Solar Radiation Model Research Articles

Estimation of Diffuse Solar Radiation Models for a Tropical Site in Nigeria

Estimation of Diffuse Solar Radiation Models for a Tropical Site in Nigeria

A Quantile Functions-Based Investigation on the Characteristics of Southern African Solar Irradiation Data

Exploration of solar irradiance can greatly assist in understanding how renewable energy can be better harnessed. It helps in establishing the solar irradiance climate in a particular region for effective and efficient harvesting of solar energy. Understanding the climate provides planners, designers and investors in the solar power generation sector with critical information. However, a detailed exploration of these climatic characteristics has not yet been studied for the Southern African data. Very little exploration is being done through the use of measures of centrality only. These descriptive statistics may be misleading. As a result, we overcome limitations in the currently used deterministic models through the application of distributional modelling through quantile functions. Deterministic and stochastic elements in the data were combined and analysed simultaneously when fitting quantile distributional function models. The fitted models were then used to find population means as explorative parameters that consist of both deterministic and stochastic properties of the data. The application of QFs has been shown to be a practical tool and gives more information than approaches that focus separately on either measures of central tendency or empirical distributions. Seasonal effects were detected in the data from the whole region and can be attributed to the cyclical behaviour exhibited. Daily maximum solar irradiation is taking place within two hours of midday and monthly accumulates in summer months. Windhoek is receiving the best daily total mean, while the maximum monthly accumulated total mean is taking place in Durban. Developing separate solar irradiation models for summer and winter is highly recommended. Though robust and rigorous, quantile distributional function modelling enables exploration and understanding of all components of the behaviour of the data being studied. Therefore, a starting base for understanding Southern Africa’s solar climate was developed in this study.

Modeling and estimation of solar radiation of Karachi through artificial neural network (ANN) using temperature and dew-point

The most influential source of energy in our lives is solar energy. Solar energy reaches the earth in three different forms, i.e., Global, diffused, and Direct Solar Radiation. The Solar flux at the earth's surface depends on the intensity of these radiations and is a function of the values of latitude and longitude. The earth's temperature and hence dewpoint are greatly affected by solar flux. This idea is used for predicting solar radiation with input parameters, temperature, and dewpoint along with day number and month. The method of prediction of solar radiation used in the study is Artificial Neural Network (ANN). ANN has four variables in the input, ten neurons in the hidden layer, and three output parameters GSR. DSR and BSR. Six different types of errors, namely, Root Mean Square error (RMSE), Mean Absolute Error (MABE), Mean percent error (MAPE), Chi-square, Coefficient of Determination, Kolmogorov Smirnov, have been calculated for training, testing, and validation mode to check the accuracy of estimation. The values of all the errors are low, which indicates the prediction of solar radiation is reliable.

Choice of Solar Spectral Irradiance Model for Current and Future Remote Sensing Satellite Missions

The accuracy of surface reflectance estimation for satellite sensors using radiance-based calibrations can depend significantly on the choice of solar spectral irradiance (or solar spectrum) model used for atmospheric correction. Selecting an accurate solar spectrum model is also important for radiance-based sensor calibration and estimation of atmospheric parameters from irradiance observations. Previous research showed that Landsat 8 could be used to evaluate the quality of solar spectrum models. This paper applies the analysis using five previously evaluated and three more recent solar spectrum models using both Landsat 8 (OLI) and Landsat 9 (OLI2). The study was further extended down to 10 nm resolution and a wavelength range from Ultraviolet A (UVA) to shortwave infrared (SWIR) (370–2480 nm) using inversion of field irradiance measurements. The results using OLI and OLI2 as well as the inversion of irradiance measurements were that the more recent Chance and Kurucz (SA2010), Meftah (SOLAR-ISS) and Coddington (TSIS-1) models performed better than all of the previous models. The results were illustrated by simulating dark and bright surface reflectance signatures obtained by atmospheric correction with the different solar spectrum models. The results showed that if the SA2010 model is assumed to be the “true” solar irradiance, using the TSIS-1 or the SOLAR-ISS model will not significantly change the estimated ground reflectance. The other models differ (some to a large extent) in varying wavelength areas.

Hybrid intelligent deep learning model for solar radiation forecasting using optimal variational mode decomposition and evolutionary deep belief network - Online sequential extreme learning machine

Accurate prediction of solar radiation is of great significance to improve the utilization of solar energy for photovoltaic power generation on the roofs of urban buildings. Therefore, a hybrid model, namely the OVMD-PACF-ISSA-DBN-OSELM model, is being proposed in this study for solar radiation prediction. Firstly, the data is decomposed by optimal variational mode decomposition (OVMD) technology. Secondly, the partial autocorrelation function (PACF) technology selected features for components decomposed by OVMD technology. Thirdly, a deep belief network-online sequential extreme learning machine (DBN-OSELM) prediction model is constructed, and sparrow search algorithm (SSA) is utilized to optimize the model hyperparameters. Aiming at the shortcomings of SSA algorithm, Tent map and differential evolution strategy are introduced to improve the SSA algorithm. Finally, the model is used to predict each component, and the prediction results of each component are summarized to obtain the prediction results of solar radiation. To verify that the proposed method can obtain accurate prediction results, experiments are conducted using solar radiation data for January, May and October 2022, and compared with other models. Taking January as an example, compared with shallow model BP, the RMSE, MAE and R of the proposed model are increased by 66.83%, 58.65% and 5.33%, respectively. Moreover, the experiments of the proposed model in other months have excellent results. The current findings demonstrate that the suggested model is a strong contender in the arena of solar radiation prediction, and can consequently provide valuable support in enhancing the efficiency of photovoltaic equipment installed on urban building roofs.

Performance evaluation of seasonal solar irradiation models—case study: Karapınar town, Turkey

This paper presents the application of Particle Swarm Optimization (PSO) Algorithm, Artificial Neural Networks (ANNs) and Bagged Tree (BT) methods for forecasting seasonal solar irradiance in Karapınar town Turkey. These methods, namely ANN, PSO and BT methods are widely used for solar irradiance estimation. The study utilizes input data from 2007 to 2020, including air temperature, precipitation, snow mass, air density, and cloud cover fraction, to estimate solar irradiance as the output value. The performance of these methods is analysed according to error indicators such as mean absolute error (MAE), root mean square error (RMSE) and R2. The results demonstrate that the BT method yields the lowest statistical error metrics. Additionally, the correlation matrix reveals a strong positive linear relationship (correlation coefficient of 0.9045) between air temperature and solar irradiance. The investigation of the impact of utilizing independent inputs on the forecast output has been conducted via the employment of multiple linear regression (MLR) subsets and diverse combinations.

Quantifying Dieback in a Vulnerable Population of Eucalyptus macrorhyncha Using Remote Sensing

A disjunct population of red stringybark (Eucalyptus macrorhyncha) trees in South Australia is experiencing increasing amounts of dieback. Because the population is considered vulnerable to extinction, we investigated spatiotemporal vegetation changes, quantified the extent of dieback, and determined how topography influences dieback using aerial and satellite imagery. Classification of vegetation health status using hyperspectral aerial imagery indicated that 37% (accuracy = 0.87 Kappa) of the population was unhealthy and potentially experiencing dieback. When correlating this classification with a digital terrain model (DTM), the aspect and amount of solar radiation had the strongest relationship with the presence of unhealthy vegetation. PlanetScope satellite-derived, and spectral index-based analysis indicated that 7% of the red stringybark population experienced negative vegetation health changes during a five-year period (2017–2022), with positive vegetation health changes (9.5%) noted on pole-facing slopes. Therefore, our integrated remote sensing approach documented the extent and spatiotemporal dynamics of dieback, suggesting it could be applied in other studies. Topographical aspects exposed to high-solar radiation were particularly vulnerable to dieback, and pole-facing aspects demonstrated some recovery between droughts. The influence of topography and maps of vegetation health can be used to guide future management and restoration of the population.

Using artificial intelligence for global solar radiation modeling from meteorological variables

Long-term quantification of solar energy variables at ground level is not easily achievable in many locations. In order to overcome this limitation, use of artificial intelligence such as the application of machine learning methods is commonly used for solar irradiance prediction.In this context, this study proposes the implementation of artificial neural networks as deep learning and the XGBoost algorithm as a machine learning method for modeling the hourly global solar radiation for a humid climate such as the Rabat region. For this purpose, hourly meteorological data from the city of Rabat in Morocco are chosen in order of importance using the random forests method, for training and testing the models, namely date and time, sunshine duration, temperature, relative humidity, wind speed/direction and pressure. Subsequently, models are selected after the validation phase for testing, whose performance is evaluated using relevant statistical indicators. As a result, we retain 2 ANN and 1 XGBoost models which are eventually very close in terms of performance with a coefficient of determination value equal to 98% and 97% respectively. However, statistical indicators have proven to be effective in assessing the accuracy and fidelity of each model.Ultimately, the intent of the modeling in terms of accuracy, simplicity or fidelity is a crucial factor in the selection of the model algorithm to adopt.

3D Solar Irradiance Model for Non-Uniform Shading Environments Using Shading (Aperture) Matrix Enhanced by Local Coordinate System

Building-integrated photovoltaics (BIPVs) and vehicle-integrated photovoltaics (VIPVs) receive solar irradiance through non-uniform shading objects. Standard scalar calculations cannot accurately determine the solar irradiance of BIPV and VIPV systems. This study proposes a matrix model using an aperture matrix to accurately calculate the horizontal and vertical planes affected by non-uniform shading objects. This can be extended to the solar irradiance on a VIPV by applying a local coordinate system. The 3D model is validated by a simultaneous measurement of five orientations (roof and four sides, front, left, tail, and right) of solar irradiance on a car body. An accumulated logistic function can approximate the shading probability. Furthermore, the combined use of the 3D solar irradiance model is effective in assessing the energy performance of solar electric vehicles in various zones, including buildings, residential areas, and open spaces. Unlike standard solar energy systems, the energy yield of a VIPV is affected by the shading environment. This, in turn, is affected mainly by the location of vehicle travel or parking in the city rather than by the climate zones of the city.

Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms

This paper proposes an ensemble voting model for solar radiation forecasting based on machine learning algorithms. Several ensemble models are assessed using a simple average and a weighted average, combining the following algorithms: random forest, extreme gradient boosting, categorical boosting, and adaptive boosting. A clustering algorithm is used to group data according to the weather, and feature selection is applied to choose the most-related inputs and their past observation values. Prediction performance is evaluated by several metrics using a real-world Brazilian database, considering different prediction time horizons of up to 12 h ahead. Numerical results show the weighted average voting approach based on random forest and categorical boosting has superior performance, with an average reduction of 6% for MAE, 3% for RMSE, 16% for MAPE, and 1% for R2 when predicting one hour in advance, outperforming individual machine learning algorithms and other ensemble models.

Optimum chain desalination process design for treatment of high TDS brine: A case assessment for future treatment of extracted brine from Shenhua CO2storage site

AbstractCarbon dioxide‐enhanced water recovery (CO2‐EWR) is a promising strategy for managing reservoir pressure build‐up and mitigating the risk of fault activation resulting from CO2injection in deep saline aquifers. CO2‐EWR can also be employed for supplying the required water for different applications after a treatment stage for the produced saline water. In this study, a brief review on CO2‐EWR technology and its necessities are first carried out. After that, the feasibilities, advantages, and challenges of various available treatment technologies that can potentially be used to treat high total dissolved solids (TDS) brine are comprehensively assessed. Based on comprehensive evaluation on technologies, a chain desalination process, consisting of pretreatment, main treatment, and post treatment, is proposed as a strategic path for the treatment of high TDS brine extracted from the Shenhua CCS site. It is concluded that coagulation‐flocculation and gravity filtration are needed as primary stages to remove suspended particles, while membrane distillation (MD) is selected as a suitable main treatment technology for high TDS Shenhua brine. Then, MD treatment is comprehensively discussed for a small‐scale treatment of extracted Shenhua brine assuming that the pretreated brine is free of suspended solids. After presenting the heat and mass transfer equations for direct contact membrane distillation (DCMD), a mathematical thermodynamic model is programmed in EES (Engineering Equation Solver) software to briefly analyze the performance parameters of DCMD. The results indicate that the designed DCMD, in the absence of auxiliary systems and considering the inherent temperature of extracted brine from different formations, has the capability of producing 15.1 kg m−2hr of freshwater from the extracted brine of the Shihezi formation layer. In the case of employing the auxiliary system of flat‐plate collector (FPC) combined with heat exchanger (HX) to heat up the extracted Shenhua brine to the desired temperature of 80°C, the amounts of produced flux are enhanced by 133%, 72%, and 45% for the brine extracted from Liujiagou, Shiqianfeng, and Shihezi formations, respectively. Using the yearly solar radiation model in TRNSYS software, the maximum solar radiation on the tilted surface at the location of Shenhua project in Inner Mongolia Autonomous Region of China reaches 3800 kJ m−2hr at 1 PM on April 1. Considering maximum solar radiation on the tilted surface, it is proved that a small‐surface FPC can supply the required energy to heat up the extracted brine from its inherent temperature to the desired temperature of 80°C. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.

Neural-network modeling of solar radiation and temperature variability due to climate change in Ibadan Metropolis

his research focused on studying the variability of solar radiation and temperature under climate change in the Ibadan metropolis. In the study, spatial distribution, temporal variations, annual distribution, estimation and prediction of the solar radiation, minimum and maximum temperature data in the Ibadan Metropolis was collected. A Long Short-Term Memory Neural Network (LSTM-NN) model was developed for the prediction using the time-series data obtained. An ARIMA model was further developed to compare and validate the LSTM-NN model. The performance of the prediction models were determined using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The RMSE values for the minimum, maximum temperature and solar radiation predictions were 1.543, 1.290, 1.967, and 1.611, 1.309, 2.106 for the LSTM-NN and ARIMA models respectively, while the MAPE values for the minimum, maximum temperature and solar radiation predictions were 3.603, 4.351, 8.859, and 3.840, 4.480, 9.502 for the LSTM-NN and ARIMA models respectively. The LSTM-NN model had a better prediction performance in all categories with lower error when compared with ARIMA. From the prediction, it was observed that there will be a reduction in the maximum temperature, minimum temperature and solar radiation values when compared to obtained data. The observed minimum temperature ranged from 22.9032-23.2032(0C), while the predicted minimum temperature ranged from 19.9260- 19.977(0C) also the observed maximum temperature ranged from 32.87096-33.7064(0C), while the predicted maximum temperature ranged from 29.5159-29.5529(0C), the observed solar radiation ranged from 19.203-19.722 (W/m2 ), while the predicted solar radiation ranged from 14.123-14.115 (W/m2 ). The year with the highest solar radiation which constitutes the useful energy is 2024 with an average value of 14.1395 W/m2

Numerical investigation on the performance of a Solar Chimney Power Plant System (SCPP)

AbstractIn this work, a parametric two‐dimensional computational fluid dynamics (CFD) analysis of solar chimney power plant was performed to describe the physical mechanism in order to establish realistic predictions for the operation of such plants in different location in Algeria. The developed numerical model includes a solar radiation model within the collector; an energy storage model; an air flow and heat transfer model, and a turbine model. It is based on the solution of the Navier‐Stokes equation for turbulent flow using the standard k − ϵ model. The Boussinesq model was adopted in this simulation for the density of the fluid. The boundary conditions were adapted according to the selected sites and the effect of this on the SCPP performance was studied. The numerical simulation was performed using the commercial computational fluid dynamics (CFD) code “Ansys Fluent 19.2”.

Modification of the radiation-based Abtew reference evapotranspiration model under humid climate, Northeast India

Abstract This study aimed to improve the Abtew model for reference evapotranspiration (ET0) calculation in Northeast India using seven temperature-based solar radiation models. The temperature-based models require only air temperature as input data, which can be easily measured in most locations worldwide. The performance of the improved Abtew models (A1-A7), along with the Stephen Stewart model (SS), the Irmak model (Ir), and the modified Turc model (MT), was evaluated under the climatic conditions of Dibrugarh, Northeast India, using statistical indices such as mean absolute error (MAE), root mean square error (RMSE), standard error (SE), coefficient of correlation (r), coefficient of determination (R2), and index of agreement (D). The results showed that the seven improved Abtew models (RMSE = 0.40–0.53 mm/day; D = 0.81–0.93) outperformed the four physical models (RMSE = 0.43–2.77 mm/day; D = 0.49–0.91) for the ET0 estimate at Dibrugarh. The statistical analysis identified that the A6 model ranked highest for Dibrugarh. This study highlights the significant improvement in ET0 estimation accuracy by utilizing temperature-based solar radiation models in the Abtew model. Therefore, we strongly recommend using the A6 model to estimate ET0, which requires only temperature data as input, for estimating ET0 under the climatic conditions of Dibrugarh, Northeast India.

Solar Electric Vehicles as Energy Sources in Disaster Zones: Physical and Social Factors

Electric vehicles (EVs) have the advantage of being resilient to natural disasters. However, users hesitate to donate electricity when they lose the chance to recharge at the utility. Solar electric vehicles (SEVs) save energy through vehicle-integrated photovoltaics (VIPV) and make it possible to voluntarily donate excess energy, thus maintaining facility resilience. Given that the supply of solar energy to VIPV systems is not continuous and is difficult to forecast, the contribution of VIPV to the resilience of the larger energy system has been called into question. This is the first study in which the potential of VIPV to maintain utility resilience is investigated in the context of physical factors, such as irradiance, and social factors. The actual energy yield of a VIPV car was determined using an advanced 3D solar irradiation model under a nonuniform shading distribution, with validation from actual measures of solar irradiance on five orthogonal sides of the car body. The Monte Carlo method was used to model the complex factors in VIPV energy storage and energy donations under different scenarios. Depending on the climate, population density, and shading environment, the voluntary contribution of stored electricity in SEV is sufficient to provide disaster relief support.

An efficient model for solar radiation induced top displacement of steel-concrete composite bridge towers

The temperature effect caused by solar radiation significantly influences the stress state of bridges. Especially for ultra-high bridge towers, considerable deformation induced by solar radiation affects the safety of the structures. This research is conducted to evaluate the temperature deformation of a steel–concrete composite bridge tower where a sliding saddle is adopted. First, the temperature distribution pattern of the structure is analyzed. Second, based on the temperature distribution, an efficient model which can be applied to the long-term calculation is established, aiming at directly calculating the deformation results. Then, an outdoor experiment study is carried out to verify the temperature distribution and the results of sectional curvature. Finally, the efficient model is applied to calculate the annual bidirectional deformation. This method realizes the long-term temperature effect evaluation of the structure before construction, and the results can be referred to as guidance for the analysis of related types of structures.

The Application of VECM (Vector Error Correction Model) for Rainfall Modeling in Manokwari Regency

This study aims to determine the modeling of rainfall in Manokwari Regency with a Vector Error Correction Model (VECM) based on the period January 2012 to December 2017. In the modeling used temperature, humidity, and solar irradiation models. The research begins by looking at data stationary, if the data is not stationary, a differencing stage will be carried out to station the data. Then an optimal lag is determined and then a cointegration test is performed to determine whether there is a long-term relationship between variables on rainfall as a condition to proceed to VECM modeling. Based on VECM (1) obtained there are two relationships to changes in rainfall, namely the long-term and short-term relationships. In the long term relationship, changes in rainfall are only influenced by changes in the irradiation period of 1 (one) previous period. In the long term relationship, changes in rainfall are influenced by rainfall and irradiation period of 1 (one) previous period. In the short term relationship, changes in rainfall are only affected by changes in the irradiation period of 1 (one) previous period. When compared to ARIMA (1, 0, 2), VECM (1) can predict rainfall better because it has validation values smaller models than ARIMA (1, 0, 2).

Novel stair-shaped ground absorber for performance enhancement of solar chimney power plant

The present study investigates the use of a stair-shaped ground surface of a chimney-based solar thermal power plant (SCPP). The stair-shaped absorber is implemented to augment the efficacy of SCPP. The current investigation is conducted with a finite volume method-based solver, considering turbulent flow and solar irradiation models. Thermo-hydraulic performance, power generation, and irreversibility production of the plant are analyzed for different dimensions of the stair-shaped absorber. The present computing model is validated against the Manzaranes pilot plant with good agreement. The analysis indicates that the proposed stair-shaped absorber can improve power generation by up to ∼ 80% (compared to a flat ground absorber surface). The power generation of a classical plant can be enhanced from 51 to 92 kW. The sloped ground absorber (a limiting case with infinite steps) generates 82 kW power, which is ∼ 19% less than the stepped ground absorber surface. The role of geometric dimensions of the stair-shaped absorber is systematically examined. It reveals an increasing central height or number of steps of the absorber improves overall plant efficiency. However, the net exergy efficiency does not increase significantly with stair height, despite the rise in chimney exergy efficiency. Mathematical correlations for predicting power generation and different efficiencies are also developed. Thus, this work provides comprehensive insights into the potential of a stair-shaped absorber, and the findings suggest that the proposed ground absorber of a stair shape could be an alternative solution for improving power generation in classical SCPPs.

Theoretical study on the optical properties of a phase change material blind in a double skin facade system

The use of a phase change material (PCM) blind system has great potential in mitigating overheating issues in double skin facade (DSF) systems while maintaining their optical and thermal benefits. However, there is a lack of research information available regarding the optical properties of such systems. This paper establishes a solar radiation model of PCM blinds for use in a DSF system that integrates the optical path of sunlight. The influences of the solar incidence angle, slat inclination angle, the ratio of slat distance to slat width, and slat surface material on the optical coefficient of the blind system are analyzed. The results indicate slat inclination angle significantly affects diffuse absorptance and diffuse transmittance, while solar incidence angle has little impact. Diffuse absorptance gradually increases with increasing slat inclination angle, reaching a peak at 90°, while diffuse transmittance decreases. The optical coefficient of the blind is closely related to the ratio of slat distance to slat width. The smaller the slat inclination angle, the more noticeable the difference in absorptance or transmittance of direct/diffuse solar radiation. The reflectance of the blind surface material has a significant effect on the optical coefficient. As the reflectance of the slat surface material increases, both direct absorptance and diffuse absorptance of the slat show a decreasing trend. Our results suggest that this method could be used for optical properties measurement in PCM blind system. The results of this study provide effective references for the final selection of PCM and the estimation of component dosage.

The spatial distribution of China's solar energy resources and the optimum tilt angle and power generation potential of PV systems

This study aims at filling the gaps and completing the missing global solar radiation and diffuse solar radiation data considering the limited radiation data of 85 meteorological observation stations and the routine meteorological data of 2001 meteorological observation stations. Employing the Inverse Distance Weighing model, the annual and seasonal spatial distributions of global solar radiation and diffuse solar radiation are obtained. A calculation method of the optimum tilt angle of photovoltaic systems for optimal utilization is established using the Collares-Pereira and Rabl hourly solar radiation model and the solar radiation on sloped surfaces model. The spatial distribution of the optimum tilt angle is obtained using the proposed method. The simplified calculation method of the optimum tilt angle based on latitude and altitude is established by introducing two factors of latitude and altitude. In addition, the annual and seasonal photovoltaic power of China is calculated, and the spatial distribution of China's solar resource utilization potential is obtained using the calculated optimum tilt angle, solar radiation data on sloped surfaces, and the photovoltaic power model. The results show that the annual global solar radiation in China is in the range of 3097–7311 MJm−2, and the annual diffuse solar radiation value ranges from 495 to 3036 MJm−2. Moreover, the optimum tilt angle value in different regions of China is found to range between 14.5°and 49.1°, and the theoretical optimum tilt angle value generally aligns with the law of angle increasing with latitude. The annual photovoltaic power generation is between 117 kWhm−2 and 483 kWhm−2. Compared with the solar energy utilization potential of a PV placed on the horizontal surface, the annual average power generation of a PV panel placed at the optimum tilt angle can increase by up to 144.76 kWhm−2, with an average increase of 10.41%.