Estimation Of Global Solar Radiation Research Articles

Performance evaluation of independent global solar radiation estimation models for different climatic zones: A case study

ABSTRACT Rapid technological advancement in solar applications requires accurately determined daily global solar radiation (DGSR). The independent models to predict DGSR are super cited as they do not require any other climatological or geographical parameter. In this study, nine day-of-the-year based (DYB) independent models are developed to estimate daily global solar radiation in Pakistan. The meteorological data from the year 2000–2017 is used while covering thirty locations from five climatic zones of Pakistan. The models’ performance is statistically assessed in terms of normalized mean bias error (nMBE) and normalized root mean square error (nRMSE). The results indicate that the two Gaussian function model estimates DGSR with a satisfactory performance at most stations in different climatic zones. On average, the correlation coefficient and index of agreement between day-of-the-year and DGSR are 0.934 and 0.956, respectively. The two Gaussian function model is then further compared with four existing best claimed models found in literature. Results show that the nMBE and nRMSE ranges from 0.87 to 3.00 and 0.95 to 3.21, respectively. Thus, it shows that the existing models overestimate the DGSR and are not applicable for long-term planning. In comparison, for the present study, nMBE and nRMSE take values between −0.05 to 0.17 and 0.09 to 0.24, respectively. Furthermore, Global Performance Indicator (GPI) is used to rank the models based upon their prediction performance which ranges between −3.5626 and 4.1998. The study revealed that DYB models are best suited for hot climatic regions of Pakistan as compared to cold climatic zones. Also, the locations within the China Pakistan Economic Corridor (CPEC) highway network have maximum accuracy. This study can help energy system designers to predict DGSR values at any location in Pakistan.

Estimation of daily global solar radiation based on MODIS satellite measurements: The case study of Adrar region (Algeria)

In solar energy studies, solar irradiance data at a given site are a fundamental input for reliably predicting performance behavior and the potential of solar applications. Unfortunately, this parameter is not measured in many parts of the world, especially in developing nations, because of the lack of measuring facilities. In this paper, an alternative method for resource assessment is proposed and evaluated. In the proposed method, a model satellite-derived solar irradiance technique was implemented to estimate daily global solar irradiance using satellite earth observation data, particularly products derived from NASA's MODerate‐resolution Imaging Spectroradiometer (MODIS). The estimation results were validated with the observed data collected in the Renewable Energy Research Unit in the Saharan Region of Adrar (URERMS) over a three-year period. The effects of cloud cover and solar zenith angle on the daily mean global solar irradiance are studied. The accuracy of the model is evaluated with various statistical parameters (RMSE, R, MBE, MAE). The results show that the correlation between the estimated and measured values of global solar irradiance is significant, and the effect of cloud and solar zenith angle on the estimation is confirmed.

An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

A Method to Estimation of Global Solar Radiation with Meteorological Parameters under Cloudless Sky Condition using Artificial Neural Network

In this work, variation of global radiation hourly basis and model developed to estimate the radiation under cloudless sky condition were purposed at Lopburi province (14.83°N, 100.62°E). Data of global radiation and meteorological parameters from 2012 to 2017 were investigated. For the variation of global radiation, the maximum and minimum of radiation are 3.46 MJ/m2 in April and 2.40 MJ/m2 in December, and there are some meteorological parameters influencing on the radiation. Therefore, a model for estimation of hourly global solar radiation under cloudless sky condition at this site was proposed based on the artificial neural network (ANN). This ANN has one input layer, two hidden layers and one output layer. The input layer consists of some meteorological parameters that are solar zenith angle, visibility, air temperature, relative humidity, wind speed and air pressure, and the output layer is global solar radiation under clear sky condition. The ANN was trained using the input and output data collected at Lopburi meteorological station during the year: 2012-2014. It was then validated against an independent data at the site for the period of three years (2015-2017). The validation result indicates that the estimated solar radiation under clear sky condition obtained from ANN are in good agreement with that from the measurement, with root mean square difference (RMSD) of 8.52% and mean bias difference (MBD) of 1.22%. Therefore, the model can be applied for estimation of global radiation under cloudless sky condition at other meteorological stations with similar climate. The estimated solar radiation data are useful for management in solar power plant, solar thermal energy system and also for the studies in the atmospheric field. Keywords: solar radiation, meteorological parameter, artificial neural network

Estimation of Global Solar Radiation and Optimal Tilt Angles of Solar Panels for Pune, India

Solar panels can achieve the highest yield when they are oriented in the right way as the influence of the tilt angles can lead to a high impact on the performance. This paper demonstrates the effect of the tilt angle on the final yield and maximum utilization of solar energy. The annual optimal tilt angle of the solar panels for Pune was found out using the Liu and Jordan model. The optimal tilts yielding an increase of 7% was found out to be 0° on the months starting from April and lasting till September and 40° starting from October and holding at 40° till March. By changing the tilt angle every month, there was an increase in the yield by more than 8%. For better yield and maximum utilization of solar energy, optimizing the tilt angles of solar panels monthly or even twice a year is effective based on the study. Most of the panels are fixed at an angle equal to the latitude of the place and oriented towards the Southern sky in the northern hemisphere. Measurements of the monthly average global solar radiation, monthly average temperatures, and monthly average relative humidity were compared. Further ahead regression correlation for a linear model was carried and the regression coefficients a and b were found out to be 0.2804 and 0.3618 respectively.

Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations

Reliable solar radiation data are essential to study the feasibility and to determine the optimal size of solar plants in a particular location. However, the network of solar radiation measurement stations is very weak around the world. This has led to the development of a wide variety of models and techniques for estimating solar radiation from commonly measured meteorological variables (Ambient air temperature, relative humidity, wind velocity …). In this work, 22 empirical models, Artificial Neural Networks (ANN) techniques, and tree-based ensemble methods were tested in estimating the daily global solar radiation (GSR) in five Moroccan locations. Each database was partitioned into two sets: the training set and the validation set. The training set is used to calibrate the models, while the validation set is used to assess their credibility. The best-performing model at each station was selected based on three statistical indictors, the coefficient of correlation (R), the normalized mean absolute error (nMAE) and the normalized root mean square error (nRMSE). The results on the validation dataset revealed that the Random Forest method (R: 87.53–96.20%; nMAE: 5.84–11.81%; nRMSE: 7.85–15.33%) outperformed the all tested models in terms of accuracy. The other machine learning methods achieved generally good performance (R: 81.73–95.14%; nMAE: 5.88–13.86%; nRMSE: 8.22–18%). Among the empirical models (R: 56.58–93.46%; nMAE: 6.96–21.83%; nRMSE: 9.89–26.96%), the Temperature - Geographic factors model (TG1) (R: 72.38–93.46%; nMAE: 6.96–17.94%; nRMSE: 9.89–22.39%) can be recommended for estimating the GSR for the all considered stations.

Investigating the impact of weather parameters selection on the prediction of solar radiation under different genera of cloud cover: A case-study in a subtropical location

In the present study, a classification system of four meteorological indices were introduced to classify the sky types from sunny to cloudy. These meteorological indices are; cloud cover (Cc), sunshine hour (S), clearness index (Kt) and diffuse fraction (K). Frequency of occurrence and cumulative frequency distribution of each sky indices were established to interpret the prevailing sky conditions in eight cases, each case investigates one day, these days were chosen according to the type of clouds that were overwhelming that day at a subtropical location in Turkey (Izmir). The impact of Cc, on measured global solar radiation (GSR) intensity were also analyzed. In order to predict GSR in each selected case and to making it more and more precise, NARX, FFNN and GRNN weather parameters-based models were evaluated and compared. To achieve accuracy of estimation, ten different input configurations were used to train the models. In addition global solar radiation in a cloudless skies (GSR’) is evaluated as a new input parameter. NARX models had the best performance for estimation of GSR in the first case, with GPI of 0.936 (NARX-C1M1). Results are also indicating that all NARX artificial neural network models provided better estimations than GRNN and FFNN models in case 2 with GPI value of 0.872 (NARX-C2M1). As for case 3, NARX was considered as the best models (NARX-C3M1, GPI = 0.957), followed by NARX (NARX-C3M6, GPI = 0.956) and then FFNN (FF-C3M1, GPI = 0.949). In case 4, NARX models had the highest GPI value (NARX-C4M1, GPI = 0.957) compared with FFNN and GRNN indicated models. Regarding case 5, NARX was considered as the best network (NARX-C5M4, GPI = 0.748), followed by NARX-C5M9 (GPI = 0.549), while FFNN and GRNN model couldn't provide positive results. Likewise, the NARX models did provide better results in case 6 with best GPI value up to 0.64. Furthermore, only NARX and FFNN models provided the better estimation in case 7 (best model, NARX-C7M1 with GPI = 0.826), whereas, GRNN models did not give such acceptable results. Similar to the mentioned above and in comparison with FFNN and GRNN, we have deducated that NARX models offered a better accuracy results compared with the other models (best model, NARX-C8M4 with GIP = 0.364) in case8.

Empirical model for estimation of global solar radiation at lowland region Biratnagar using satellite data

This study proposes to find the regression coefficient of the modified Angstrom type model for the estimation of global solar radiation (GSR) in lowland Biratnagar (Lat. 26.5º N, Long. 87.3º E and Alt. 72m) using relative sunshine duration and satellite data of GSR. Using the regression technique, the empirical constants 0.29 and 0.56 are found in the modified Angstrom model. Furthermore, Modified Angstrom model along with other linear models such as Glover and McCulloch model, Page model, Rietveld model, and Turton's model are statistically assessed to evaluate the significance of models. Statistical tests like MPE, MBE, RMSE, and CC reveal that all these models are statistically significant. These findings can be utilized for other locations with a high confidence level at the similar climatic locations of Nepal.
 BIBECHANA 18 (2021) 193-200

Estimation of Global Solar Radiation in Mubi, Nigeria

Application of solar energy system requires having knowledge about solar irradiation potential in different locations. This study therefore used artificial neural networks for predicting solar global radiation by using metrological data. There is no report about prediction of solar radiation potential for Mubi by using Artificial Neural Network (ANN) method. It is very encouraging to observe a very fine agreement between the measured and estimated values shown in study. The ANN Model is considered the best relation for estimating the global solar radiation intensity for Mubi region with an acceptable error. The MSE, RMSE, MBE, MABE and MAPE values are 0.930, 0.964, 0.3358 MJm−2day−1, 0.8175 MJm−2day−1 and 19.30%, respectively. The ANN models appear auspicious for estimating the Global Solar Radiation in the locations where there are no solar radiation measurement stations.

Global solar radiation prediction model with random forest algorithm

Global solar radiation estimation is crucial for regional climate assessment and crop growth. Therefore, studies on the prediction of solar radiation are emerging. With the availability of the public data on solar radiation, computerized models have been developed as well. These predictive models play significant role in determining the potentials of regions suitable for renewable energy generation required by engineering and agricultural activities. Herein a computerized model has been presented for estimating global solar radiation. The model utilizes random forest algorithm and reached predictive value of 93.9%.

Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r

According to the World Economic Outlook (WEO), the global demand for energy is presum- ably going to be increased due to growing the world’s population up during the upcoming two decades. As a result of that, apprehensions about environmental effects, which appear as a re- sult of greenhouse gases are grown and cleaner energy technologies are developed. This clearly shows that extended growth of the worldwide market share of clean energy. Solar energy is considered as one of the fundamental types of renewable energy. For this reason, the need for a predictive model that effectively observes solar energy conversion with high performance becomes urgent. In this paper, classic empirical, artificial neural network (ANN), deep neural network (DNN), and time series models are applied, and their results are compared to each other to find the most accurate model for daily global solar radiation (DGSR) estimation. In addition, four regression models have been developed and applied for DGSR estimation. The obtained results are evaluated and compared by the root mean square error (RMSE), rela- tive root mean square error (rRMSE), mean absolute error (MAE), mean bias error (MBE), t-statistic, and coefficient of determination (R2). Finally, simulation results provided that the best result is found by the DNN model.

Sunshine-based Global Solar Radiation Modelling: Case Study of Putrajaya, Malaysia

Electricity demands are on the rise and with it, carbon dioxide emissions from many conventional power plants are increasing. In the efforts to mitigate such phenomena, the Malaysian government seeks to implement Building Integrated Photovoltaic (BIPV) projects. Early stage studies on Global Solar Radiation (GSR) have been carried out in several states in Malaysia including Penang, Kuala Lumpur and Kota Bharu. Afterward, data from the Malaysia Meteorological Department and the Malaysia National University have been used to estimate the monthly average daily global radiation in various locations in Malaysia. Putrajaya, a location which is implementing Malaysian Building Integrated Photovoltaic (MBIPV) is among the locations where a GSR study is currently absent. Conventional methods exist for GSR estimation with the aid of pyranometer. However, this method of GSR estimation is time consuming and not cost-effective practice. The main objective of this study is to estimate the GSR in Putrajaya. This is achieved in this study by utilizing sunshine-based data with calculated monthly average daily extraterrestrial radiation on a horizontal surface and monthly average maximum possible daily sunshine to plot a linearly fitted graph. Coefficients in the Angstrom-Prescott (A-P) model was generated from the plotted graph and was used for GSR estimation where a = 0.5 and b = 0.11. The mean percentage error (MPE) of the GSR estimation was found to be 3.4. Therefore, the estimation of GSR in Putrajaya have been successful for the first-time using sunshine-based data from dual locations method. The GSR estimation of Putrajaya in this study could benefit stakeholders in civil development sectors, policy and energy authorities.

Estimation of global solar radiation data based on satellite-derived atmospheric parameters over the urban area of Mashhad, Iran.

Global solar radiation is the total amount of solar energy received on a horizontal surface and defined as the sum of direct, diffused, and reflected solar radiation. Global solar radiation is an important variable in agricultural, meteorological, hydrological, and climatological studies. The purpose of this paper is to develop an effective method to estimate the daily global solar radiation using different atmospheric properties detected from satellite data, including cloud fraction, cloud optical depth, aerosol optical depth, aerosol exponent, aerosol index, and precipitable water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) and ozone monitoring instrument (OMI) daytime data in the urban area of Mashhad, Iran, during the years from 2000 to 2018. Based on seven combinations of the atmospheric properties, models were developed using a standard statistical method, namely, multiple linear regression method and a specific class of artificial neural networks, namely, feedforward multilayer perceptron. The efficiency of the models was compared for the assessment of the daily global solar radiation based on the combinations of the input data. For both methods, 80% percent of the data are used for model development and the remaining data for validation. Results of pairwise statistics indicate that, on average, the estimates were more accurate using the artificial neural networks than the regression method. Results show that in both methods, the accuracy of estimation improves when cloud fraction is used as a predictor. This implies the significant effect of cloud cover on solar radiation. However, using the cloud optical depth decreases the accuracy of the estimation of global solar radiation, i.e., the least accurate model is the one with cloud fraction and cloud optical depth for the neural network method and the model with CF and AE for the regression method. The estimation error comes from the inaccuracy in measuring cloud optical depth that depends on satellite sensor resolution and the inhomogeneity of types and microphysical properties of clouds over the study area. Due to the arid climate of the study area, the precipitable water vapor content does not considerably affect radiation attenuation. The best estimate is earned by cloud fraction and aerosol index as inputs indicating the simultaneous role of aerosol and cloud in global solar radiation. Aerosol index considers the effect of absorbing aerosols such as black carbon and dust and is a complementary information to the cloud cover. The results imply that both methods have the potential to achieve an operational stage, taking advantage of the better availability of satellite data. Even though the artificial neural network is found to be more accurate than multiple linear regression, using the regression method is recommended because it is more easy to use. Results show that the effective variables vary in different seasons. In both methods, estimation error is highest in the spring and lowest in the fall and winter. The high inaccuracy may be due to the high sensitivity of radiative transfer to atmospheric condition in spring. On the other hand, the high accuracy may be caused by the less solar radiation fluctuations during fall and winter because of the lower solar radiation flux.

Determining the radiation use efficiency of potato using sunshine hour data: a simple and costless approach

Aim of study: Radiation parameters and photoperiod influence potato biomass and tuber yield significantly. Lack of instrument facilities in developing countries is the main hindrance to estimate global solar radiation (GSR) and radiation use efficiency (RUE). Considering these facts, an experiment was conducted to estimate light extinction coefficient (K) and RUE using a simple but indirect approach that can be implied in any location lacking sophisticated instruments.Area of study: Field experiments were conducted in Kalyani, West Bengal, representing the Indo-Gangetic Plains.Material and methods: Angstrom-Prescott (A-P) equation was used to calculate GSR. The experiment was laid out in a split-plot design with three dates of planting (DOP), 15th Nov, 29th Nov and 13th Dec, as main plot treatment and three potato cultivars (ˈKufri Suryaˈ, ˈKufri Chandramukhiˈ and ˈKufri Jyotiˈ) as sub-plot treatment. Leaf area indices and K values were used to determine intercepted PAR (IPAR) as well as RUE.Main results: The cumulative IPAR from emergence to harvest ranged 246-429 MJ m-2 depending on planting time and varieties. Irrespective of DOPs, the highest mean RUE (4.19 g MJ-1) was calculated in ˈKufri Chandramukhiˈ, showing that it used the radiation more efficiently that the other two cultivars (ˈKufri Suryaˈ= 3.75 g MJ-1 and ˈKufri Jyotiˈ= 3.14 g MJ-1).Research highlights: Statistical indices confirmed that the A-P model can be reliably used in the study region for estimation of GSR. This simple way to estimating RUE using bright sunshine hours data can be used in developing countries, where costly radiation instruments are not available.

INFLUENCE OF THE ESTIMATED GLOBAL SOLAR RADIATION ON THE REFERENCE EVAPOTRANSPIRATION OBTAINED THROUGH THE PENMAN-MONTEITH FAO 56 METHOD

Agriculture accounts for a significant portion of the state of Minas Gerais' GDP, which is one of the most productive Brazilian states in the agricultural sector. Given this scenario, it is essential to know the elements that affect agricultural productivity. The data manipulated in this work were obtained from the Network of Automatic Meteorological Stations (EMA's) from the National Institute of Meteorology (INMET) located in 51 cities from the State of Minas Gerais. There is a lack of studies to determine the most appropriate model to estimate evapotranspiration with less error in the state of Minas Gerais. Such studies are necessary because the state in question has different climatic and hydrological conditions. The objective of this paper was to evaluate whether the estimation of global solar radiation can influence the daily reference evapotranspiration by the Penman-Monteith FAO 56 method, for the state of Minas Gerais, Brazil. The estimated radiation data were applied to the Penman-Monteith FAO equation for reference evapotranspiration estimation, and these results were compared to the reference evapotranspiration data obtained by the Penman-Monteith FAO equation with radiation data measured at the stations. The performance indexes used were: Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Willmott's d index. The solar radiation values used, when calibrated for the study site, tended to overestimate the evapotranspiration values, presented dispersion of 0.76 mm d-1 on average and the degree of precision of the calibrated models had an average of 0.65 mm d-1. The best solar radiation estimation model for the cities studied was the Thorton and Running model.

Performance and estimation of solar radiation models in state of Minas Gerais, Brazil

Solar radiation is one of the most important meteorological variables, as it is directly related to evaporation. Based on this variable, it is possible to develop models for estimating meteorological elements. However, when there is lack of data, estimates can be made using mathematical models. The objective of this paper was to perform the calibration and statistical performance evaluation of fifteen simplified models of global solar radiation estimation based on air temperature for 51 cities in the state of Minas Gerais, Brazil. The data were provided by the National Meteorological Institute using Automatic Meteorological Stations (EMA’s) located in the cities studied. The performance indexes used were the Coefficient of Determination for Linear Regression (R2), Root-Mean-Square Error and Mean Relative Error, and the Willmott d Index. Through the results obtained, it is possible to observe that the model with the best performance for the state of Minas Gerais was that of Donatelli and Campbell, because, based on the statistical analysis and ordering of the indexes, that is, the use of the position values (Vp) of the indicatives statistics to classify and define the best method for estimating global radiation, this model was the one that obtained the lowest Vp value.

Application of nonlinear autoregressive neural network to estimation of global solar radiation over Nigeria

In this paper, surface data meteorological were used as input variables to create, train and validate the network in which global solar radiation serves as a target. These surface data were obtained from the archives of the European centre for Medium-Range weather forecast for a span of 36 years (1980-2015) over Nigeria. The research aims to evaluate the predictive ability of the nonlinear autoregressive neural network with exogenous input (NARX) model compared with the multivariate linear regression (MLR) model using the statistical metrics. Model selection analysis using the index of agreement (dr) metric showed that the MLR and NARX models have values of 0.710 and 0.853 in the Sahel, 0.748 and 0.849 in the Guinea Savannah, 0.664 and 0.791 in the Derived Savannah, 0.634 and 0.824 in the Coastal regions, and 0.771 and 0.806 in entire Nigeria respectively. Meanwhile, error analyses of the models using root mean square errors (RMSE) showed the values of 1.720 W/m2 and 1.417 in the Sahel region, 2.329 W/m2 and 1.985 W/m2 in the Guinea Savannah region, 2.459 W/m2 and 2.272 W/m2 in the Derived Savannah region, 2.397 W/m2 and 2.261 W/m2 in the Coastal region and 1.691 W/m2 and 1.600 W/m2 in entire Nigeria for MLR and NARX models respectively. These showed that the NARX model has higher dr values and lower RMSE values over all the climatic regions and entire Nigeria than the MLR model. Finally, it can be inferred from these metrics that the NARX model gives a better prediction of global solar radiation than the traditional common MLR models in all the zones in Nigeria.

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.

Performance improvement of empirical models for estimation of global solar radiation in India: A k-fold cross-validation approach

In this work, global solar radiation is estimated based on sunshine duration. Solar radiation measurements have been collected from the Indian Meteorological Department (Pune, India) for the period 1986–2000. 25 model forms were selected from the literature which correlates the clearness index with sunshine duration. The coefficients of models are extracted from the data. k-fold cross-validation is then employed to improve the performance of the models. Data is split into k-groups with each group containing the same amount of data. (k-1) groups are utilized for the development of models and the rest one group is utilized for the testing of models performance. The procedure is repeated k-times and those coefficients are selected which produces the least error. Models are evaluated and compared with the help of statistical errors. Further, the statistical errors were scaled and Global Performance Indicator (GPI) was evaluated. Using GPI, models were given rank in order of suitability of estimates produced. The GPI value lies between −9.4269 and 0.4695. It has been observed that the quartic model (M-04) exhibited the best performance with MBE = 0.0259, RMSE = 1.7927, MPE = 0.1495, RRMSE = 0.0352, erMAX = 0.9072, MARE = 0.0812, MAE = 1.3843, U95 = 6.3100, t-stats = 0.2397 and R = 0.8597 resulting in the highest GPI amongst all the models which were proposed.

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.