daily data used in the development and validation of models are extracted from the database of O. N. M (National Office of Meteorology, Dar el Beida. Algeria) for 2001-2005. We test the proposed models on two sites such as Djelfa and Ain-Bessam. For each site, validity and performance of the model will be studied based on the number of parameters introduced in the analytical expressions and results are discussed in terms of statistical errors as: R, MBE and RMSE between the measured global solar radiation and global solar radiation estimated. It was found that air temperature and relative humidity are indeed important climatic parameters for the prediction of solar radiation.

Empirical Modelling of Global Solar Radiation in Hatay (Turkey) Province

There are many models used in global solar radiation prediction. However, these prediction models vary according to the climate and geographic characteristics of the region involved. When the prediction model determined for a region is used in a different region, the prediction model may not work. In order to gain absolute and stable results from these models, the studies should be either custom-made or tested. In this study, 7 different models were examined, comparison charts were created and statistical interpretations were made in order to determine the monthly average global solar radiation coming to the horizontal plane taking into account the climatic characteristics of Hatay province, located in the eastern 39.92º latitude and northern longitude 36.16º, covering an area of 5403 km, and has a humid climate with a value of 41.3, based on De Martonne-Gottmann’s drought index and 50.6 based on Erinç's rainfall efficiency index. Under the scope of this study, the most ideal global solar radiation prediction model was selected by making a statistical evaluation according to the parameters of determination coefficient (R²), mean percentage error (MPE), mean deviation error (MBE), root mean square error (RMSE). A general assessment of the graphics and statistical parameters of the predictions obtained by using the models and the measured data has indicated Model 4 as the most successful model by an RMSE value of 0.17306, and the least successful model was Model 3 by an RMSE value of 2.11851. When the models are interpreted according to the determination coefficient, it has been observed that other models are generally successful. However, this situation does not coincide with the comparison charts of the predicted and measured data. For this reason, it was seen that the determination coefficient was not sufficient in the statistical evaluation of the prediction models. In the interpretation of the models, it was observed that the interpretation of root mean square error, mean percentage error and mean deviation error values together gave more accurate results. In conclusion, it has been suggested that Model 4 should be preferred for the global solar radiation forecast of Hatay province.