Estimation Of Solar Radiation Research Articles

Prediction of Solar Radiation Using Data Driven Models

The local climatic conditions play a significant role in the amount of Solar Radiation that reaches the Earth’s surface. In India, most of the locations, receive ample Solar Radiation. The exact evaluation of SR is most essential for engineering studies on energy and climate. For the present investigations, the meteorological data for Hyderabad city in Telangana state for the period 2009 - 2020 is adopted. Five climatological variables viz., Max. Temp. (Tmax), Min. Temp. (Tmin) and Mean Air Temp. (Tmean), Sunshine hours (n), and Relative Humidity (RH) were collected from the weather forecast. In the present investigations, two conventional empirical equations viz., Abdalla, and Angstrom are adopted for analytical estimation of Solar Radiation (SR). Furthermore, two data-driven models via., Artificial Neural Networks (ANN) and Multi Gene-genetic Programming (MGGP) are used to develop an equation for estimating Solar Radiation (Rs). The model equations were developed based on the parameters adopted for each conventional equation. These results are compared with conventional equation results. The present investigation exhibited that, the expected results of the Solar radiation of the arrangement formed by the data are favourable and also simplify the testing data. The corresponding R2 values for random data are 0.976 for the training dataset, while the value for the testing dataset is 0.964. Based on the performance analysis for each equation, the efficiency and reliability of the proposed MGGP and ANN model are validated and predicted for the year 2021. The predicted results were found to be in good agreement with the analytical results evaluated from three properly used equations.

Modeling and Simulation of Hourly Irradiance for Solar Applications in Chad: Case of the City of Abeche

In this paper, the general objective is to model and simulate the hourly irradiance followed by an assessment of the solar potential in the city of Abeche located in the climatic region of the Republic of Chad. Unlike previous work carried out in the study area on solar irradiance, two hourly solar irradiance estimation models (Capderou and Liu&Jordan) were simulated at varying tilt angles. An analysis of the quality of their estimation is carried out through statistical methods (MBD, RMSD, MAD) of error calculation. The results show that the first model is well adapted on a horizontal surface to estimate the solar radiation and also during the dry season when the surface is inclined. On the other hand, the second model is preferred during the rainy season when the surface is inclined. The solar energy over the entire area of Abeche is estimated at 427 GWh on a horizontal surface in January and 504 GWh for the month of August on a tilted surface. This can be exploited by all solar technologies or applications.

Estimation of global solar radiation using sunshine-based models in Ethiopia

Estimation of global solar radiation using sunshine-based models in Ethiopia

Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction

As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

Design of LED lighting system using solar powered PV cells for a proposed business complex

The paper outlines the concepts and design of an upcoming stand-alone solar photovoltaic system to supply the energy needs of a new proposed business complex. The purpose of this study is to develop a prediction method for the use of solar energy for commercial purposes. Firstly, the annual energy demand for the illumination and utilities in a business complex is calculated. LED lights are considered in the complex for the illumination to minimize the cost of energy. Based on the load estimation, the number of solar panels are predicted as 6097, to generate the power for the proposed area. Secondly, the important part of the design in the estimation of solar radiation and optimal tilt angle of a photovoltaic panel has been calculated for the maximum energy harvest. In this case the optimal tilt angle is 49.34°. The installation of PV panels for optimal and feasible operation is also predicted. The calculated parameters are used in a simulation with a software to test their practicality in the business complex. The technique is used to determine the amount of energy produced and system’s performance ratio. A cost estimate is also provided for the solar PV system. In the end, an analysis of these simulations and estimations is presented.

Estimation of monthly global solar radiation over twelve major cities of Libya

This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.

DEVELOPMENT OF SUNSHINE BASED MODELS FOR ESTIMATING GLOBAL SOLAR RADIATION OVER KANO AND IKEJA, NIGERIA

In this present study, three (3) new sunshine based models were developed and evaluated using observed monthly average daily global solar radiation and sunshine hours meteorological variables spanning through thirty one years (1980-2010). The estimated global solar radiation values obtained from the empirical regression models for the developed models and nine (9) distinctive existing sunshine based models were compared through validation indicators of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE), Index of Agreement (IA) and coefficient of determination (R2) allowing the identification and recommendation of the most accurate model suitable for the estimation of global solar radiation in Kano and Ikeja located in the Sahelian and Coastal regions respectively. The results in this present study revealed that the developed exponent exponential and quadratic latitude 2 sunshine based models were found more appropriate for estimation of global solar radiation in Kano (Latitude 12.05 and Longitude 8.20 ) and Ikeja (Latitude 6.58 and Longitude 3.33 ) respectively. The evaluated existing linear (1st order) and quadratic (2nd order) sunshine based models for Kano were found more suitable for the estimation of global solar radiation as compared with those found through literature survey. The empirical regression equations and the values of the validation indicators obtained in this study for each model varies significantly in each of the regions under investigation indicating that the twelve (12) sunshine based models are site – specific. The correlation between the

New system designed for monitoring of solar radiation in urban areas

AbstractThis study describes the estimation of solar radiation from photovoltaic (PV) power generation in smart streetlights (SSL) with a heat transfer model. The distribution of solar radiation is important for managing PV power generation. In particular, the demand for electricity for air conditioners and lighting in urban areas is large, so it is a major target for reducing greenhouse gases by introducing PV power. Solar radiation data that includes information on building shadows and reflections can be obtained by high‐density sampling in urban areas using SSL. These data, which include the three‐dimensional effects of urban structures such as buildings, can help urban planning to incorporate PV power generation. Diverse monitoring data in addition to urban canopy top solar radiation data will accelerate the introduction of PV power. The thermal state of the PV panel is an uncertain factor that links solar radiation and PV power generation. Here, we investigate an hourly constant model, a steady‐state model, and two nonsteady state models with daily and monthly intervals for predicting solar radiation from PV power generation at two locations over one winter month. Solar radiation is estimated by evaluating solar cell temperature, except for the hourly constant model. For the nonsteady‐state model, two types of heat transfer model, which consider only wind speed or both wind speed and direction for convection, are used. The model that includes wind speed and direction calculated by a Joukowski transform is effective, and the highest model accuracy is a mean absolute percentage error of less than 6.3% for the monthly average.

Estimation of Daily Average Global Solar Radiation with Nonlinear Regression Models Developed Using Some Meteorological and Geographical Parameters

In this study, it is aimed to develop empirical models that can be used in estimation of daily average solar radiation (RS) based on some meteorological and geographical parameters. Seven estimation models were developed by nonlinear regression analysis method using various combinations of air temperature (T), relative humidity (RH), extraterrestrial radiation (Ra), saturated (es) and actual vapour pressure (ea) parameters. The models were created using the long-term average daily meteorological data of Kahramanmaraş province (1938 – 2020). The models were tested both these long-term average data and daily meteorological data measured at Kahramanmaraş Sütçü İmam University (KSU) in 2019 and 2020. Long-term average daily actual RS data varied between 4.99 – 32.56 MJ m-2 day-1. The estimated solar radiation values (("RS" ) ̂) with the highest correlation (r = 0.99) with actual RS data were obtained with the RS_7 model, in which the parameters es, ea, T, RH and Ra were used together. The ("RS" ) ̂ values obtained using this model varied between 6.45 to 33.99 MJ m-2 day-1. For the RS_7, which showed the best performance among the seven models, mean absolute percentage error (MAPE) and root mean square error (RMSE) were determined as 4.17% and 0.69 MJ m-2 day-1, respectively. The daily RS values measured in KSU varied between 7.75 – 33.48 MJ m-2 day-1 and 10.51 – 30.23 MJ m-2 day-1 for 2019 and 2020. The ("RS" ) ̂ values closest to the measured RS values were estimated with the RS_7 model. The estimated ("RS" ) ̂ values by this model varied between 11.74 – 33.93 MJ m-2 day-1 and 13.93 – 31.57 MJ m-2 day-1 for 2019 and 2020, respectively. MAPE values were determined as 11.33% and 7.54%, respectively. It is concluded that this model can be used to estimates daily average solar radiation and will be an excellent alternative since it is compatible with the Kahramanmaraş conditions.

Mapping of solar energy potential in Fiji using an artificial neural network approach

The concerned stakeholders have been pursuing renewable energy seriously due to its overwhelming benefits. Countries that receive less solar radiation are not lagging behind as they are working to optimize the available radiation let alone of countries that receive sufficient solar radiation over long durations such as Fiji. In view of the abundancy of this energy in Fiji, the country has been working intensely on tapping the full potential of this energy, thus proposed that by 2030; more than 50% of its energy will come from renewable energy. The accurate estimation of global solar radiation determines the reliability of performance evaluation of solar energy systems. Therefore, the key interest of this study is in respect of accurate mapping of solar radiation to aid reliable solar energy design especially in siting and sizing of photovoltaic power systems. In the light of this, this work modelled solar radiation on the earth of Fiji from common meteorological and geographical data in all locations in Fiji using Artificial Neural Networks (ANN). There are different configurations of ANN but in this study, Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) were selected as the learning algorithms due to the data size, speed of computation and the success of these algorithms in solar radiation modelling. Similarly, a tangent sigmoid transfer function was used in the network. In total, twelve different configurations of ANN were considered and the best configuration was selected to predict the solar radiation potential in Fiji. Since ANN requires input data to train the network, meteorological data covering 36 years (1984–2019) and geographical data from NASA database were supplied to the network. All the locations considered were distributed evenly throughout Fiji and thus covered all the four regions and 14 provinces in Fiji. The geographical and meteorological data used to train the network are month, latitude, longitude, altitude, mean temperature, relative humidity, precipitation and solar radiation. The mean squared error of 0.118838 and correlation coefficient of 0.9402 were obtained between the ANN predicted and measured solar radiation for the entire dataset. These correlation coefficients and mean squared error showed that ANN model of solar radiation in Fiji is satisfactory and thus can be used as an alternative where solar radiation data are not available. Similarly, the network produced satisfactory solar radiation result for the locations where there are no solar radiation data. To ease solar radiation assessment of all places in Fiji, the iso-lines of the solar radiation were presented in the form of monthly maps. It is believed that this prediction will aid energy stakeholders in making best decision concerning solar energy potential in Fiji thus boosting optimal utilization of the scarce resource.

Optimal Strategy on Radiation Estimation for Calculating Universal Thermal Climate Index in Tourism Cities of China.

The Universal Thermal Climate Index (UTCI) is believed to be a very powerful tool for providing information on human thermal perception in the domain of public health, but the solar radiation as an input variable is difficult to access. Thus, this study aimed to explore the optimal strategy on estimation of solar radiation to increase the accuracy in UTCI calculation, and to identify the spatial and temporal variation in UTCI over China. With daily meteorological data collected in 35 tourism cities in China from 1961 to 2020, two sunshine-based Angstrom and Ogelman models, and two temperature-based Bristow and Hargreaves models, together with neural network and support vector machine-learning methods, were tested against radiation measurements. The results indicated that temperature-based models performed the worst with the lowest NSE and highest RMSE. The machine-learning methods performed better in calibration, but the predictive ability decreased significantly in validation due to big data requirements. In contrast, the sunshine-based Angstrom model performed best with high NSE (Nash–Sutcliffe Efficiency) of 0.84 and low RMSE (Root Mean Square Error) of 35.4 J/m2 s in validation, which resulted in a small RMSE of about 1.2 °C in UTCI calculation. Thus, Angstrom model was selected as the optimal strategy on radiation estimation for UTCI calculation over China. The spatial distribution of UTCI showed that days under no thermal stress were high in tourism cities in central China within a range from 135 to 225 days, while the largest values occurred in Kunming and Lijiang in southwest China. In addition, days under no thermal stress during a year have decreased in most tourism cities of China, which could be attributed to the asymmetric changes in significant decrease in frost days and slightly increase in hot days. However, days under no thermal stress in summer time have indeed decreased, accompanying with increasing days under strong stress, especially in the developed regions such as Yangze River Delta and Zhujiang River Delta. Based on the study, we conclude that UTCI can successfully depict the overall spatial distribution and temporal change of the thermal environments in the tourism cities over China, and can be recommend as an efficient index in the operational services for assessing and predicting thermal perception for public health. However, extreme cold and heat stress in the tourism cities of China were not revealed by UTCI due to mismatch of the daily UTCI with category at hourly scale, which makes it an urgent task to redefine category at daily scale in the next research work.

Estimation of Solar Radiation on a PV Panel Surface with an Optimal Tilt Angle Using Electric Charged Particles Optimization

Solar energy is a promising renewable energy source that can fulfill the world’s current and future energy needs. The angle at which a photovoltaic (PV) panel faces the horizon determines the incidence of solar radiation. The incident solar radiation on PV panels could be optimized by adjusting their tilt angles and increasing the power output of the PV array. In this study, solar energy model-based research was conducted in the Saudi Arabian cities of Dhahran and Makkah. This study investigated the performance of a 1 kW monocrystalline silicon PV array in these cities. Analyzing the optimal tilt angle for efficiency and performance improvement of the PV panel is challenging. The optimal tilt angle is determined by combining the data of the Sun’s diffuse, direct radiation and the global horizontal Sun radiation. This research examined the four empirical models by applying the electric charged particle optimization (ECPO) algorithm to estimate the solar radiation on sloped surfaces. The model’s results were compared to the global horizontal solar radiation based on the daily mean solar radiation value in these cities. The Hay–Davies–Klucher–Reindel model presented the maximum amount of tilted surface solar radiation in the year and at different periods. In contrast, the Badescu model exhibited the weakest results of all the isotropic and anisotropic models. Finally, using the ECPO algorithm, all models indicated that tilted surfaces (IT) received more solar radiation than horizontal surfaces (Ig).

Hybrids of machine learning techniques and wavelet regression for estimation of daily solar radiation

Hybrids of machine learning techniques and wavelet regression for estimation of daily solar radiation

Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations

Daily global solar radiation (H) is practically significant for human production and life, especially for solar power generation. Due to the high construction and maintenance cost of solar radiation observation equipment, solar radiation measurement cannot be easily obtained at many sites. In addition, the H estimation fitting model cannot be directly trained in the absence of site-specific historical data of H. Therefore, in this study, the H estimation modeling method is proposed specifically for the sites that cannot afford to install solar radiation measurement equipment. The method includes a novel coding method based on information gain, Pearson correlation coefficient, and principal component analysis (PCA), which analyzes the nonlinear and high-dimensional correlation between meteorological factors and solar radiation to decide the correlation of adjacent sites. Based on the results of the coding method, a hybrid H estimation model combining empirical and machine learning is proposed, which takes the empirical model as the base model, and the machine learning model adaptively assigns the corresponding weights to estimate H. The case studies show the proposed hybrid model outperforms the benchmark models and indicate that the H estimation modeling method can be applied to different regions without solar radiation measurement.

Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper.

Estimation of Daily Average Shortwave Solar Radiation under Clear-Sky Conditions by the Spatial Downscaling and Temporal Extrapolation of Satellite Products in Mountainous Areas

The downward surface shortwave radiation (DSSR) received by an inclined surface can be estimated accurately based on the mountain radiation transfer model by using the digital elevation model (DEM) and high-resolution optical remote sensing images. However, it is still challenging to obtain the high-resolution daily average DSSR affected by the atmosphere and local topography in mountain areas. In this study, the spatial downscaling and temporal extrapolation methods were explored separately to estimate the high-resolution daily average DSSR under clear-sky conditions based on Himawari-8, Sentinel-2 satellite radiation products and DEM data. The upper and middle reaches of the Heihe River Basin (UM-HRB) and the Laohugou area of Qilian Mountain (LGH) were used as the study areas because there are many ground observation stations in the UM-HRB that are convenient for DSSR spatial downscaling studies and the high-resolution instantaneous DSSR datasets published for the LHG are helpful for DSSR temporal extrapolation studies. The verification results show that both methods of spatial downscaling and temporal extrapolation can effectively estimate the daily average DSSR. A total of 3002 measurements from six observation sites showed that the 50 m downscaled results of the Himawari-8 10-min 5 km radiation products had quite a high correlation with the ground-based measurements from the UM-HRB. The coefficient of determination (R2) exceeded 0.96. The mean bias error (MBE) and the root-mean-squared error (RMSE) were about 41.57 W/m2 (or 8.22%) and 49.25 W/m2 (or 9.73%), respectively. The fifty-two measurements from two stations in the LHG indicated that the temporal extrapolated results of the Sentinel-2 10 m instantaneous DSSR datasets published previously performed well, giving R2, MBE, and RMSE values of 0.65, 41.06 W/m2 (or 7.89%) and 88.90 W/m2 (or 17.07%), respectively. By comparing the estimation results of the two methods in the LHG, it was found that although the temporal extrapolation method of instantaneous high-resolution radiation products can more finely describe the spatial heterogeneity of solar radiation in complex terrain areas, the overall accuracy is lower than that achieved with the spatial downscaling approach.

Energy Transition in Marginalized Urban Areas: The Case of Romania

To achieve a succesful energy transition, the collaboration of all stakeholders and policies for decarbonization at all levels are required. In this context, we aim to assess the perspective of energy transition in marginalized urban areas in Romania. The research is based on findings from a consistent analysis of 37 local development strategies in Romania in terms of energy (electricity and heating service provision). Despite the issues identified, we detected that communities across the country showed limited interest in renewable energy, which may be linked to low levels of awareness and education of residents in marginalized communities. In parallel with the analysis of the strategies, we developed Romania’s maps for renewable solar resources. Then, we developed maps of energy potential by linking the map of marginalized communities with the map of renewable energy resource potential in Romania. In addition, we used the PVGIS solar radiation tool to generate results related to median solar radiation estimates. Finally, we concluded by proposing an awareness raising and education program for the local communities on the solar energy solutions and a roadmap for potential energy projects in marginalized urban areas.

Simplified estimation modeling of land surface solar irradiation: A comparative study in Australia and China

Solar irradiation maps are fundamental geospatial datasets that have been used in a variety of research fields. However, it is difficult to estimate the continuous distribution of solar irradiation over large areas accurately by using conventional interpolation or extrapolation methods based on only a few observation stations. To tackle this problem, this study proposed a method to estimate spatially continuous land surface solar irradiation based on four machine learning models, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP). Clear-sky solar irradiation data were computed based on time and location, cloud optical thickness (COT) and aerosol optical thickness (AOT) that significantly influence solar irradiation were retrieved from Himawari-8 meteorological satellite images, and land surface solar irradiation data were obtained from observation stations for training and evaluation. To explore the weather effects on land surface solar irradiation, air temperatures, humidity, wind, and atmospheric pressure were also quantified and integrated into the models. As a comparative study, this study collected six-year historical data and estimated solar distribution at a 5-km spatial resolution in Australia and China. Based on the coefficient of determination (R2), normalized Root Mean Square Error (nRMSE), normalized mean bias error (nMBE), and consumption of time (t), the results show that GBM achieved the highest accuracy with R2>0.7 at all stations, followed by RF, SVR, and MLP. It suggests that the proposed method can provide an accurate and reliable estimation of land surface solar irradiation, compared with the theoretical solar irradiation without the obstacle of the atmosphere. The annual solar distribution maps created by the built methods indicate that the proposed method is simple and effective for large geographical regions and can be used worldwide when similar datasets are obtained.

Machine Learning for Solar Resource Assessment Using Satellite Images

Understanding solar energy has become crucial for the development of modern societies. For this reason, significant effort has been placed on building models of solar resource assessment. Here, we analyzed satellite imagery and solar radiation data of three years (2012, 2013, and 2014) to build seven predictive models of the solar energy obtained at different altitudes above sea level. The performance of four machine learning algorithms was evaluated using four evaluation metrics, MBE, R2, RMSE, and MAPE. Random Forest showed the best performance in the model with data obtained at altitudes below 800 m.a.s.l. The results achieved by the algorithm were: 4.89, 0.82, 107.25, and 41.08%, respectively. In general, the differences in the results of the machine learning algorithms in the different models were not very significant; however, the results provide evidence showing that the estimation of solar radiation from satellite images anywhere on the planet is feasible.

Developing a new model in solar radiation estimation with genetic algorithm method

AbstractIn this study, a new model was developed to estimate solar radiation using the genetic algorithm method in the MATLAB program. When the literature was reviewed, it is observed that there are a lot of different methods used to estimate solar radiation. In the literature review, it was seen that the genetic algorithm approach was not used in solar radiation estimation before. In this study, the coefficients of the Angstrom equation were calculated through a genetic algorithm by using the 5‐year solar radiation data of Adana province, and accordingly, a new model was developed. The suggested model was compared to various models published in the literature for determining the solar radiation density of the given region. It was evaluated that the new developed model using a genetic algorithm has acceptable performance for estimating solar radiation.