Models For Global Solar Radiation Research Articles

Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble

Solar radiation data are crucial for the design and evaluation of solar energy systems, climatic studies, water resources management, estimating crop productivity, etc. As so, for locations where direct measurements are not available, reliable models may be developed to estimate solar radiation from more readily available data. In this study, two artificial intelligence (AI)-based models including artificial neural network and adaptive neuro-fuzzy inference systems, three temperature-based empirical models including Meza–Varas, Hargreaves–Samani, and Chen, and a conventional multi-linear regression (MLR) model were employed for multi-region daily global solar radiation estimation for Iraq. To ensure appropriate selection of input variables, sensitivity analysis was conducted to determine the dominant parameters. Finally, two ensemble approaches, neural average ensemble and simple average ensemble, were applied to improve the performance of the single models. For this purpose, daily meteorological data of maximum temperature $$ \left({T_{\hbox{max} } } \right) $$ , minimum temperature $$ \left({T_{\hbox{min} } } \right) $$ , mean temperature $$ \left({T_{\text{mean}} } \right) $$ , relative humidity $$ \left({R_{\text{H}} } \right) $$ , and wind speed $$ \left({U_{2} } \right) $$ were obtained from January 2006 to December 2016 from four major cities in Iraq representing, north, west, south, and east regions. The results revealed that temperatures $$ \left({T_{\hbox{max} } , \;T_{\text{mean}} ,\; T_{\hbox{min} } } \right) $$ and relative humidity are the dominant parameters. While temperature-based empirical models and MLR model could be employed to achieve reliable results, AI-based models are superior in performance to other models. Also promising improvement in daily global solar radiation modeling could be achieved by model ensemble. The results of this study affirmed that the provided ensemble approaches can increase the performance of single models up to 19.19%, 7.59%, and 16.81% in training, validation, and testing steps, respectively.

Performance Evaluation of Developed Empirical Models for Predicting Global Solar Radiation in Western Region of India

For the prediction of solar radiation, various models have been developed in literature with sunshine hours as prime parameter. However, an extensive amount of work is still required correlating the sunshine hours and other parameters. Seven models each for predicting mean global radiation and mean diffuse radiation has been derived in this research work for city Amravati (20.9374 o N, 77.7796 o E), correlating with sunshine hours, average temperature and relative humidity. Relative humidity and temperature together with sunshine can prove to be a best alternative for predicting solar radiation at places where direct measurement is unavailable. Both linear and non-linear correlations developed are compared with conventional models and measured values available for last five years from the meteorological data. The validation is done using several statistical parameters viz. mean absolute percentage error (MAPE), mean bias error (MBE), root mean square error (RMSE) and R 2 . The performance evaluation of these models gives the best suitable model for both global and diffuse solar radiation for the city Amravati, Maharashtra.

Development of seasonal solar radiation estimation models for Dokuz Eylul University campus area for controller of a two-axis solar tracker

In urban locations, it is generally hard to find enough spaces for solar power plants. However, micro-energy generation systems like solar trackers can be located even in crowded cities and becoming a significant technology in solar energy systems. In this study, the measured global solar radiation data for the research area, Tinaztepe/Izmir (38°22'15.3N 27°12'28.5E), were logged, then analysed using statistical methods and seasonal daily global solar radiation mathematical models of Tinaztepe/Izmir were developed. The results of these mathematical models give more precise daily average solar radiation value for Tinaztepe/Izmir than previous Izmir global solar radiation models.

Development of seasonal solar radiation estimation models for Dokuz Eylul University campus area for controller of a two-axis solar tracker

In urban locations, it is generally hard to find enough spaces for solar power plants. However, micro-energy generation systems like solar trackers can be located even in crowded cities and becoming a significant technology in solar energy systems. In this study, the measured global solar radiation data for the research area, Tinaztepe/Izmir (38°22'15.3"N 27°12'28.5"E), were logged, then analysed using statistical methods and seasonal daily global solar radiation mathematical models of Tinaztepe/Izmir were developed. The results of these mathematical models give more precise daily average solar radiation value for Tinaztepe/Izmir than previous Izmir global solar radiation models.

Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

ABSTRACTSolar radiation, moisture and temperature are the most vital meteorological variables which affect plant growth. Due to the fact that the global solar radiation (GSR) is scarcely gauged at meteorological stations in developing countries, it is commonly estimated by data-driven techniques or by empirical equations. In this study, support vector regression (SVR), model trees (MT), gene expression programming (GEP) and adaptive neuro–fuzzy inference system (ANFIS) and several empirical equations were applied to assess the relations between GSR and several meteorological variables including minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (RH), sunshine hours (n), maximum sunshine hours (N), corrected clear-sky solar irradiation (ICSKY), day of year (DOY) and extra-terrestrial radiation (Ra). For this purpose, the daily GSR measured from the beginning of 2011 to the end of 2013 at Tabriz synoptic station, which is located in semi-arid regions of Iran, were used. A direct strong relationship was observed to exist between the GSR and n. For evaluating the performances of studied techniques, three different statistical indicators were used namely root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). Additionally, a Taylor diagram was utilized to test the similarity between the observed and predicted GSR values. Results indicated that the SVR-6 with input parameters of Ra, RH, Tmin, Tmax, n/N had better accuracy in predicting GSR with RMSE of 1.656, MAE of 0.990, CC of 0.980 and WI of 0.990 than the other models. Moreover, MT-6 ranked as the second best model in the prediction of GSR values. As an interesting point, studied empirical equations had lower accuracies comparing with the SVR, GEP, MT and ANFIS methods. For instance, GSR values were computed by Angstrom and Prescott equation, as the best empirical equation, with RMSE of 1.786, MAE of 1.156, CC of 0.977 and WI of 0.988. Conclusively, results from the current study proved that the SVR provided reasonable trends for GSR modeling at Tabriz synoptic station. Furthermore, MT models with linear equations can be implemented with a high degree of simplicity and acceptable precision in GSR estimation.

Cloudiness and Solar Radiation During the Longest Total Solar Eclipse of the 21st Century at Tianhuangping (Zhejiang), China

AbstractThe lack of comprehensive solar radiation monitoring during the longest total solar eclipse of the 21st century at Tianhuangping (Zhejiang), China, on 22 July 2009, has led to this investigation in order to evaluate the cloudiness contribution in estimating the impact on global solar radiation throughout this phenomenon. In doing so, we applied a cloud cover empirical model to obtain the global solar radiation and, at the same time, we deduced a theoretical model to get the direct solar radiation in which both the occultation and obscuration functions of this eclipse are included. We took limb darkening and atmospheric transmission into account. Though the weather during our eclipse observations agreed with the forecasts for that day, clouds and some rain, we were nonetheless able to observe all phases of the eclipse from our observation site at Tianhuangping. This experience suggests that for coming eclipses a record of the in situ observation protocol of cloudiness is mandatory. Our results for comparing global solar radiation models indicate that our total solar eclipse radiation model is quite acceptable and representative of that which could have happened at that time.

A support vector machine approach to estimate global solar radiation with the influence of fog and haze

Abstract In recent years, fog and haze occurred frequently, due to energy crisis and environmental pollution. Fog and haze have significant scattering-weakening effect on solar radiation, resulting in a severe weaken to solar radiation received on a horizontal surface. In this paper, air quality index (AQI) is taken as an additional input parameter, and some new models for estimating global solar radiation on a horizontal surface are proposed based on a support vector machine (SVM). The accuracy of SVM-1 and SVM-2 models are compared and analyzed, and the results show that the performance of SVM-2 models with an extra input parameter AQI are generally improved, for which the R value is promoted from 0.848 to 0.876, the NSE value is lifted from 0.682 to 0.740, the RMSE value is reduced from 0.114 to 0.102, and the MAPE value is decreased from 9.257 to 8.214. Comparing with existing models, SVM models proposed in this paper can improve the accuracy of global solar radiation models. If AQI is used as an additional input parameter to estimate global solar radiation, the accuracy will be further improved.

Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities

Designing predictive models of global solar radiation can be an effective renewable energy feasibility studies approach to resolve future problems associated with the supply, reliability and dynamical stability of consumable energy demands generated by solar-powered electrical plants. In this paper we design and present a new approach to predict the monthly mean daily solar radiation (GSR) by constructing an extreme learning machine (ELM) model integrated with the Moderate Resolution Imaging Spectroradiometer (MODIS)-based satellite and the European Center for Medium Range Weather Forecasting (ECMWF) Reanalysis data for solar rich cities: Brisbane and Townsville, Australia. A self-adaptive differential evolutionary ELM (i.e., SaDE-ELM) is proposed, utilizing a swarm-based ant colony optimization (ACO) feature selection to select the most important predictors for GSR, and the SaDE-ELM is then benchmarked with nine different data-driven models: a basic ELM, genetic programming (GP), online sequential ELM with fixed (OS-ELM) and varying (OSVARY-ELM) input sizes, and hybridized model including the particle swarm optimized-artificial neural network model (PSO-ANN), genetic algorithm optimized ANN (GA-ANN), PSO-support vector machine model (PSO-SVR), genetic algorithm optimized-SVR model (GA-SVR) and the SVR model optimized with grid search (GS-SVR). A comprehensive evaluation of the SaDE-ELM model is performed, considering key statistical metrics and diagnostic plots of measured and forecasted GSR. The results demonstrate excellent forecasting capability of the SaDE-ELM model in respect to the nine benchmark models. SaDE-ELM outperformed all comparative models for both tested study sites with a relative mean absolute and a root mean square error (RRMSE) of 2.6% and 2.3% (for Brisbane) and 0.8% and 0.7% (for Townsville), respectively. Majority of the forecasted errors are recorded in the lowest magnitude frequency band, to demonstrate the preciseness of the SaDE-ELM model. When tested for daily solar radiation forecasting using the ECMWF Reanalysis data for Brisbane study site, the performance resulted in an RRMSE ≈ 10.5%. Alternative models evaluated with the input data classified into El Niño, La Niña and the positive and negative phases of the Indian Ocean Dipole moment (considering the impacts of synoptic-scale climate phenomenon), confirms the superiority of the SaDE-ELM model (with RRMSE ≤ 13%). A seasonal analysis of all developed models depicts SaDE-ELM as the preferred tool over the basic ELM and the hybridized version of ANN, SVR and GP model. In accordance with the results obtained through MODIS satellite and ECMWF Reanalysis data products, this study ascertains that the proposed SaDE-ELM model applied with ACO feature selection, integrated with satellite-derived data is adoptable as a qualified tool for monthly and daily GSR predictions and long-term solar energy feasibility study especially in data sparse and regional sites where a satellite footprint can be identified.

Estimation and comparison of various global solar radiation models on horizontal surface in a hot and dry region

ABSTRACTIn this study, 54 empirical models including 22 new models were used to estimate the global solar radiation on horizontal surface. The data were collected in Dohuk cities in the northern Iraq. The evaluated models are based on sunshine duration, air temperature, cloud cover, and other meteorological parameters. In the investigated region, the statistical error indicators value for all empirical models of R, R2, mean absolute bias error, root mean square error, mean absolute percentage error, Nash–Sutcliffe equation, and t-test value were low and within an acceptable range. The results of Model 49 based on the other meteorological parameters which is newly developed model with equation of had the most appropriate result with a value of R2 (0.9942) in the 54 empirical models. Besides, 22 new equation models are produced based on sunshine hours, air temperature, and other meteorological parameters. The best accuracy among the new equation was observed in Model 49. Furthermore, the Models 34, 12, and 25 based on cloud cover, sunshine hour, and air temperature in terms of data accuracy were ranked, respectively.

A new correlation between global solar radiation and the quality of sunshine duration in China

Accurate and reliable solar radiation models are important in the building energy conservation, solar thermal and photovoltaic utilization, agricultural production and ecological environment, in which the global solar radiation model based on sunshine percentage is the most common model. Sunshine duration only reflects the time accumulation of solar radiation, but cannot reflect its intensity accumulation. Therefore, it is difficult to improve the accuracy of such models. To solve this problem, a new quality of sunshine duration model (QSD model) is proposed, which takes into account both the influence of sunshine duration on solar radiation and the effect of solar radiation intensity on the accumulation of day. The QSD model is simplified from the energy balance equation which is established based on theoretical analysis of the heat transfer process among the Earth's surface layer, air layer and atmospheric layer. Then Meteorological data of 24 stations are utilized to analyze the accuracy of QSD model. The results show that the QSD model is more accurate, and the accuracy of QSD model is best during sunny days, followed by cloudy days, whereas overcast days provide the worst results. Meanwhile, a QSD model is obtained based on data from different regions. Statistical analysis shows that a QSD model has universal applicability in China, and the average value of correlation coefficient (R) is 0.929, Nash-Sutcliffe equation (NSE) is 0.864, and root mean squared error (RMSE) is 0.088.

Prospects of hybrid photovoltaic–diesel standalone system for six different climate locations in Indian state of Tamil Nadu

In developing countries like India, very limited observatory stations are available to collect daily global solar radiation (GSR). This paper focuses to develop a common model for daily GSR estimation and standalone hybrid power system (HPS) design in view of six different climate locations in India. A new strategy is applied for finding the daily GSR model in Madurai location in Tamil Nadu, India. It is based on new parameter, which is named as 'approximate-bright sunshine duration' from the real-time measurement. The remaining six locations are estimated from the new model developed. In addition, to improve the accuracy of estimation, the developed linear one input variable model is further modified as two input variable model, based on the latitudes of six different climatic locations. The performances of developed one-input and two input models are analyzed using statistical indicators. The result shows that the developed two input variable model has provided excellent estimations for Madurai and also other six Indian climatic locations of latitudes ranging from 9°N to 13°N. This work also projects a detailed analysis of HPS design based on three meteorological data inputs to cover the residential energy demand. The NASA data, predicted ANFIS data (estimated data) and the measurement data are used in the analysis for the HPS feasibility and size optimization. The analysis shows that the hybrid configurations are same for all the locations, whereas PV and converter sizes are varied in the range of 47–52 kW and 23–27 kW, respectively.

Comparative and Evaluate of Empirical Models for Estimation Global Solar Radiation in Al-Baha, KSA

Comparative and Evaluate of Empirical Models for Estimation Global Solar Radiation in Al-Baha, KSA

Global Solar Radiation Models: A Review

The Sun is a larger planent which emits light and heat to the Earth for many applications such as solar heating, cooking, drying and interior illumination of buildings. Solar radiation data are required by solar engineers, architects, agriculturists and hydrologists for many applications. In the past, several empirical correlations have been developed in order to estimate the solar radiation around the world. The main objective of this study is to review the global solar radiation models available in the literature. There are several formulae which relate global radiation to other climatic parameters such as sunshine hours, relative humidity and maximum temperature. In this paper, the models are classified into three viz: models based on ratio (H/H_o), non-linear models and models based on empirical coefficients ‘a’ and ‘b’.

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Complete and accurate global solar radiation (Rs) data at a specific region are crucial for regional climate assessment and crop growth modeling. The objective of this paper was to evaluate the capability of 12 solar radiation models based on meteorological data obtained from 21 meteorological stations in China. The results showed that the estimated and measured dailyRshad statistically significant correlations (P<0.01) for all the 12 models in 7 subzones of China. The Bahel model showed the best performance for dailyRsestimation among the sunshine-based models, with averageR2of 0.910, average RMSE of 2.306 MJ m−2 d−1, average RRMSE of 17.3%, average MAE of 1.724 MJ m−2 d−1, and average NS of 0.895, respectively. The Bristow-Campbell (BC) model showed the best performance among the temperature-based models, with averageR2of 0.710, average RMSE of 3.952 MJ m−2 d−1, average RRMSE of 29.5%, average MAE of 2.958 MJ m−2 d−1, and average NS of 0.696, respectively. On monthly scale, Ögelman model showed the best performance among the sunshine-based models, with average RE of 5.66%. The BC model showed the best performance among the temperature-based models, with average RE of 8.26%. Generally, the sunshine-based models were more accurate than the temperature-based models. Overall, the Bahel model is recommended to estimate dailyRs, Ögelman model is recommended to estimate monthly average dailyRsin China when the sunshine duration is available, and the BC model is recommended to estimate both dailyRsand monthly average dailyRswhen only temperature data are available.

Method and strategy for predicting daily global solar radiation using one and two input variables for Indian stations

Estimation of daily global solar radiation (GSR) for a particular location is a key parameter in modeling and designing any solar energy system. Very limited observatory stations are available to collect solar radiation data. This demands solar radiation estimation for new locations where there is no observatory station. In the present study, solar radiation models are developed for estimating daily GSR for sunny locations of India. Solar radiation data are collected for a period of three years from in situ measurements. Angstrom-Prescott linear correlation and nonlinear correlations such as quadratic, cubic, exponential, and power models are developed based on the bright sunshine duration. In this new approach, the “bright sunshine duration” is estimated by a new variable named “approximate bright sunshine duration.” The developed linear one input parameter model is applied to predict the daily GSR for five other Indian locations, namely, Allahabad, Bhopal, Chennai, Hyderabad, and Mumbai. The developed linear one input parameter model was further modified as a latitude dependent model to improve the accuracy of the model and named as the latitude model. The performance of all the models is analysed by using the statistical tools, namely, Relative Root Mean Square Error, Mean Percentage Error, and Mean Absolute Percentage Error. The latitude model resulted in fair to good estimations for Tiruchirappalli and the other five Indian locations of latitudes ranging from 10°N to 26°N. Further, other solar radiation models from the literature were considered to identify their applicability for the same selected locations. The models recommended for the selected Indian locations are presented. Out of all the universal models, the monthly mean daily GSR model of Page and out of all Indian models, the monthly mean daily GSR models of Katiyar and Mani showed satisfactory performance for the majority of selected Indian locations.

Evaluation of some global solar radiation models in selected locations in Northwest, Nigeria

Evaluation of some global solar radiation models in selected locations in Northwest, Nigeria

Comparison of the daily global solar radiation from different data sources in Northwest China climate

ABSTRACTThe solar radiation data are of high importance to the solar energy systems. Conventional methods to obtain the solar radiation data are from weather stations, solar radiation models, commercial software databases, and field measurements. In the present study, a new daily global solar radiation model is proposed, by combining the quadratic function of sunshine fraction and sine function of the day of the year. The solar radiation model calculated data are then compared with China Meteorological Data Sharing System (CMDSS) data, TRNSYS data, and field-measured data in Northwest China climate. It is found that the newly proposed solar radiation model has better performance than the other nine solar radiation models in the literature. The solar radiation model calculated data fit well with the CMDSS annually average data. The TRNSYS data are a bit larger than the CMDSS annually average data in summer half year and a little smaller than those in winter half year. The solar radiation model and the CMDSS annually average data have the best correlation, whereas the TRNSYS data and the field-measured data have the worst correlation. The solar radiation model calculated data have the best correlation with the other three data sources.

Modelling of Global Solar Radiation on Horizontal Surfaces for Mersin City

Turkey is rich in solar energy potential due to its geographical location. Turkey's average annual sunshine duration is 2737 hours and the solar energy potential is 1527 kWh / m2 year. The major solar energy regionsare South Eastern Anatolia Region and the Mediterranean regions in Turkey. In this context, Mersin province is located in the provinces of maximum solar radiation in Turkey. Calculations of all the factors affecting the solar radiation from the atmosphere to the earth; creates a huge workload and the results cannot be obtained with sufficient accuracy.Therefore calculating the solar radiation coming to the earth models have been developed. In this study, the models for the prediction of solar radiation for Mersin city were evaluated using measured solar radiation data.Statistical aspects compliance of studied models were tested and validated which models the closest predicting to measured value. Introduction Energy is essential input of socio-economic prosperity and forward-looking development of today's technology. Therefore, region and country based energy policies and alternative strategies are developing in order to provide adequate, reliable and least damaging to the environment. This matter has a different position and property in Turkey. Turkey is dependent on importation for energy at a rate exceeding 70%. This rate is increasing day by day. Therefore, government and the community have to apply some special policy and specify behavior pattern. Therefore, government and the community have to requires apply some special policies and behavior pattern [1]. While the gap between Turkey's energy production and consumption has been increasing from 1990 to 2004, in the later years of 2010 Turkey's energy production has provided just %28-30 of our consumption. While Turkey’s energy production is 34,47 MTEP in 2012, energy demand is 121 MTEP level in the same year [1].Renewable energy sources aren’t imported therefore it’s national and reliable energy sources.Also these energy sources aren’t run out and not produce exhaust and greenhouse gases emissions unlike conventional energy sources. The potentials of Renewable energy sources of Turkey showed in Table 1. Table 1. Renewable energy potentials of Turkey [2] Renewable Energy Source Specify Natural Potentials Technical Potentials Economical Potentials Solar Energy Electricity(TWh/year) 977000 6105 305 Thermal(MTEP/year) 80000 500 25 Hydro Energy Electricity(TWh/year) 433 216 127.4 Wind Energy Electricity (TWh/year) 400 110 50 Wave energy Electricity (TWh/year) 150 18 Geothermal Energy Thermal(MWt) 31500 750

Identification of clear days from solar irradiance observations using a new method based on the wavelet transform

A new method using the wavelet transform properties is developed to determine clear days of solar irradiance. These days are needed to model the solar radiation and to compare the existing empirical models. We use this method to process four years of global solar irradiation data collected at the Research Unit of Applied Renewable Energies at Ghardaïa city in Algeria. We also determine clear days from this data set using a standard method based on the clearness index criteria. The results show that the two methods give different numbers of clear days. The effect of this difference is analyzed by computing the Global Solar Radiation (GSR) with the Iqbal C model but also by the estimation of turbidity parameters using for that a innovative approach. We find that some significant differences are observed in the GSR modeling leading to bad estimation of turbidity parameters. We conclude that using our method is therefore more efficient since it is not dependent of the site and observations.

Daily global solar radiation modeling for Gran Canaria Island

ABSTRACTThe main aim of this paper is the daily global solar radiation modeling with different estimation models for the island of Gran Canaria (Spain). The errors of those ones will be studied and, moreover, the results will be compared with a solar radiation numerical model previously developed and validated. We have tested five different models starting from the Ångström linear model. Logarithmic, exponential, cubic models, and an exponential-linear composed model have been developed. The best results were obtained with the composed model. The development of these models will allow getting accurate results far from the measurements stations.