Effect of solar radiation correlations on system sizing: PV pumping case

The precise determination of solar radiation intensity is important for accurate system sizing. For PV pumping system case, the issue becomes more important since inaccurate solar radiation data nonlinearly reflected to the calculations. Inappropriate solar radiation model selection for a specific design site is a major cause for inaccurate system sizing. The differences in power output of a PV pumping system due to using some solar radiation correlations are examined here. The results are then compared with those of long-term solar radiation measurements. It is reported that the system sizing with the measurements and the most appropriate correlation for the application site significantly differ since the errors in predicting solar radiation data are nonlinearly propagated to the system power output.     

An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria

In this paper we investigate, the possibility of using an adaptive Artificial Neural Network (ANN), in order to find a suitable model for sizing Stand-Alone Photovoltaic (SAPV) systems, based on a minimum of input data. The model combines Radial Basis Function (RBF) network and Infinite Impulse Response (IIR) filter in order to accelerate the convergence of the network. For the sizing of a photovoltaic (PV) systems, we need to determine the optimal sizing coefficients (K_PV, K_B). These coefficients allow us to determine the number of solar panels and storage batteries necessary to satisfy a given consumption, especially in isolated sites where the global solar radiation data is not always available. These coefficients are considered the most important parameters for sizing a PV system. Results obtained by classical models (analytical, numerical, analytical-numerical, B-spline function) and new models like feed-forward (MLP), radial basis function (RBF), MLP-IIR and RBF-IIR are compared with experimental sizing coefficients in order to illustrate the accuracy of the new developed model. This model has been trained by using 200 known optimal sizing coefficients corresponding to 200 locations in Algeria. In this way, the adaptive model was trained to accept and handle a number of unusual cases. The unknown validation sizing coefficients set produced very accurate estimation with a correlation coefficient of 98%. This result indicates that the proposed method can be successfully used for the estimation of optimal sizing coefficients of SAPV systems for any locations in Algeria. The methodology proposed in this paper however, can be generalized using different locations of the world.     

Evaluation of decomposition models of various complexity to estimate the direct solar irradiance over Belgium

Solar energy production is directly correlated to the amount of radiation received at a given location. Appropriate information on solar resources is therefore very important for designing and sizing solar energy systems. Concentrated solar power projects and photovoltaic tracking systems rely predominantly on direct normal irradiance (DNI). However, the availability of DNI measurements from surface observation stations has proven to be spatially too sparse to quantify solar resources at most potential sites. Satellite data can be used to calculate estimates of direct solar radiation where ground measurements do not exist. Performance of decomposition models of various complexity have been evaluated against one year of in situ observations recorded on the roof of the radiometric tower of the Royal Meteorological Institute of Belgium in Uccle, Brussels. Models were first evaluated on a hourly and sub-hourly basis using measurements of global horizontal irradiance (GHI) as input. Second, the best performing ground-based decomposition models were used to extract the direct component of the global radiation retrieved from Meteosat Second Generation (MSG) images. Results were then compared to direct beam estimations provided by satellite-based diffuse fraction models and evaluated against direct solar radiation data measured at Uccle. Our analysis indicates that valuable DNI estimation can be derived from MSG images over Belgium regardless of the satellite retrieved GHI accuracy. Moreover, the DNI retrieval from MSG data can be implemented on an operational basis.     

A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach

The purpose of this work is to develop a hybrid model which will be used to predict the daily global solar radiation data by combining between an artificial neural network (ANN) and a library of Markov transition matrices (MTM) approach. Developed model can generate a sequence of global solar radiation data using a minimum of input data (latitude, longitude and altitude), especially in isolated sites. A data base of daily global solar radiation data has been collected from 60 meteorological stations in Algeria during 1991–2000. Also a typical meteorological year (TMY) has been built from this database. Firstly, a neural network block has been trained based on 60 known monthly solar radiation data from the TMY. In this way, the network was trained to accept and even handle a number of unusual cases. The neural network can generate the monthly solar radiation data. Secondly, these data have been divided by corresponding extraterrestrial value in order to obtain the monthly clearness index values. Based on these monthly clearness indexes and using a library of MTM block we can generate the sequences of daily clearness indexes. Known data were subsequently used to investigate the accuracy of the prediction. Furthermore, the unknown validation data set produced very accurate prediction; with an RMSE error not exceeding 8% between the measured and predicted data. A correlation coefficient ranging from 90% and 92% have been obtained; also this model has been compared to the traditional models AR, ARMA, Markov chain, MTM and measured data. Results obtained indicate that the proposed model can successfully be used for the estimation of the daily solar radiation data for any locations in Algeria by using as input the altitude, the longitude, and the latitude. Also, the model can be generalized for any location in the world. An application of sizing PV systems in isolated sites has been applied in order to confirm the validity of this model.     

Integrated solar heating systems: From initial sizing procedure to dynamic simulation

The sizing of the solar installation of an individual dwelling is a problem which can be solved in many ways. The approach described in this paper is a simplified procedure of considerable interest. It requires only a small quantity of data and can be computed in a short time. The performance of this procedure was evaluated by a more complex sizing method based on detailed simulation. The simplified procedure was applied to the case of an individual dwelling using a solar collector field to produce domestic hot water and space heating. The building and the solar installation have then been modelled with the software TRNSYS 16 and their behaviour was simulated during a whole year. The results obtained are particularly close to the ones expected by the simplified sizing procedure.     

Combination of Hargreaves method and linear regression as a new method to estimate solar radiation in Perlis, Northern Malaysia

The best way to obtain the solar radiation data of a particular place of interest (POI) is to measure at the specific site continuously and accurately over the long term. However, due to financial, maintenance, calibration requirement of the measuring equipment or institutional limitations, these data are absent, incomplete or inaccessible in most areas of the world. Based on meteorological data from Chuping Station, Perlis which is at Northern Malaysia, there were several missing data of solar radiation for the year 2007 and 2008. This paper presents a new method to estimate the solar radiation which is a combination of Hargreaves method and linear regression. Normally, both regression coefficients, a and b of the linear regression are found based on the measured data, but using the proposed method, both regression coefficients based on the Hargreaves method with the correlated parameter, x is the difference of daily temperature. This paper also presents the basic knowledge of Hargreaves method before the proposed method is implemented. As validation, those solar radiation data that are measured by Chuping Station for the year 2006 and by Electrical Energy and Industrial Electronic System (EEIES) Cluster Station for the month of March–June 2011 and their estimated solar radiation data are compared and analyzed using coefficient of residual mass (CRM), root mean squared error (RMSE), Nash–Sutcliffe equation (NSE) and percentage error (e). The statistical analysis of the average monthly measured solar radiation data for the past 26 years (1979–2006) is compared with the estimated solar radiation data for 3 years (2006–2008). The proposed method result shows that the value of CRM is closer to zero which indicates that the proposed method is perfectly estimated, the values of RMSE are low value, this indicates that the method performs well, the value of NSE is closer to 1 which indicates that the estimated solar radiation match perfectly with the measured data taken for the past 26 years, the value of e is closer to zero which indicates that the proposed method is acceptable and applicable.     

A stochastic method for stand-alone photovoltaic system sizing

Photovoltaic systems utilize solar energy to generate electrical energy to meet load demands. Optimal sizing of these systems includes the characterization of solar radiation. Solar radiation at the Earth’s surface has random characteristics and has been the focus of various academic studies. The objective of this study was to stochastically analyze parameters involved in the sizing of photovoltaic generators and develop a methodology for sizing of stand-alone photovoltaic systems. Energy storage for isolated systems and solar radiation were analyzed stochastically due to their random behavior. For the development of the methodology proposed stochastic analysis were studied including the Markov chain and beta probability density function. The obtained results were compared with those for sizing of stand-alone using from the Sandia method (deterministic), in which the stochastic model presented more reliable values. Both models present advantages and disadvantages, however, the stochastic one is more complex and provides more reliable and realistic results.     

Global,direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah,Saudi Arabia

The measured data of global and diffuse solar radiation on a horizontal surface, the number of bright sunshine hours, mean daily ambient temperature, maximum and minimum ambient temperatures, relative humidity and amount of cloud cover for Jeddah (lat. 21°42′37′′N, long. 39°11′12′′E), Saudi Arabia, during the period (1996–2007) are analyzed. The monthly averages of daily values for these meteorological variables have been calculated. The data are then divided into two sets. The sub-data set I (1996–2004) are employed to develop empirical correlations between the monthly average of daily global solar radiation fraction (H/H₀) and the various weather parameters. The sub-data set II (2005–2007) are then used to evaluate the derived correlations. Furthermore, the total solar radiation on horizontal surfaces is separated into the beam and diffuses components. Empirical correlations for estimating the diffuse solar radiation incident on horizontal surfaces have been proposed. The total solar radiation incident on a tilted surface facing south H_t with different tilt angles is then calculated using both Liu and Jordan isotropic model and Klucher’s anisotropic model. It is inferred that the isotropic model is able to estimate H_t more accurate than the anisotropic one. At the optimum tilt angle, the maximum value of H_t is obtained as ∼36 (MJ/m² day) during January. Comparisons with 22 years average data of NASA SSE Model showed that the proposed correlations are able to predict the total annual energy on horizontal and tilted surfaces in Jeddah with a reasonable accuracy. It is also found that at Jeddah, the solar energy devices have to be tilted to face south with a tilt angle equals the latitude of the place in order to achieve the best performance all year round.     

A simple model for sizing stand alone photovoltaic systems

We consider a general model for sizing a stand-alone photovoltaic system, using as energy input data the information available in any radiation atlas. The parameters of the model are estimated by multivariate linear regression. The results obtained from a numerical sizing method were used as initial input data to fit the model. The expression proposed allows us to determine the photovoltaic array size, with a coefficient of determination ranging from 0.94 to 0.98. System parameters and mean monthly values for daily global radiation on the solar modules surface are taken as independent variables in the model. It is also shown that the proposed model can be used with the same accuracy for other locations not considered in the estimation of the model.     

Stochastic approach for daily solar radiation modeling

Mathematical modeling of solar radiation continues to be an important issue in renewable energy applications. In general, existing models are mostly empirical and data dependent. In this paper, a novel approach for solar radiation modeling is proposed and illustrated. The proposed application consists of hidden Markov processes, which are widely used in various signal processing topics including speech modeling with successful results. In the experimental work, mean of hourly measured ambient temperature values are considered as observations of the model, whereas mean of hourly solar radiation values are considered as the hidden events, which constitute the outcomes of the proposed mathematical model. Both solar radiations and temperatures are converted to quantized number of states. Finally, after a training stage that forms the transition probability values of the described states, the hidden Markov model parameters are obtained and tested. The tests are repeated for various numbers of states and observations are presented. Plausible modeling results with distinct properties in terms of accuracy are achieved.     

Experimental and numerical model of wall like solar heat discharge passive system

The present work raises the use of solar energy as an aid for air conditioning by means of architectural envelope parts such as walls, basically as heat discharge systems. Using a thermal balance applied to these systems, an analytic model was formulated to simulate its behavior and to consider the time variation of the environmental temperature, solar radiation, heat storage in the wall and the temperature of the room to be ventilated. The analytical results were compared against experimental data, creating an experimentally validated model that gives confidence on the accuracy and trustworthiness of the analytic proposal. Six tests were carried out in the experimental model. In four of them, the heat flux simulation was performed with electrical resistors; in the other two, solar radiation was directly employed. The results show that the thermal performance of the system can be appropriately determined and described by the analytical model, within a small margin of error. The proposed analytic model can calculate the behavior of a heat discharge system in walls by simply knowing the dimensions of the prototype and the environmental conditions.     

A revised empirical model to estimate solar radiation using automated surface weather observations

The transition from manual to automated weather observations at US National Weather Service Offices has compromised the ability to use these data as a means for estimating global horizontal and direct solar radiation. The creation of long term model-derived solar radiation climatologies continues to rely on the in situ cloud data that these observations provide, since homogeneous and readily available satellite data does not span the transition. An existing semi-physical solar radiation model is revised to allow for the estimation of hourly solar radiation based on these observations. Model evaluation reveals that errors in solar radiation estimates are comparable to other contemporary solar radiation models that estimate global horizontal solar radiation on both daily (10–15% mean absolute error) and hourly (15–19% mean absolute error) timescales. Hourly mean absolute errors are similar for different sky conditions, while daily percent errors are similar between seasons. Model updates also allow for accurate estimates of solar radiation in various climate regimes; regional patterns in model bias are not evident.     

Calculation of solar irradiance profiles from hourly data to simulate energy systems behaviour

In various studies of solar energy systems (sizing, estimation of production), we need solar irradiation data collected with a smaller time interval than those often available in databases (often hourly irradiations). We compare three models (constant, linear and extraterrestrial) which simulate an hourly profile of solar irradiance and allow the determination, with a given reliability, of minute data of global solar irradiation from hourly measured irradiations. These models are compared from the point of view of various statistical tests using data collected on a Mediterranean site. The extraterrestrial model shows very satisfying results and improves greatly the accuracy compared with a constant model.     

Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks

Four variables (total cloud cover, skin temperature, total column water vapour and total column ozone) from meteorological reanalysis were used to generate synthetic daily global solar radiation via artificial neural network (ANN) techniques. The goal of our study was to predict solar radiation values in locations without ground measurements, by using the reanalysis data as an alternative to the use of satellite imagery. The model was validated in Andalusia (Spain), using measured data for nine years from 83 ground stations spread over the region. The geographical location (latitude, longitude), the day of the year, the daily clear sky global radiation, and the four meteorological variables were used as input data, while the daily global solar radiation was the only output of the ANN. Sixty five ground stations were used as training dataset and eighteen stations as independent dataset. The optimum network architecture yielded a root mean square error of 16.4% and a correlation coefficient of 94% for the testing stations. Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.     

Simulation and modeling of solar radiation in Saudi Arabia

A mathematical model is used to generate the hourly data for the total solar radiation on a horizontal surface. The generated data are based on the hourly recorded visibility data for 20 years (1970–1989). The model year technique was then applied to model the 20 years of hourly data of solar radiation into one statistically representative year. A model year of hourly data was then generated for the beam and diffuse components of solar radiation on a horizontal surface. Similarly, a model year of hourly data was also generated for the total solar radiation on tilted surfaces with different orientations with its beam, diffuse and reflected components. A simple methodology is proposed for calculating the solar radiation on vertical surfaces, based on a solar impact factor (SIF). Monthly means and daily totals of hourly sums for each month of the year are discussed. The hourly data of solar radiation for a typical day for each month of the year are presented. The data were generated for the four climatic zones of Saudi Arabia, the hot-dry (Riyadh), the warm-humid (Jeddah), the maritime inland desert climate (Dhahran) and the upland climate zone (Taif). The accuracy of the results is discussed and found to be above 90% representative.     

Neural models of solar collectors for prediction of daily performance

This paper presents the application of an online identification neural technique to the prediction of the in-situ daily performance of solar collectors. First, it is shown that the use of the Laplace transform helps to find the order of an approximated model; the input of the studied system being the solar radiation. Then it is shown that an NNOE model can be more accurate using the right size of the regression vector; the learning database consisting of the data obtained during half a day. Finally, it is shown that a multiple inputs single output (MISO) NNOE model can be accurate; the inputs being the solar radiation and the thermal heat loss conductance that depends on the wind velocity. In any case the differential between the actual value of the daily energy and the value computed by a neural model is less than 0.5%.     

Theoretical variations of the thermal performance of different solar collectors and solar combi systems as function of the varying yearly weather conditions in Denmark

The thermal performances of solar collectors and solar combi systems with different solar fractions are studied under the influence of the Danish design reference year, DRY data file, and measured weather data from a solar radiation measurement station situated at the Technical University of Denmark in Kgs. Lyngby. The data from DRY data file are used for any location in Denmark. The thermal performances of the solar heating systems are calculated by means of validated computer models. The measured yearly solar radiation varies by approximately 23% in the period from 1990 until 2002, and the investigations show that it is not possible to predict the yearly solar radiation on a tilted surface based on the yearly global radiation.The annual thermal performance of solar combi systems cannot with reasonable approximation be fitted to a linear function of the annual total radiation on the solar collector or the annual global radiation. Solar combi systems with high efficient solar collectors are more influenced by weather variations from one year to another than systems with low efficient solar collectors.The annual thermal performance of solar collectors cannot be predicted from the global radiation, but both the annual thermal performance and the annual utilized solar energy can with a reasonable approximation be fitted to a linear function of the yearly solar radiation on the collector for both flat plate and evacuated tubular solar collectors. Also evacuated tubular solar collectors utilize less sunny years with large parts of diffuse radiation relatively better than flat plate collectors.     

Predicting the spectral and broadband aerosol transmittance in the atmosphere for solar radiation modelling

Calculations of the spectral transmittance of the atmospheric aerosol, using Mie theory, for wavelengths between 0 and 40 μm is presented. The chemical composition of the aerosol particles has been modelled in order to correspond to the atmospheric conditions of medium and large coastal or near coastal cities with important industrial and other anthropogenic emission sources. Individual size distributions and optical properties for each aerosol constituent have been considered.Based on the detailed aerosol model, and using parameterization techniques, analytical broadband aerosol transmission functions for the absorption and total attenuation are obtained. The accuracy of the proposed expressions are verified with various tests, using data from the National Observatory of Athens (NOA). The proposed broadband aerosol transmission functions can be incorporated directly into solar radiation models to predict accurately the beam, diffuse and global solar radiation at a given place.     

Generation of operational maps of global solar irradiation on horizontal plan and of direct normal irradiation from Meteosat imagery by using SOLARMET

The physical model SOLARMET, elaborated in ENEA (Italian National Agency for New Technologies, Energy and Environment), provides hourly average global solar irradiance on a horizontal surface (GHi) and hourly average direct normal solar irradiance (DNi) for Italy based on primary satellite images in the visible band.

In the present study, the hourly estimates of surface radiation generated by SOLARMET have been summed up to produce monthly average daily irradiation maps. Hourly and monthly maps were done for the years 1996 and 2002. The parameters of this model were obtained by comparing the Meteosat satellite data with ground data gathered in 2002. Differences, relative to 1996, between SOLARMET estimates and observations obtained over two radiation networks of Italian ground sites are presented: the Meteorological Service of the Italian Air Force and National Agro-Meteorological Network; In total 51 ground stations. The comparison between SOLARMET and the previous Italian method carried out in ENEA shows an improvement due to SOLARMET. Such comparison between the values derived using SOLARMET and previous ENEA methodologies and with data from ground-based stations was possible only for monthly averages of daily global radiation due to an almost total lack of direct radiation ground data in Italy.

The operational monthly solar radiation maps, showing solar energy potentials, permit the selection of construction sites to solar energy project developers. In Italy, these data are necessary for installing solar thermal concentration power plants in support of the R&S program recently funded to demonstrate the possibility of these technologies.     

Comparison of eight clear sky broadband models against 16 independent data banks

A selection of eight high performance clear sky solar irradiance models is evaluated against a set of 16 independent data banks covering 20 years/stations, altitudes from sea level to 1600 m and a large range of different climates. Their performance evaluated on very clear condition measurements are within 4% in term of standard deviation.The conclusions are that the accuracy of the input parameters such as the turbidity is crucial in the validity of the obtained radiation components, and that the choice of a specific model is secondary. The model selection criteria should be based upon either implementation simplicity, input parameter availability (Linke turbidity or aerosol optical depth) or the capacity of the model to produce spectral radiation.