Estimation Of Global Solar Radiation Research Articles

Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate

Accurate estimation of solar radiation is the major concern in renewable energy applications. Over the past few years, a lot of machine learning paradigms have been proposed in order to improve the estimation performances, mostly based on artificial neural networks, fuzzy logic, support vector machine and adaptive neuro-fuzzy inference system. The aim of this work is the prediction of the daily global solar radiation, received on a horizontal surface through the Gaussian process regression (GPR) methodology. A case study of Ghardaia region (Algeria) has been used in order to validate the above methodology. In fact, several combinations have been tested; it was found that, GPR-model based on sunshine duration, minimum air temperature and relative humidity gives the best results in term of mean absolute bias error (MBE), root mean square error (RMSE), relative mean square error (rRMSE), and correlation coefficient (r) . The obtained values of these indicators are 0.67 MJ/m2, 1.15 MJ/m2, 5.2%, and 98.42%, respectively.

Comparison of artificial intelligence methods in estimation of daily global solar radiation

Abstract Assessment of solar potential over a location of interest is introduced as an important step for the successful planning of solar energy systems (photovoltaic or thermal). Due to the absence of meteorological stations and sophisticated solar sensors, solar data may be unavailable for every point of interest. Hence, empirical and intelligence methods are developed to estimate solar irradiance data. In this study, the idea of artificial intelligence methods is employed to predict the daily global solar radiation. The developed models are: group method of data handling (GMDH) type neural network, multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized with particle swarm optimization algorithm (ANFIS-PSO), ANFIS optimized with genetic algorithm (ANFIS-GA) and ANFIS optimized with ant colony (ANFIS-ACO). The data are collected from 12 stations in different climate zones of Iran. The input variables of the models are including month, day, average air temperature, maximum air temperature, minimum air temperature, air pressure, relative humidity, wind speed, top of atmosphere insolation, latitude and longitude. The results demonstrated that although the developed models can successfully predict the global horizontal irradiance, the GMDH model outperforms the other developed models. The values of root mean square error (RMSE), determination coefficient (R2) and mean square error (MSE) for the GMDH model were 0.2466 (kWh/m2/day), 0.9886 and 0.0608 (kWh/m2/day), respectively.

Economic feasibility of hybrid energy generation with reduced carbon emission

Increasing concern about the shortage of energy resources and harmful outcome of fossil fuel emission has initiated new requirement of reliable and cleaner green power sources. Hence, solar photovoltaic and wind power system are fastest developing sources among different renewable energy sources. In the proposed work, the urgency of the national policy to upgrade the existing coal-based plant as integrated solar, wind and coal-based power plant to reduce the carbon emission and to evaluate the feasibility of developing grid-connected hybrid energy system in Ramapuram Chennai, India has been presented. Moreover, the regression models for estimation of global solar radiation using different metrological parameters are developed and compared with the results of other models. In this work, Ramapuram, Chennai, India area is chosen to install the wind and solar photovoltaic systems to feed three types of load (residential, commercial and industrial). In this study, ENNORE thermal power station, Chennai is considered to reduce the carbon emission as the integration of solar photovoltaic system and wind system to the grid reduce the units generation from this plant. Hence, study shows that 110329.56 kg emission is reduced from ENNORE thermal power station by using this system.

Artificial neural network and neuro fuzzy inference modelling of global solar radiation data using bayesian algorithm for design of solar energy conversion system

Measurement of global solar radiation is particularly required for proper design of solar energy conversion systems. This study investigates the use of software tools like neural networks and fuzzy inference systems for modelling so as to predict global solar radiation using different input parameters based on available weather data. Advantages include simplicity, speed and efficiency, to make short term predictions of global solar radiation at different locations in India, Germany and United Kingdom. It helps in estimation of effectiveness of the applied model which matches solar radiation and other meteorological parameters which are in a non-linear relationship. Bayesian Inference algorithm is used for the current study in estimation of global solar radiation.

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.

New method for estimating daily global solar radiation over sloped topography in China

A new scheme for the estimation of daily global solar radiation over sloped topography in China is developed based on the Iqbal model C and MODIS cloud fraction. The effects of topography are determined using a digital elevation model. The scheme is tested using observations of solar radiation at 98 stations in China, and the results show that the mean absolute bias error is 1.51 MJ m−2 d−1 and the mean relative absolute bias error is 10.57%. Based on calculations using this scheme, the distribution of daily global solar radiation over slopes in China on four days in the middle of each season (15 January, 15 April, 15 July and 15 October 2003) at a spatial resolution of 1 km × 1 km are analyzed. To investigate the effects of topography on global solar radiation, the results determined in four mountains areas (Tianshan, Kunlun Mountains, Qinling, and Nanling) are discussed, and the typical characteristics of solar radiation over sloped surfaces revealed. In general, the new scheme can produce reasonable characteristics of solar radiation distribution at a high spatial resolution in mountain areas, which will be useful in analyses of mountain climate and planning for agricultural production.

Comparative study of the monthly global solar radiation estimation data in Jakarta

The objective of this paper is to get the most suitable model of monthly global solar radiation estimation in Jakarta which performs better than the others model. In this study, measured data of solar radiation were utilized to be compared with the empirical models from the literature and a new based-temperature model, to estimate the monthly intensities of global solar radiation in Jakarta, Indonesia (Latittude: 6.13° S, Longitude: 106.8° E). Global solar radiations were measured for two complete years, and the estimation of monthly solar radiation were calulated by four empirical models and also one linear regression model. The accuracy of the models was then analyzed in terms of some statistical indicators such as Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The statistical analysis of result revealed that the empirical models have not the same accuracy, and it is concluded that there are the models that can be preferred for the estimation of monthly global solar radiation in Jakarta, Indonesia. The obtained results indicate that Allen is performing better than the others by obtaining maximum MBE of 0.52 and maximum RMSE of 1.82. It also can be said that Allen is the most suitable model for estimating a monthly global solar radiation in Jakarta, Indonesia.

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.

Support vector regression methodology for estimating global solar radiation in Algeria

Accurate estimation of Daily Global Solar Radiation (DGSR) has been a major goal for solar energy applications. In this paper we show the possibility of developing a simple model based on the Support Vector Regression (SVM-R), which could be used to estimate DGSR on the horizontal surface in Algeria based only on sunshine ratio as input. The SVM model has been developed and tested using a data set recorded over three years (2005-2007). The data was collected at the Applied Research Unit for Renewable Energies (URAER) in Ghardaia city. The data collected between 2005-2006 are used to train the model while the 2007 data are used to test the performance of the selected model. The measured and the estimated values of DGSR were compared during the testing phase statistically using the Root Mean Square Error (RMSE), Relative Square Error (rRMSE), and correlation coefficient (\(r^{2}\)), which amount to 1.59(MJ/m2), 8.46 and 97,4%, respectively. The obtained results show that the SVM-R is highly qualified for DGSR estimation using only sunshine ratio.

Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions

In the present study, 14 existing temperature–based empirical models for daily horizontal global solar radiation estimation were comprehensively reviewed, and six new temperature–based models (P1P6) were developed for solar radiation estimation in humid regions. The accuracy and suitability of these models were further evaluated for the humid subtropical and tropical regions of China as a case study using meteorological data during 1966–2015 from 20 radiation stations. The results indicated that despite most studied models were capable of estimating daily global solar radiation with reasonable accuracy, the accuracy of the single temperature-based models was much improved when daily precipitation and relative humidity were included in the models. The newly proposed polynomial model P2 was the top-ranked single temperature-based model for most radiation stations and thus recommended at sites where only air temperatures were available. In terms of complex temperature-based models, the proposed models P3 and P5 by incorporating a proposed logarithmic form of precipitation and the actual value of relative humidity were superior to the existing complex models. Generally, the proposed temperature–based models can be applied for daily global solar radiation estimation with higher accuracy in humid subtropical and tropical regions of China and maybe elsewhere with similar climates.

Models for forecasting monthly mean daily global solar radiation from in-situ measurements: Application in Tropical Climate, India

Radiation availability varies with reference to the geometrical location. It is necessary to develop the models to predict the daily global solar radiation (GSR) for the locations where there is a huge potential of radiation exists and to improve the utilization of energy. This study aimed to develop the solar radiation models with readily available input parameters for Tiruchirappalli, which covers an annual mean daily global solar radiation of 4.50kWh/m2-day.Exponential model (EM), modified exponential model (MEM), power series model (PSM), Hargreaves and Samani model (HSM), Hargreaves-model (HGM) and Hunt models (HUM) which uses air temperature and extra-terrestrial radiation on a horizontal surface as input parameters were developed and validated. Dumas model which uses air temperature and maximum possible sunshine duration (So) as input parameters was also verified. Dumas model was modified as hybrid models to check for their applicability. The performance of the models was analyzed by using the statistical tools. Out of all the models, HSM and PSM had shown an accurate estimation of daily GSR (errors: 6.62% & 7.95% respectively) for the selected location. Dumas model's performance was also found satisfactory (error: 11.02%).

New combined models for estimating daily global solar radiation based on sunshine duration in humid regions: A case study in South China

In the present study, two single (T1 and T2) and eight combined (C1–C8) sunshine-based empirical models for estimating daily horizontal global solar radiation were compared, and four new combined sunshine-based models (P1–P4) were developed. The accuracy and applicability of these models were further evaluated in South China with humid subtropical and tropical climates influenced by the Asian monsoon as a case study. For this purpose, daily meteorological data during 1966–2000 from 20 radiation stations were used for model calibration and the data from 2001 to 2015 were used to validate the models. These models were evaluated and compared based on coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE) and a global performance index (GPI). The single sunshine-based model T2 was recommended for Rs estimation with acceptable accuracy when using only sunshine duration data. In terms of combined sunshine-based models, the newly proposed model P3 was the optimum model for daily global solar radiation estimation when sunshine duration, air temperature and precipitation were available. The proposed complex model P4 was recommended for estimating daily global solar radiation with an excellent accuracy when vapor pressure deficit and relatively humidity were further available. Generally, the proposed combined models based on sunshine duration can be applied for daily global solar radiation estimation with high accuracy in humid regions of China and maybe elsewhere with similar climate.

Classification of solar radiation zones and general models for estimating the daily global solar radiation on horizontal surfaces in China

Classification of solar radiation zones constitutes the prerequisite for the establishment of regional daily global solar radiation (H) estimation general model. Current zone establishment methods are ordinary based on solar radiation observation stations (SROS) which present a sparse and non-uniform distribution. As a result, the possibility of misclassifications of the stations occurs in cases there are no records of radiation and far away from SROS. Therefore, by using k-means cluster and Support Vector Machine-Genetic Algorithm, a two-step radiation zoning method was proposed in this paper according to: (a) H, sunshine duration, temperature and relative humidity from 98 SROS and (b) sunshine duration, temperature and relative humidity from 562 stations without radiation. The method is capable to combine the SROS and the stations without radiation in the process of classification. Thus, these misclassifications have been effectively reduced and the accuracy of each classification has been significantly improved. Based on the method, five radiation zones have been identified. Concurrently, four sunshine-based models were obtained for each SROS and the analysis of statistical indexes indicated that the cubic models presented the best performances in each zone. According to the best site-specific models and radiation zones, the general models of regional H estimation were developed by introducing the geographical parameters, including latitude and altitude. The comparative results demonstrated that the general models proposed in this paper had better accuracies and can represent the general models for the H estimation of stations without radiation records in China.

An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia

This research paper aims to develop a hybrid neuro-evolutionary wrapper-based model for daily global solar radiation estimation in the solar-rich Sunshine State of Queensland, Australia. To design a robust hybrid modelling mechanism, the Interim-ERA European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis data are employed to train and cross-validate the estimation model that is formulated by an evolutionary-type algorithm: the Coral Reefs Optimization (CRO) integrated with an Extreme Learning Machine (ELM) model. The hybrid CRO-(ELM) algorithm is applied in two stages: first for the feature selection process guided by an ELM algorithm (a class of fast training neural network tool) as the model’s fitness function to screen an optimal set of predictor variables and second, for the estimation of the solar radiation using the optimally screened variables by the final hybrid CRO-(ELM)-ELM system. To benchmark the performance of the hybrid CRO-ELM algorithm for this estimation problem we apply an alternative, yet a similar feature screening approach: the Grouping Genetic Algorithm encoded into the ELM-based model (GGA-(ELM) used as the predictor mechanism). After the feature selection process is performed by the CRO algorithm that extracts the data patterns for accurate estimation the alternative objective algorithm is applied (in this case the ELM again) to formulate the hybrid CRO-(ELM)-ELM modelling system. To provide a rigorous evaluation of the CRO-(ELM)-ELM hybrid system, alternative estimation approaches are considered: the Multivariate Adaptive Regression Splines (MARS), Multiple Linear Regression (MLR) and the Support Vector Regression (SVR). The hybrid CRO-(ELM)-ELM system is tested in a real problem where the results are evaluated by means of several statistical score metrics and diagnostic plots of the observed and the estimated daily global solar radiation in the testing phase. We show that the hybrid CRO-(ELM)-ELM model is able to yield promising results; thus improving those attained by the 7 alternative models (i.e., hybrid CRO-(ELM)-MARS, CRO-(ELM)-MLR and CRO-(ELM)-SVR and the GGA equivalent models). The study ascertains that the CRO-based hybrid system where a large pool of predictor data are carefully screened through a wrapper-based modelling system and the ELM model is applied as a objective estimation tool can be adopted as a qualified stratagem in solar radiation estimation problems. The proposed hybrid CRO-(ELM)-ELM system exhibits clear advantages over the alternative machine learning approaches tested and also over the other machine learning algorithms used without the feature selection tool; thus advocating its scientific utility in renewable energy applications.

A Modal Study on the Estimation of Daily Global Solar Radiation from Sunshine Duration

A Modal Study on the Estimation of Daily Global Solar Radiation from Sunshine Duration

Estimation of Monthly Average Hourly Global Solar Radiation from the Daily Value in Çanakkale, Turkey

Estimation of Monthly Average Hourly Global Solar Radiation from the Daily Value in Çanakkale, Turkey

An application of the Multilayer Perceptron: Estimation of Global Solar Radiation and the Establishment of Solar Radiation Maps of Togo

This paper presents a new neural network approach for the generation of synthetic monthly radiation data for nine localities in Togo. The neural model employed is the well-known Multi-Layer Perceptron (MLP) paradigm, in feedback architecture, using a record of historical values for the supervised network training. The method is based on the MLP ability to extract, from a sufficiently general training set, the existing relationships between variables whose interdependence is unknown a priori. Simulation results are compared to the measured values for the three towns where solar irradiation is measured in Togo. The results show that the generated values are of the real values. The method has been developed using data values from Lomé, Atakpamé and Mango, and is generalized to generate data of any location for the establishment of solar maps. Indeed, the proposed methodology is of general applicability to the estimation of highly complex temporal series.

The global solar radiation estimation and analysis of solar energy: Case study for Osmaniye, Turkey

ABSTRACTThis paper investigates the prediction of solar radiation model and actual solar energy in Osmaniye, Turkey. Four models were used to estimate using the parameters of sunshine duration and average temperature. In order to obtain the statistical performance analysis of models, the coefficient of determination (R2), mean absolute percentage error (MAPE), mean absolute bias error (MABE), and root mean square error (RMSE) were used. Results obtained from the linear regression using the parameters of sunshine duration and average temperature showed a good prediction of the monthly average daily global solar radiation on a horizontal surface. In order to obtain solar energy, daily and monthly average solar radiation values were calculated from the five minute average recorded values by using meteorological measuring device. As a result of this measurement, the highest monthly and yearly mean solar radiation values were 698 (April in 2013) and 549 (2014 year) W/m2 respectively. On an annual scale the maximum global solar radiation changes from 26.38 MJ/m2/day by June to 19.19 MJ/m2/day by September in 2013. Minimum global solar radiation changes from 14.05 MJ/m2/day by October to 7.20 MJ/m2/day by January in 2013. Yearly average energy potential during the measurement period was 16.53 MJ/m2/day (in 2013). The results show that Osmaniye has a considerable solar energy potential to produce electricity.

Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation

The estimation of daily solar radiation is needed in many studies related to solar power plant placements. To optimize photovoltaic (PV) systems, their placement must be as efficient as possible in terms of the prevailing meteorological conditions. There are situations where radiation data are not available, as in the case of desert areas, suitable for the operation of PV systems. In this work, daily global solar radiation has been estimated in desert areas using Artificial Neural Networks (ANN), where the inputs used are daily minimum and maximum temperatures and extraterrestrial radiation. The ANN model is validated with data from deserts in Chile, Israel, Saudi Arabia, South Africa and Australia. The results show that the average Relative Root-Mean-Square Deviation (RRMSD) value is 13%, the average Relative Mean Bias Difference (RMBE) value is less than 4% and the average correlation coefficient (r) value is about 0.8.

A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics.