Monthly Average Solar Radiation Research Articles

Modeling of diffuse solar radiation exergy in Turkey: Comparison of diffuse fraction and diffusion coefficient models

AbstractIn this study, 19 regression models were established for the prediction of exergy value of monthly average diffuse solar radiation with a single or multiple input predictor (Relative sunshine duration, clearness index, relative humidity, and air temperature). Five model categories were defined depending on the diffuse fraction and diffusion coefficient. The most frequently used statistical indicators were used to determine the performance of the empirical models. The global performance indicator (GPI) method was used to determine the accuracy ranking of the models. Consequently, the results show a good agreement with the values ​​estimated from the regression models and the measurement data. The regression models established in this study can be used with great accuracy to evaluate the monthly average diffuse solar radiation exergy in the regions under consideration or in similar climatic conditions.

Creation of Turkey's Solar Exergy Potantial Atlas (SEXPATR)

In this study, Turkey's annual and monthly solar exergy potential atlases (SEXPATR) in ArcGIS software has been created by calculating exergy of solar radiation values for all provinces in Turkey (MJ/m2yr). Annual solar radiation exergy values for Turkey 4346,67 MJ/m2yr and 5613,68 MJ/m2yr calculated and the average annual solar radiation exergy amount was set at 5034,37 MJ/m2yr. The highest value was determined for Karaman province and the lowest value for Bartın province. The average monthly solar radiation exergy value was determined as 160,25 MJ/m2months in average at the lowest December and 665,06 MJ/m2months in July at the highest. The annual exergy / energy ratio was calculated between 92,95% and 94,17%, with an annual average value of 93,39%.

Design of a Photovoltaic Mini-Grid System for Rural Electrification in Sub-Saharan Africa

This paper presents a detailed design of a photovoltaic (PV) system for use in the rural electrification of remote settlements that are far off from the electricity grid. Since investment in building transmission lines from the grid to these localities is not viable, a good solution is an installation in these areas of standalone photovoltaic systems. The design process comprises the choice and dimensioning of the solar panels, the battery storage, DC-AC inverter, and mini transmission grid to the different homes. The design is for a 15 kW PV system including an economic evaluation and analysis using Hybrid Optimization of Multiple Energy Resources (HOMER) software. Data on the average monthly solar radiation and temperature were obtained from various sources, including, Photovoltaic Geographical Information System (PVGIS) for Africa. From this data the study area receives a monthly average solar insolation of 6.16 kWh/m2/day with the worst month being August with 5.22 kWh/m2/day. The total daily electrical energy consumption is estimated to be about 72.525 kWh. Simulation results using HOMER software shows that the overall capital cost of the PV system components is $122,337, a replacement cost of $12,889 and an operation and maintenance cost of $29,946 over 10years. A financial analysis of the system showed that the design was both viable and sustainable with low maintenance cost.

Investigation of the Geometric Shape Effect on the Solar Energy Potential of Gymnasium Buildings

Gymnasium are typically large-span buildings with abundant solar energy resources due to their extensive roof surface. However, relevant research on this topic has not been thoroughly conducted to investigate the effect of the geometric shape of gymnasium buildings on their solar potential. In this paper, an investigation of the geometric shape effect on the solar potential of gymnasium buildings is presented. A three-dimensional radiation transfer model coupled with historical meteorological data was established to estimate the real-time solar potential of the roof of a gymnasium building. The rooftop solar potential of three typical building foundation shapes and different types of roof shapes that have evolved was systematically analyzed. An annual solar potential cloud map of each gymnasium building is generated. The monthly and annual average solar radiation intensities of the different types of roof shapes are investigated. Compared to the optimal tilt angle, the maximum decrease in the average radiation intensity reached −20.42%, while the minimum decline was −8.64% for all types of building shapes. The solar energy potential fluctuated by up to 11% across the various roof shapes, which indicate that shape selection is of vital importance for integrated photovoltaic gymnasium buildings. The results presented in this work are essential for clarifying the effects of the geometric shape of gymnasium buildings on the solar potential of their roofs, which provide an important reference for building design.

Estimation of Solar Energy Potential Using Sunshine-Based Model for Busa-Baso Kebele, Dirashe Woreda, Ethiopia

The purpose of this study was focused on estimation of solar photovoltaic power systems in Busa Baso, Dirashe Woreda, Ethiopia. Solar photovoltaics are being promoted to replace fuel-based lighting and off-grid electrical needs. It was conducted to assess and evaluate the data collections which were administered on selected site. The data collected were analyzed using descriptive survey. The radiation had to be predicted from sunshine hours by employing empirical models. Using data from National Aeronautics and Space Administration (NASA), linear monthly average solar radiation has been developed. The paper presents an estimation of the solar energy resource based on the primary data taken between January 2019 and December 2019.

Estimation and validation of a clear sky hourly global solar radiation in Beni-Suef, Egypt

The availability of hourly and daily solar radiation data is considered one of the important design tools for several solar energy applications. The objective of this work is to develop a model to calculate the hourly solar radiation falling on a horizontal surface in Beni-Suef city, Egypt via a validated simulation model. This model will help the researches in upper Egypt universities along with the designing of solar energy projects in the Beni-Suef city residential and industrial zones. The average solar radiation is calculated daily on hourly basis throughout the year, then the monthly average solar radiation is estimated. The results indicate the maximum value of average solar radiation is in June as 8.1 kWh/m2/day. Solar radiation is also calculated with several tilt angles to find out the optimum tilt angle that enable the surface to receive maximum solar radiation. The calculations focused on empirical equations of solar radiation and the experimental measurements obtained from the nearest station to the location. The model describes solar equations and it can be used to estimate global irradiance. Also, it shows the parameters of the system and climate change of the specific site (Beni-Suef city). Results are validated by comparison between the measured, calculated, and published reference data for the specific site and obtained good agreement with each other. It is found also that the optimal tilt angle that receives maximum solar radiation is 29 deg. which comply with the latitude angle of Beni-Suef city.

An Image Processing-Based Method to Assess the Monthly Energetic Complementarity of Solar and Wind Energy in Colombia

Solar and wind energy systems, without storage, cannot satisfy variable load demands, but their combined use can help to solve the problem of the balance between generation and consumption. Energetic complementarity studies are useful to evaluate the viability of the use of two or more renewable energy sources with high variability in a specific interval of time in a determined region. In this paper, the monthly energetic complementarity study of solar and wind resources of Colombia is carried out. A novel approach to conduct the study is proposed. A dataset with the average monthly solar radiation and wind speed values is obtained from high-resolution images of renewable resources maps, using image processing algorithms. Then, the dataset is used to calculate the energetic complementarity of the sources employing the negative of the Pearson correlation coefficient. The obtained values are transformed to energetic complementarity maps, previously eliminating the protected areas. The obtained results show that there is a good energetic complementarity in the north and northeastern regions of the country throughout the year. The results indicate that projects related to the joint use of solar and wind generation systems could be developed in these regions.

Optimal design and evaluation of a hybrid energy system for off-grid remote area

ABSTRACTThis paper discusses optimal solutions for supplying electricity to consumers in the Dibba area, which requires 27 MW of power. Data on wind speed and average monthly solar radiation were collected and projected seven-year power demand was assessed. According to HOMER software analyses, the best solution for Dibba according to the software would be a combination of wind, natural gas, and diesel. Pursuing such a hybrid energy solution would result in electricity produced for USD$ 0.0787/kWh. A solution incorporating multiple energy sources would require the lowest amount of fuel annually and would do the least damage to the environment due to a reduction in greenhouse gases emission. Using these hybrid systems for off-grid connections will definitely provide a safer power supply to Dibba while strengthening Oman’s existing power supply system.

On the performance, economic, and environmental assessment of integrating a solar-based heating system with conventional heating equipment; a case study

In this paper, the performance, economic, and environmental aspects of integrating a flat plate solar collectors (FPSC)-based system with conventional heating equipment fueled by natural gas are evaluated for a residential building, located in Urmia city, Iran. The main component of the conventional domestic heating equipment is a hot water boiler, powered by a burner. Generally, the present analysis is divided into two parts. The first part is based on analyzing the system for 12 daylight hours in which solar radiation is available, while the second part evaluates it for 12 night hours. To provide the heating load of the building during daylight hours, the solar-based system is used, and the conventional equipment is utilized at night hours. The real heating load of the building constituted of the total load relevant to hot consumed water and the building thermal loss rate in cold months and the available solar radiation are calculated and employed in the simulations. Also, the monthly average total solar radiation, applied in the simulations, is between 7.63 MJ/m2/day and 25.39 MJ/m2/day throughout a year. The results revealed that the highest heating load for air conditioning purposes in warm and cold days are 37.52 kW and 91.05 kW, respectively. Furthermore, the highest heating load is provided using 63.3 m2 and 443.8 m2 of FPSCs for hot and cold air conditioning, correspondingly. Besides, the maximum payback period of coupling the FPSCs with the conventional equipment is found to be 3.1 years. Also, the employment of FPSCs contributes to reductions in CO2 emission rate with values of 84.67 kg/MWh and 84.71 kg/MWh for cold and warm days, respectively.

The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model

New development of dynamic evolving neural-fuzzy inference system model was proposed for modeling solar radiation based on univariable air temperature scheme. The proposed predictive model was validated against several robust heuristic regression models including multivariate adaptive regression spline, M5 model tree and least square support vector regression. Historical data of solar radiation and air temperature for two meteorological stations: Adana and Antakya located in Turkey, were investigated. The prediction results were evaluated based on several statistical metrics. The modeling is conducted based on different data division scenarios (training/testing) phases. The attained prediction results evidenced the potential of the dynamic evolving neural-fuzzy inference system over the comparable models and for all the investigated data division scenarios. In quantitative terms, dynamic evolving neural-fuzzy inference system was enhanced the solar radiation prediction capability over the multivariate adaptive regression spline, M5 model tree and least square support vector regression models (Adana-Antakya) by 20–42%, 29–47% and 19–43% based on the root mean square errors metric. The applied predictive models were compared with the field measured average monthly solar radiation values and it was found that the proposed model estimates accurately. However, the comparable models were exhibited a considerable overestimation of the monthly averaged solar radiation values and for both inspected stations specifically in the summer months (June, July and August).

Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea

Forecasting solar radiation has recently become the focus of numerous researchers due to the growing interest in green energy. This study aims to develop a seasonal auto-regressive integrated moving average (SARIMA) model to predict the daily and monthly solar radiation in Seoul, South Korea based on the hourly solar radiation data obtained from the Korean Meteorological Administration over 37 years (1981–2017). The goodness of fit of the model was tested against standardized residuals, the autocorrelation function, and the partial autocorrelation function for residuals. Then, model performance was compared with Monte Carlo simulations by using root mean square errors and coefficient of determination (R2) for evaluation. In addition, forecasting was conducted by using the best models with historical data on average monthly and daily solar radiation. The contributions of this study can be summarized as follows: (i) a time series SARIMA model is implemented to forecast the daily and monthly solar radiation of Seoul, South Korea in consideration of the accuracy, suitability, adequacy, and timeliness of the collected data; (ii) the reliability, accuracy, suitability, and performance of the model are investigated relative to those of established tests, standardized residual, autocorrelation function (ACF), and partial autocorrelation function (PACF), and the results are compared with those forecasted by the Monte Carlo method; and (iii) the trend of monthly solar radiation in Seoul for the coming years is analyzed and compared on the basis of the solar radiation data obtained from KMS over 37 years. The results indicate that (1,1,2) the ARIMA model can be used to represent daily solar radiation, while the seasonal ARIMA (4,1,1) of 12 lags for both auto-regressive and moving average parts can be used to represent monthly solar radiation. According to the findings, the expected average monthly solar radiation ranges from 176 to 377 Wh/m2.

MODELING STUDY OF PV POWER PLANTS IN DIFFERENT IRAQ’S GOVERNORATES

The available huge amount of solar radiation in Iraq all over the year can be regarded as a useful energy source. It can be utilized to generate electrical energy using PV panel’s power plants. Therefore, it is important to recognize the behavior of these PV Panels through the year with changing in amount of the solar radiation and weather temperature. The monthly average (daily and hourly) of extraterrestrial, global, diffused, and beam radiation, with PV panel’s temperature and electrical efficiency of different tilt angle is studied in three Iraqi’s cities. The results indicated that the total radiation ranges are greater in Karbala than Samarra and Basra cities; despite of the extraterrestrial radiation in Basra higher value than Samarra and Karbala cities. The higher magnitude in the monthly average solar radiation in any city is not necessarily best location for construction PV power plant, because the electrical performance of PV panels is affected by its temperature. Rising in temperature led to drop in the PV panel’s efficiency.

Optimization of installing angles of photovoltaic panels to maximize solar radiation by simulated annealing algorithm

In this study, simulated annealing (SA) algorithm was used to optimize the installing angles, specifically the tilt angle and surface azimuth angle, to maximize the solar radiation on photovoltaic (PV) panels in Wuhan, China. The KT model was adopted to calculate the monthly average solar radiation on the tilt PV panels and the SA algorithm was chosen to do the optimization. The optimized installing angles obtained by the KT method was compared with those obtained by the experimental solar radiation information, and the results agreed quite well. It is found that the summer in Wuhan has the largest monthly solar radiation. The optimized tilt angle varies in different months but the optimized surface azimuth angle is approximate 0 degree. It is implied that the tilt angle is the key parameter in the PV installation rather than the azimuth angle. For the yearly total solar radiation, the optimized tilt angle is 15°, which is about 15.52° less than the latitude.

Assimilation of Optimal Sized Hybrid Photovoltaic-Biomass System by Dragonfly Algorithm with Grid

A growing interest in renewable energy resources has been observed for several years, due to their pollution-free nature, availability all over the world, and continuity. These facts make these energy resources attractive for many applications. In this work, the hybrid combination of a photovoltaic-biomass system is investigated as an energy source. This paper determines optimal sizing and cost reduction of grid-integrated renewable energy resources by using an intelligence optimization technique, the dragonfly algorithm. The efficiency of the proposed methodology is also compared with an existing technique, which uses the artificial bee colony (ABC) algorithm. The scope of this work is to reduce the annual total cost of power with a reduced number of solar panels. The monthly average solar radiation is used to compute the obtained power. The outcome of the proposed technique proves that the grid-connected system with an optimal number of components satisfactorily meets the needs of the village at a reduced price. The simulation results are carried out under the MATLAB environment. The comparison of results clearly demonstrates that the proposed system is much more efficient than the existing one.

Modeling and mapping of solar radiation using geostatistical analysis methods in Iran

Because of economic and technical limitations, measuring solar energy received at ground level (R s ) isn’t possible in all parts of the country, and in only 12% of synoptic stations is this parameter measured and recorded. Thus, it should be estimated and modeled spatially based on other climatic variables using mathematical methods. In this research, many attempts have been made to introduce an air temperature-based model for Rs estimation, and then, based on the output of the mentioned models, several geostatistical methods have been tested, and finally an elegant spatial model is proposed for (Rs) zoning in Iran. In this regard, the relationships between the measured amounts of monthly solar radiation and other climatic parameters, such as a monthly average, maximum and minimum temperature, precipitation, relative humidity, and the number of sunny hours during the period 1970–2010, are examined and modeled. It was revealed that based on the linear relationship between the monthly average air temperatures and solar radiation values recorded in each of the stations, that the best-fit linear model, with R 2 = 0.822, MAE = 1.81, RMSE = 2.51%, and MAPE = 10.08, can be introduced for Rs estimation. Then, using the outputs of the proposed model, the amounts of (R s ) are estimated in another 171 meteorological stations (a total of 192 stations), and eight geostatistical methods (IDW, GPI, RBF, LPI, OK, SK, UK, and EBK) were investigated for zoning. Comparing the resulting variograms showed that in addition to proof of spatial correlation between solar radiation data, they can be applied for modeling changes in various directions. Analyzing the ratio of the nugget effect on the roof of the variograms showed that the Gaussian model with the lowest ratio (Co/Co + C = 0.883) and (R 2 = 0.972), could model the highest correlation between the data and, therefore, it was used for data interpolation. To select the best geostatistical model, R2, MAE, and RMSE were used. On this basis, it was found that the RBF method with R 2 = 0.904, MAE = 3.02, RMSE = 0.39% is the most effective. Also, the IDW method with R 2 = 0.90, MAE = 3.08, RMSE = 0.391%, compared to other methods is the most effective. In addition, for data validation, correlations between observed and estimated values of solar radiation were studied and found R 2 = 0.86.

NEW MODEL FOR SOLAR RADIATION ESTIMATION FROM MEASURED AIR TEMPERATURE AND RELATIVE HUMIDITY IN NIGERIA

Solar radiation prediction is essential for effective and reliable solar power project, predicted solar radiation can be used for accurate solar energy prediction. Solar radiation measurement is not sufficient in Nigeria for various reasons such as maintenance and repair cost, calibration of instrument, and expansive of measuring device. In this paper, adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the monthly average solar radiation in Nigeria. Air temperature of monthly mean minimum temperature, maximum temperature and relative humidity obtained from Nigerian Meteorological Agency (NIMET) were used as inputs to the ANFIS model and monthly mean global solar radiation was used as out of the model. Statistical evaluation of the model was done based on root mean square error (RMSE) and correlation coefficient R to examine the accuracy of the developed model. The values of RMSE and R for the training data are 0.91315MJ/m 2 and 0.91264MJ/m 2 respectively. The obtained result showed a good correlation between the predicted and measured solar radiation which proves ANFIS to be a good model for solar radiation prediction. http://dx.doi.org/10.4314/njt.v36i3.35

NEW MODEL FOR SOLAR RADIATION ESTIMATION FROM MEASURED AIR TEMPERATURE AND RELATIVE HUMIDITY IN NIGERIA

Solar radiation prediction is essential for effective and reliable solar power project, predicted solar radiation can be used for accurate solar energy prediction. Solar radiation measurement is not sufficient in Nigeria for various reasons such as maintenance and repair cost, calibration of instrument, and expansive of measuring device. In this paper, adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the monthly average solar radiation in Nigeria. Air temperature of monthly mean minimum temperature, maximum temperature and relative humidity obtained from Nigerian Meteorological Agency (NIMET) were used as inputs to the ANFIS model and monthly mean global solar radiation was used as out of the model. Statistical evaluation of the model was done based on root mean square error (RMSE) and correlation coefficient R to examine the accuracy of the developed model. The values of RMSE and R for the training data are 0.91315MJ/m2 and 0.91264MJ/m2 respectively. The obtained result showed a good correlation between the predicted and measured solar radiation which proves ANFIS to be a good model for solar radiation prediction.  http://dx.doi.org/10.4314/njt.v36i3.35

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.

Solar Radiation at Surface for Typical Cities in the Arid and Semi-Arid Area in Xinjiang, China Based on Satellite Observation

Xinjiang, a region of China with arid and semi-arid areas, has abundant solar incidence with 166.5×104 km2 and diverse underlying surface. The meager number of surface radiation observatories cannot meet the need for efficient exploration of solar energy. In this study we classified Xinjiang into three regions: southern Xinjiang, northern Xinjiang and Tu-Ha region and applied satellite data to provide the surface solar radiation’s temporal distribution for 10 typical cities. The study is focused on seasonal, annual and variations of all sky downward shortwave radiation flux at surface based on 24-year satellite dataset GEWEX-SRB from the WCRP/GEWEX (World Climate Research Program/Global Energy and Water Cycle Experiment) from 1984 to 2007. The results are as follows. In general, the monthly average solar radiation flux for the cities in the Tu-Ha region was the largest followed by the south Xinjiang and northern Xinjiang. The solar radiation in the most northern cities were less than 150.0 W/m2 in winter, the minimum is 138.7 W/m2, while the other cities were greater than 150.0 W/m2. The maximum of monthly solar flux for the Tu-Ha region, southern and northern Xinjiang was 400.0 W/m2.

서울시 햇빛지도 기반의 RPS제도를 고려한 옥상녹화 연계 태양광발전 시스템의 경제성 분석

In power supply systems for urban areas, issues such as a progressive tax have escalated recently. In this regard, photovoltaic power generation, which is appraised as an alternative power generation system, is drawing attention increasingly for its high stability and applicability to existing infrastructure. This study assessed the realistic feasibility of photovoltaic power generation and also analyzed the economic benefits expected when it is linked with green roof, which is likely to promote ecological functions in urban areas, based on the Seoul solar map, RPS, and actual monitoring data. The economics analysis of 30kW photovoltaic power generation applied with the monthly average horizontal solar radiation of six grades in the Seoul solar map showed that positive NPV was up to grade 4, while grade 5 or poorer showed negative NPV and indicated that it is difficult to assure appropriate feasibility. Compared with non-afforestation, when green roof was applied, monthly average power improvement efficiency was 7.2% at highest and 3.7% at lowest based on yearly actual monitoring data. The annual average was 5.3%, and the efficiency was high relatively in summer, including September and November. As for the economic benefits expected when 30kw photovoltaic power generation is combined with green roof based on the average horizontal solar radiation of grade 1 in the Seoul solar map, SP has improved 0.2 years to 7.4 years, and EP has improved 0.5 years to 8.3 years.