Estimation of monthly average daily global solar irradiation using artificial neural networks

This study explores the possibility of developing a prediction model using artificial neural networks (ANN), which could be used to estimate monthly average daily global solar irradiation on a horizontal surface for locations in Uganda based on weather station data: sunshine duration, maximum temperature, cloud cover and location parameters: latitude, longitude, altitude. Results have shown good agreement between the estimated and measured values of global solar irradiation. A correlation coefficient of 0.974 was obtained with mean bias error of 0.059 MJ/m² and root mean square error of 0.385 MJ/m². The comparison between the ANN and empirical method emphasized the superiority of the proposed ANN prediction model.     

Evaluation and improvement of empirical models of global solar irradiation: Case study northern Spain

This paper presents a new methodology to build parametric models to estimate global solar irradiation adjusted to specific on-site characteristics based on the evaluation of variable importance. Thus, those variables highly correlated to solar irradiation on a site are implemented in the model and therefore, different models might be proposed under different climates. This methodology is applied in a study case in La Rioja region (northern Spain). A new model is proposed and evaluated on stability and accuracy against a review of twenty-two already existing parametric models based on temperatures and rainfall in seventeen meteorological stations in La Rioja. The methodology of model evaluation is based on bootstrapping, which leads to achieve a high level of confidence in model calibration and validation from short time series (in this case five years, from 2007 to 2011).The model proposed improves the estimates of the other twenty-two models with average mean absolute error (MAE) of 2.195 MJ/m²day and average confidence interval width (95% C.I., n = 100) of 0.261 MJ/m²day. 41.65% of the daily residuals in the case of SIAR and 20.12% in that of SOS Rioja fall within the uncertainty tolerance of the pyranometers of the two networks (10% and 5%, respectively). Relative differences between measured and estimated irradiation on an annual cumulative basis are below 4.82%. Thus, the proposed model might be useful to estimate annual sums of global solar irradiation, reaching insignificant differences between measurements from pyranometers.     

Data-driven modelling and optimization of hydrogen adsorption on carbon nanostructures

In the present study, using modelling based on experimental data, models for predicting the hydrogen adsorption isotherm were presented. The three Automatic Learning of Algebraic Models (ALAMO), feed-forward artificial neural networks (ANNs), and group method of data handling-type polynomial neural networks (GMDH-PNN) were constructed. The created models were evaluated to predict the equilibrium data of hydrogen storage on carbon nanostructures, including activated carbons doped with palladium (Pd) nanoparticles, fullerene pillared graphene nanocomposites, and nickel (Ni)-decorated carbon nanotubes. The inputs were nanostructure characteristics such as surface area, pore-volume, and thermodynamic conditions such as pressure. The generalization of the trained models was acceptable, and the models successfully predicted the hydrogen adsorption isotherm for new inputs. The relative error percentage for most data points is less than 4%, which demonstrates their applicability in determining adsorption isotherms for any operating conditions. By performing error analysis calculations, it was shown that the ALAMO model has the highest accuracy. Also, sensitivity analysis calculations show that pressure is the most influential parameter in the adsorption process. Besides, by performing Genetic Algorithm (GA) optimization using the ALAMO model, the amount of pressure and adsorbent properties were determined so that the amount of hydrogen adsorption is maximized. According to the optimization results based on the GA, the higher the pressure, the greater the amount of hydrogen adsorption. The nanotubes with a surface area of 194.15 m²/g, a total volume of 1.8 cm³/g, micropore volume of 0.097 cm³/g, and mesopore volume of 0.963 cm³/g, graphene with a surface area of 2977.13 m²/g, a total volume of 1.5134 cm³/g, density of 617.45 kg/m³, and activated carbon at pressures less than 30 bar with a surface of 2546.36 m²/g, a total volume of 1.237 cm³/g, micropore volume of 0.839 cm³/g, and activated carbon at pressures more than 30 bar with a surface of 3027 m²/g, a total volume of 1.343 cm³/g, a micropore volume of 0.9582 cm³/g, and a mesopore volume of 1.23 cm³/g, have the highest amount of stored hydrogen.     

Survey of the incident solar radiation in Brazil by use of meteosat satellite data

Meteosat-2 satellite data in the visible band were used to calculate monthly averages from daily mean incident solar radiation over Brazil, using the IGMK physical model for the period 1985–1986. Satellite estimates are compared with ground data from 22 national stations. The global root mean square error between model and ground results for all data points was 13%, and the mean bias error was 1.23 MJ m⁻². About 68% of the individual errors were below 10% and clustered around 8%. Global radiation estimated by the model ranged from 9.0 to 27 MJ m⁻² with the diffuse to global ratio falling into the 0.2–0.5 interval. Enhanced inverted zonal trends were found for both satellite predictions and the ground station results.     

Optimal angles for harvesting solar electricity in some African cities

In recent years, the quest for renewable and sustainable energy has been extensive while solar energy has been on the vanguard of sustainable alternative and renewable energy sources due to its clean nature and cost effectiveness for most human activities such as water pumping and electric power generation, amongst others. The off beam installation of Photovoltaic (PV) modules has posed a severe challenge to the optimal functioning of these PV cells despite the abundance of solar irradiation receivable in most African cities. This paper presents the Optimal Inclination Angles (OIA) for mounting PV modules in the absences of a mechanized or automated solar tracking device, for optimum yield in solar electricity generation for some selected African cities using the Photovoltaic Geographic Information Systems (PVGIS) dataset. The OIA of the selected African cities has been identified for optimal solar irradiation exploitation and if the modules are mounted on a horizontal plane, it is expected that considerable amount of solar irradiation would not be harnessed as it has been estimated using the difference from the Irradiation on OIA (Hopt) and the Irradiation on horizontal plane (Hh), whose difference shows that the northern African cities, Algiers, Rabat and Tripoli, are seen to have high levels in unutilized solar irradiation of 780 Wh/m², 760 Wh/m² and 680 Wh/m² respectively while Harare, Lusaka, Maiduguri, Khartoum, Maputo and Luanda would have considerably high levels in untapped solar irradiation of 360Wh/m², 330 Wh/m², 180 Wh/m², 260 Wh/m², 570 Wh/m² and 80 Wh/m² respectively if PV modules are mounted on horizontal plane. However cities such as Bangui, Abidjan and Mogadishu have quite low levels in unexploited solar irradiation of 40 Wh/m², 70 Wh/m² and 10 Wh/m² respectively when PV modules are mounted on horizontal plane. These differences show the amount of solar irradiation which if adequately harnessed, adds to the solar energy potentials of the region.     

Parameterization of a simple model to estimate monthly global solar radiation based on meteorological variables, and evaluation of existing solar radiation models for Tabouk, Saudi Arabia

Using 9 years of solar radiation data, we established a simple model to calculate the monthly mean global solar radiation on a horizontal surface in Tabouk (28.38° N, 36.6° E, Saudi Arabia). The model correlates the global solar radiation with five meteorological parameters. These parameters are the perceptible water vapor, air temperature, relative humidity, atmospheric pressure, and the mean monthly daily fraction of possible sunshine hours. The estimated global radiation from the model was compared with the measured values using the mean bias error (MBE), coefficient of correlation (R), root mean square error (RMSE), and mean percentage error (MPE). The t statistics were also applied as another indication of suitability. The model has a high coefficient of correlation (R = 0.99), MBE = −14 × 10⁻⁴ kW h/m², RMSE = 0.10 kW h/m², and MPE = −0.03%. It is believed that the model developed in this work is applicable for estimating, with great accuracy. The monthly mean daily global radiation at any site having similar conditions to those found in Tabouk.Furthermore, 29 regression models available in the literature were used to estimate the global solar radiation data for Tabouk. The selected models were different in terms of the variables they use and in the number of the variables they contained. The models were compared on the basis of the statistical errors considered above. Apart from Abdall’s model, which showed a reasonable estimate (MPE = −2.04%, MBE = −0.22 kW h/m², and RMSE = 0.59 kW h/m²), all the models under or overestimate the measured solar radiation values. Comparisons between these models and the produced model, from this study, were also considered. According to the statistical results, the model of Abdall showed the prediction closest to those estimated using the developed model.     

Modelling of solar energy potential in Nigeria using an artificial neural network model

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4–14°N, log. 2–15°E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983–1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01–5.62 to 5.43–3.54 kW h/m² day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.     

Estimating monthly mean valuesof daily total solar radiation for Australia

Monthly mean values of daily total solar radiation were obtained for the widest possible network acrossAustralia. Bureau of Meteorology sources yielded 11 stations with long term records of both measured daily total solar radiation and sunshine hour values. Monthly modified Angstrom equations were developed from these data and used to estimate radiation values for a further 90 stations in the Bureau of Meteorology network that had sunshine hour data. Measured daily total solar radiation data were obtained from a variety of sources mostly outside the Bureau of Meteorology network for an additional 33 stations. Finally, estimates of solar radiation from detailed cloud cover data were used for a further 12 stations, selected because they filled in significant gaps in coverage. These various sources yielded a total of 146 sets of monthly mean values of daily total solar radiation. For each month optimal surfaces, which were functions of position only, were fitted to this network of values using Laplacian smoothing splines with generalized cross validation. Residuals from the fitted surfaces at the data points were acceptably low. Fitted surfaces which included, in addition to position variables, a cloudiness index based on a transform of mean monthly precipitation further reduced these residuals. The latter fitted surfaces permit estimation of monthly mean values of total daily solar radiation at any point on the continent with a root mean square predictive error of no more than 1.25 MJ m⁻² day⁻¹ (5.2 per cent of the network mean) in summer and 0.74 MJ m⁻² day⁻¹ (5.5 per cent of the network mean) in winter.     

Monthly mean solar radiation statistics for Australia

Tables of monthly mean solar radiation parameters are computed from detailed cloud cover information. The parameters include direct and global daily total energy inputs to horizontal, inclined and “sun-tracking” surfaces. Comparison with measured global radiation at 12 stations reveals virtually no systematic error in the computation scheme, and an error of 2MJ m⁻² day⁻¹ in the worst case month of any station.     

Year round test of a solar adsorption ice maker in Kunming,China

A solar adsorption ice maker with activated carbon–methanol adsorption pair was developed for a practical application. Its main features include utilization of a water cooled condenser and removing all valves in the refrigerant circuit except the one that is necessary for refrigerant charging. Year round performance tests of the solar ice maker were performed in Kunming, Yunnan Province, China. Test results show that the COP (coefficient of performance) of the solar ice maker is about 0.083–0.127, and its daily ice production varies within the range of 3.2–6.5 kg/m² under the climatic conditions of daily solar radiation on the surface of the adsorbent bed being about 15–23 MJ/m² and the daily average ambient temperature being within 7.7–21.1 °C. The suitable daily solar radiation under which the solar ice maker can run effectively in Kunming is above 16 MJ/m².     

Effect of high irradiation on photovoltaic power and energy

Solar photovoltaics (PV) is a promising solution to combat against energy crisis and environmental pollution. However, the high manufacturing cost of solar cells along with the huge area required for well‐sized PV power plants are the two major issues for the sustainable expansion of this technology. Concentrator technology is one of the solutions of the abovementioned problem. As concentrating the solar radiation over a single cell is now a proven technology, so attempt has been made in this article to extend this concept over PV module. High irradiation intensity from 1000 to 3000 W/m² has been investigated to measure the power and energy of PV cell. The numerical simulation has been conducted using finite element technique. At 3000 W/m² irradiation, the electrical power increases by about 190 W compared with 63 W at irradiation level of 1000 W/m². At the same time, at 3000 W/m² irradiation, the thermal energy increases by about 996 W compared with 362 W at 1000 W/m² irradiation. Electrical power and thermal energy are enhanced by about 6.4 and 31.3 W, respectively, for each 100‐W/m² increase of solar radiation. The overall energy is increased by about 179.06% with increasing irradiation level from 1000 to 3000 W/m². It is concluded that the effect of high solar radiation using concentrator can significantly improve the overall output of the PV module.     

A model for determining the global solar radiation for Makurdi,Nigeria

An empirical model for determining the monthly average daily global solar radiation on a horizontal surface for Makurdi, Nigeria (Latitude 7°7′N and Longitude 8°6′E) was developed using the Angstrom–Page equation. The solar radiation (W/m²), hours of bright sunshine and cloudiness were measured hourly from 0600 H to 1800 H daily for 18 months. The constants ‘a’ and ‘b’ of the Angstrom linear type equation were determined by plotting the clearness index (H/H_o) against the possible sunshine hours (n_s/N) to obtain the line of best fit. The constant ‘a’ was obtained from the intercept of the line on the y-axis while the constant ‘b’ was obtained from the slope of the line. The developed model for determining the global horizontal solar radiation at the location was H = H_o [0.17 + 0.68(n/N)] with a coefficient of correlation of 0.78. The mean bias error and root mean square error that were used to test the performance of the constants were 0.17% and 1.22% respectively. The measured solar radiation was compared with the solar radiation predicted by the model and no significant difference was found between them using F-LSD at P ≤ 0.05.     

Development of an economical model to determine an appropriate feed-in tariff for grid-connected solar PV electricity in all states of Australia

Australia is a country with a vast amount of natural resources including sun and wind. Australia lies between latitude of 10–45°S and longitude of 112–152°E, with a daily solar exposure of between less than 3 MJ/(m² day) in winter and more than 30 MJ/(m² day) in summer.Global solar radiation in Australia varies between minimum of 3285 MJ/(m² year) in Hobart to 8760 MJ/(m² year) in Northern Territory. As a result of this wide range of radiation level there will be a big difference between costs of solar PV electricity in different locations.A study we have recently conducted on the solar PV electricity price in all states of Australia. For this purpose we have developed an economical model and a computer simulation to determine the accurate unit price of grid-connected roof-top solar photovoltaic (PV) electricity in A$/kWh for all state of Australia. The benefit of this computer simulation is that we can accurately determine the most appropriate feed-in tariff of grid-connected solar PV energy system. The main objective of this paper is to present the results of this study.A further objective of this paper is to present the details of the unit price of solar PV electricity in the state of Victoria in each month and then to compare with electricity price from conventional power systems, which is currently applied to this state. The state Victoria is located south of Australia and in terms of sun radiation is second lowest compared with the other Australian states.The computer simulation developed for this study makes it possible to determine the cost of grid-connected solar PV electricity at any location in any country based on availability of average daily solar exposure of each month as well as economical factors of the country.     

An assessment of solar energy potential in Kampuchea

A satellite technique was adopted to assess solar energy potential in Kampuchea. The study aims to explore solar irradiation potential and distribution under the influence of Asian monsoons over land and a large water surface of a lake by using the satellite technique, with a relatively small spatial scale, which have never been accessed before. In this study, the solar irradiation potential over Kampuchea (10°N–14.5° N, 101.5°E–105°E) was estimated at interval of half a degree grid. The seasonal variations of mean daily solar irradiation in Kampuchea were measured during two Asian winter and summer monsoon seasons.The results revealed that the mean solar irradiation depends more on orographic effects than on seasonal changes. During the winter monsoon, the local minimal means of daily solar irradiation were found on the great Lake Tonle Sap and on the northern, windward side of the Elephant Mountain with a range of 13–14 MJ m^?2 day^?1. The local maximal means of daily solar irradiation were found on the northwestern part of Kampuchea, with a value of 18 MJ m^?2 day^?1. In contrast, during the summer monsoon, the local minimal means of daily solar irradiation were, again, found on the same mountainous region of the Elephant Mountain, but the area of minimal means shifted to the southern side where it is the windward side of the mountain during the summer monsoon with a value of 12 MJ m^?2 day^?1. The local maximal means of the daily solar irradiation were found scattered over various areas: south of Lake Tonle Sap and at various places in the north and northwestern parts of the country, with a range of 18–19 MJ m^?2 day^?1. It was also found that a high mean of solar irradiation is generally associated with a low standard deviation, i.e., it is less in temporal variation.     

The solar radiation climate of Thailand

Geographical, seasonal, and diurnal variations of global solar radiation in Thailand are surveyed. Seasonal effects are shown by separate studies for eight 1.5 month periods of the year defined by standard solar declination values. Detailed maps are given of the geographical distribution of solar radiation prepared from data on cloudiness at 44 stations, duration of sunshine at 18 stations, and linear regressions relating radiation to sunshine at Chiang Mai and Bangkok. The highest mean values are above 19.5 MJ m⁻² d⁻¹ and are widespread in spring. The lowest values are below 15.0 MJ m⁻² d⁻¹ in restricted localities with heavy rainfall in autumn.Rough estimates of diffuse solar radiation and atmospheric turbidity are made from the radiation-sunshine regression parameters. Diffuse radiation averages 8.4 MJ m⁻² d⁻¹. Turbidity at Chiang Mai is high in spring and low in summer and autumn; at Bangkok it is high throughout the year.The diurnal variation of global solar radiation determined from hourly measurements at Chiang Mai and Bangkok is analysed. The mean midday radiation fluxes range from 0.80 kW m⁻² in spring to 0.60 kW m⁻² in autumn. On the average the radiation received in the afternoon is slightly less than that received in the morning.     

Modeling of solar radiation using remote sensing and artificial neural network in Turkey

Artificial neural networks (ANNs) were used to estimate solar radiation in Turkey (26–45°E, 36–42°N) using geographical and satellite-estimated data. In order to train the Generalized regression neural network (GRNN) geographical and satellite-estimated data for the period from January 2002 to December 2002 from 19 stations spread over Turkey were used in training (ten stations) and testing (nine stations) data. Latitude, longitude, altitude, surface emissivity for ?_4, surface emissivity for ?_5, and land surface temperature are used in the input layer of the network. Solar radiation is the output. Root Mean Square Error (RMSE) and correlation coefficient (R²) between the estimated and measured values for monthly mean daily sum with ANN values have been found as 0.1630 MJ/m² and 95.34% (training stations), 0.3200 MJ/m² and 93.41% (testing stations), respectively. Since these results are good enough it was concluded that the developed GRNN tool can be used to predict the solar radiation in Turkey.     

Daily UV radiation modeling with the usage of statistical correlations and artificial neural networks

The information regarding solar UV radiation (UVA + UVB) in Brazil and around the world is scarce with low spatial and temporal coverage. This information scarcity, due to the small number of measuring stations, has directed some researchers towards the creation of computational parametric models or the generation of statistical models for the estimation of the UV radiation from the measurement of the global radiation. Information about UV irradiation is expanded for other places where there is only global radiation data. Thus, two stations were set up, in 2008, one in the city of Pesqueira and the other in Araripina, both in the state of Pernambuco, for simultaneous measurements of daily global solar and UV radiation. Another station is being set up in Recife-PE, completing a group of stations that are located between latitudes 8 and 10° and longitudes 34–38° W, representing the typical climate of the region. The daily values of G global and UV ultraviolet radiation (A + B) striking the horizontal plane in Pesqueira and Araripina during the time period (2008–2010) were measured, analyzed and compared. The collected data enabled the generation of three different statistical models for estimating the daily UV solar radiation from the daily global radiation: a) linear correlation between global and UV radiation (model 1), b) polynomial correction of the average fraction of UV irradiation, 〈F_UV〉 as a function of the transmittance index of global solar irradiation 〈K_T〉 (model 2) and c) the UV atmospheric transmittance index 〈K_TUV〉 is obtained by multiple regression of the air mass 〈m_r〉and 〈K_T〉 (model 3). Besides, they were modeled by two artificial neural networks: a) estimative of the (F_UV), considering the same physical variables of model 2 (model ANN1) and b) estimative of (K_TUV) from the same physical variables of model 3 (model ANN2). The statistical models and the artificial neural networks displayed a good statistical performance with RMSE% inferior to 5% and MBE between ?0.4%–2%. All the models can be used for estimating the UV radiation in places where there is only global irradiation data.     

An improved dynamic test method for solar collectors

A comprehensive improvement of the mathematical model for the so called transfer function method is presented in this study. This improved transfer function method can estimate the traditional solar collector parameters such as zero loss coefficient and heat loss coefficient. Two new collector parameters t and m_fC_f are obtained. t is a time scale parameter which can indicate the heat transfer ability of the solar collector. m_fC_f can be used to calculate the fluid volume content in the solar collector or to validate the regression process by comparing it to the physical fluid volume content if known. Experiments were carried out under dynamic test conditions and then test data were processed using multi-linear regression method to get collector parameters with statistic analysis. A comparison of the collector parameters obtained from the improved transfer function (ITF) method and the quasi-dynamic test (QDT) method is carried out. The results show that the improved transfer function method can accurately obtain reasonable collector parameters. The influence of different averaging time intervals is investigated. Based on the investigation it is recommended to use on line calculation if applicable for the second-order differential term with 6–9 min as the best averaging time interval. The measured and predicted collector power output of the solar collector are compared during a test of 13 days continuously both for the ITF method and the QDT method. The maximum and averaging error is 53.87 W/m² and 5.22 W/m² respectively of the ITF method while 64.13 W/m² and 6.22 W/m² of the QDT method. Scatter and relative error distribution of the measured power output versus the predicted power output is also plotted for the two methods. No matter in either error analysis or scatter distribution, the ITF method is more accurate than the QDT method in predicting the power output of a solar collector.In conclusion, all the results show that the improved transfer function method can accurately and robustly estimate solar collector parameters and predict solar collector thermal performance under dynamic test conditions.     

Solar radiation data for Beer Sheva, Israel

A number of years of data on the global irradiation incident on a horizontal surface in Beer Sheva, Israel (lat. = 31°15′N, long. = 34°48′E, elevation 240 m) have been correlated. It is apparent from these data that this locale possesses a relatively high abundance of sunshine. The average cumulative annual irradiation is 6722MJ/m² and the average daily irradiation is 18.43 MJ/m². The percentage frequency of days possessing irradiation rates greater than 20 MJ/m² is 46 per cent, whereas that possessing less than 10 MJ/m²d is 11.9 per cent. The percentage frequency of cloudy days (K_T < 0.34) is low, 7.5 per cent, whereas that for clear days (K_T > 0.65) is 29.2 per cent.     

Experimental study on the performance of solar-assisted multi-functional heat pump based on enthalpy difference lab with solar simulator

In the enthalpy difference lab with a solar simulator, the performance of the indirect expansion solar-assisted multi-functional heat pump (IX-SAMHP) can be tested in stable external environment quantificationally. In this paper, the performances of the IX-SAMHP working in the solar water heating mode and solar space heating mode were compared under different conditions. The experimental results indicate that the evaporating heat exchange rate and condensing heat exchange rate were synthetically effected by the evaporating and condensing temperature in the solar water heating mode. Moreover, compared with the situation without irradiation, when the irradiation was 500 W/m², the evaporating heat exchange rate and condensing heat exchange rate increased by 37.4% and 32.3%, respectively. In the solar space heating mode, when the irradiation increased from 0 W/m² to 500 W/m², the heating capacity increased by 20.4%. In the second-law analysis, the calculating results demonstrate that the exergy efficiency of the IX-SAMHP would be enhanced by inputting solar energy to the evaporator.