Estimation Of Solar Radiation Research Articles

Water balance estimation for an ordinary solid waste sanitary landfill in Costa Rica

To assess the necessity of treatment systems for leachate generated by ordinary solid waste deposited in a sanitary fill, accurate estimation of the leachate and gas amount produced during biodegradation process is essential. Climatic conditions at the site significantly influence leachate generation; therefore, accurate estimation of rainfall, temperature, solar radiation, and infiltration into the fill is imperative. In this study, we analyzed the water balance of the Miramar landfill in Costa Rica, assessed the amount of leachate produced using various methodologies, and then compared these findings with the field data to identify the best methodology for specific environmental conditions. To comprehensively analyze landfill behavior, as well as the leachate and gases generated from waste decomposition, determining the hydraulic properties of the materials inside the cells is necessary. These properties must consider variables such as the types of materials present at the site, age of the ordinary waste, and their densities. The hydraulic properties of the materials were obtained from various laboratory tests, which included determining the soil–water characteristic curve necessary for assessing unsaturated conditions in the solid waste. These properties are compared with the results of studies conducted in other landfills to validate the current methodology and the obtained results, which can be used for conducting studies on leachate generation or slope stability analysis for sanitary landfill slopes. The amount of leachate generated was estimated using the methodology proposed by Delgado A. (2018). Additionally, the Hydrologic Evaluation of Landfill Performance model (HELP V 4.0) was employed, demonstrating superior estimation compared to recorded field data. The HELP V 4.0 model facilitated the incorporation of different material layers and thicknesses, weather information at the site, slopes, and drainage conditions.

Estimation of Spectral Solar Irradiance in the Ecliptic Plane Using Synthetic Solar Surface Magnetograms

AbstractThe primary external energy input to planetary atmospheres comes from solar radiation. Accurate estimates of solar irradiance are needed in order to understand atmospheric conditions at other planets. Many studies have interpolated Earth‐based irradiance measurements at other planets, disregarding the evolution of solar features, which are responsible for the irradiance variability. We present an approach for assessing solar irradiance at other planets by using synthetic full surface magnetograms of the Sun, generated by the Surface Flux Transport Model, and then converted into sunspots and faculae areas. Following the Spectral And Total Irradiance REconstruction approach, we compute the irradiance as the sum of contributions from sunspots, faculae, and the quiet Sun. We calculate the S‐index, which is a proxy for near‐ultraviolet irradiance, and the total solar irradiance (TSI), which is the wavelength‐integrated value. We demonstrate that a simple phase shift of the Earth‐observed TSI does not produce reliable estimates in the ecliptic. By comparing and correlating our results with the interpolated measurements, we find that the two models agree in the S‐index variability, because faculae have a long lifetime, so the effect of the solar rotation becomes less important. Conversely, the methods disagree strongly in the TSI, since sunspots have a short lifetime, and the interpolation does not take into account their emergence and evolution on the far‐side of the Sun, as it only uses the information of the disc visible for an Earth‐bound observer.

EVALUATION OF NEW DEVELOPED MODELS FOR ESTIMATION OF DIFFUSE SOLAR RADIATION OVER IKEJA, LAGOS STATE, NIGERIA

This study investigates the estimation of diffuse solar radiation in Ikeja, Nigeria (6.58°N, 3.33°E, 39.40m above sea level). Employing a 22-year dataset (2001-2022) of monthly average climatic data from NASA website, nineteen new models were developed to predict diffuse solar radiation. These models incorporated various meteorological parameters including global solar radiation, wind speed, temperature, pressure, and relative humidity. A categorized approach was used, with models falling into five groups: modified Page, Liu and Jordan; clearness index with one additional variable; two-variable; three-variable; and a four-variable model. Evaluation using five statistical tests tools of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, and Coefficient of determination (R²) identified Equation 28h (the model relating diffuse radiation to clearness index and temperature) as the most suitable model out of all the models in each of the five categories for estimation of diffuse solar radiation for Ikeja. Hence, emphasizing the importance of considering both atmospheric clarity and temperature for accurate diffuse solar radiation estimation in this coastal location.

Deep learning approach for one-hour ahead forecasting of solar radiation in different climate regions

ABSTRACT The accurate prediction of hourly global solar radiation is critical to solar energy conversion systems selecting appropriate provinces, and even future investment policies. With this viewpoint, the aim of this study is to predict one hour ahead the global solar radiation of six provinces that have different climate regions in Turkey. A deep learning technique based on the LSTM neural network is used for this purpose. To see the success of this proposed model, the deep learning technique GRU, a machine-learning approach ANFIS with FCM, and standard statistical models ARMA, ARIMA, and, SARIMA are also applied. Forecasting models developed to estimate the future values of hourly global solar radiation are based on past time series data. The study discusses four different statistical metrics (MAE, RMSE, NSE, and, R) to determine the success of these algorithms. The results indicate that the R, MAE, NSE, and, RMSE values of the two approaches range from 0.9352 to 0.9806, from 29.0737 to 59.4840 Wh/m2, from 0.6445 to 0.9686 and from 57.4492 to 94.3287 Wh/m2, respectively. The results revealed that the methods used in the study performed satisfactorily in terms of hourly solar radiation estimation, but the LSTM method performed better.

Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example

This research examines the estimation of solar radiation using artificial neural network (ANN) models in Turkish cities with similar latitude values such as Ankara, Sivas and Erzurum. The aim of this study is to investigate whether cities at similar latitudes exhibit similar trends in solar radiation values, despite their geographical differences. In this study, solar radiation prediction was made for 3 cities with a single-layer neural network. Monthly solar radiation intensity was estimated for the 10-year period between 2012 and 2022 with a total of 4764 samples taken from the General Directorate of State Meteorology. An artificial neural network model was developed with 8 units in the first hidden layer and 4 units in the second hidden layer. The optimizer used in compiling the model was determined as Adam, the loss function as 'mean_squared_error' and the metric as 'mse'. ReLU activation function was used in the input layer and hidden layers. A 10-year solar radiation intensity value was used in the output layer. 70% of the data set is reserved for training and 30% for testing data set. As a result, similar solar radiation trends were obtained in the same latitude regions, the results were confirmed by meteorological data.

Comparative analysis of single and hybrid machine learning models for daily solar radiation

This study investigates the estimation of daily solar radiation (SR) through various machine learning (ML) models, including the k-nearest neighbor algorithm (KNN), support vector regression (SVR), and random forest (RF), both individually and in combination with the wavelet transform (WT). The assessment of these models is based on meteorological data spanning three decades (1981–2010) from the province of Kütahya in Türkiye. To address the inherent uncertainty in these data-driven models, the quantile regression method is employed for uncertainty analysis. Statistical metrics, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), mean prediction interval (MPI), and prediction interval coverage probability (PICP), are utilized to evaluate the effectiveness and uncertainties of the models. The SVR and KNN models exhibit comparable performances concerning both predictive accuracy and uncertainty levels. However, hybrid models, such as KNN-WT, RF-WT, and SVR-WT, display enhanced accuracy compared to individual ML models, as indicated by statistical performance criteria. Notably, the SVR-WT model, incorporating inputs such as sunshine duration, air temperature, wind speed, and relative humidity, outperforms other models in terms of RMSE (2.174 MJ/m2), MAE (1.721 MJ/m2), R2 (0.923), MPI (28.55), and PICP (0.80) for the testing dataset. In conclusion, the integration of WT significantly improves the performance of ML models, providing valuable insights for the design and operation of solar energy systems, where precise daily SR estimation is critical for optimal and cost-efficient operation.

Using of the Weibull distribution in developing global solar radiation forecasting models

AbstractIn this study, a solar radiation prediction model was developed using the Weibull distribution function (WDF). The main purpose of this study is to develop a new model for solar radiation (SR) estimation using the WDF and to bring this model to the literature. Although the WDF is widely used in wind energy forecasting due to its compatibility with wind speed data, it has not been used before in solar radiation forecasting model development. In this study, it is aimed to make SR estimation with the WDF for the first time. SR data for all regions where this new model was developed and its performance was examined were obtained from the General Directorate of Meteorology. In order to decide the success of these developed models, five different statistical metrics (RPE, MPE, MAPE, SSRE, and t‐stat) were discussed in the study. When the results are evaluated in general, it is seen that the new developed model has an acceptable performance. According to all test results in the region where the model was developed, it is seen that the new developed model showed the best performance with 0.0353, 0.4068, 7.1851, 0.1144, and 0.0162 values, respectively. The performance of this new developed model was observed in three different regions. When the statistical test results in these regions were examined, it was seen that the new model developed had a similar performance to the popular solar forecasting models widely used in the literature.

New independent daily global solar radiation estimation models based on the day number of the year

ABSTRACT Solar radiation data are necessary information for the analysis and prediction of certain environmental processes and the planning of related energy projects. Many models have been proposed in the literature for their estimation due to their unavailability in some locations, especially in developing countries. However, peoples are currently finding more accurate and simple models. One type of these models is the day of the year-based (DYB) model, which has as merit over others, the fact that it does not depend on any environmental parameter. In this work, nine existing DYB models are calibrated with Levenberg–Marquardt nonlinear curve fitting technique for the estimation of solar radiation in the regional cities of Cameroon. In addition, two new DYB models are proposed. The results show that one of the two proposed models (M11) best estimates the solar radiation in nine sites and is ranked second in one site with the root mean square error in the range [0.2365; 0.3039], the mean absolute percentage error in the range [0.1873; 0.2348], and the correlation coefficient in the range [0.8332; 0.9164]. The new model M11 is therefore recommended for estimating solar radiation of some sites of Cameroon and in regions with similar climate.

Study on the impact of valley orientation on solar radiation shading of roofs in western Sichuan

Solar energy is a commonly used renewable energy source in buildings. In the high-altitude mountainous regions of western Sichuan, solar energy resources are abundant, but the complex and variable terrain significantly affects the shading of solar radiation on building roofs. In the mountainous regions of western Sichuan, towns located in valley terrain are the most prevalent, accounting for as much as 42.14%. Therefore, based on the mountainous solar radiation estimation method, this study estimates the direct radiation shading rate and diffuse radiation shading rate for building roofs in the valley towns of western Sichuan. The research delves into the analysis of the impact of valley terrain characteristics on the shading of solar radiation on building roofs. The study reveals that, for a specific valley town, the orientation of the valley has a more significant impact on the shading of direct radiation compared to its impact on diffuse radiation. When the town's orientation approaches an east-west direction, both direct and diffuse radiations are less affected by terrain shading.

A novel soft sensing method using intelligent modeling method for solar irradiance and temperature in distributed PV power plant

AbstractDistributed photovoltaic (PV) power plants often lack solar irradiance monitoring devices, significantly hindering crucial functions such as power forecasting, fault diagnosis, and performance calculation for distributed PV. To address this issue, a real‐time method for soft sensing solar irradiance was proposed in distributed PV. First, we investigated the typical relationship between solar irradiance, ambient temperature, and the electrical characteristics of PV cells. Based on this relationship, we utilized the small sample modeling technique of the Genetic Algorithm‐Support Vector Machine to calculate the ambient temperature. Subsequently, a solar irradiance calculation model based on the backpropagation neural network was developed, taking the PV array voltage, current, calculated ambient temperature, and power as inputs. This approach enables for the estimation of solar irradiance in distributed PV power plants through a simple and efficient calculation process. To demonstrate the reliability and flexibility of the algorithm, we conducted testing with data under various input conditions, such as different power plant configurations, and seasons, coefficient of determination for the proposed model reached 0.95. Overall, the novelty of the proposed method offers a practical solution for soft sensing of solar irradiance in PV power plants, enabling accurate performance analysis and effective operation management without hardware investment.

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

In recent years, with the growing proliferation of photovoltaics (PV), accurate nowcasting of PV power has emerged as a challenge. Global horizontal irradiance (GHI), which is a key factor influencing PV power, is known to be highly variable as it is determined by short-term meteorological phenomena, particularly cloud movement. Deep learning and computer vision techniques applied to all-sky imagery are demonstrated to be highly accurate nowcasting methods, as they encode crucial information about the sky’s state. While these methods utilize deep neural network models, such as Convolutional Neural Networks (CNN), and attain high levels of accuracy, the training of image-based deep learning models demands significant computational resources. In this work, we present a computationally economical estimation technique, based on a deep learning model. We utilize both all-sky imagery and meteorological data, however, information on the sky’s state is encoded as a feature vector extracted using traditional image processing methods. We introduce six all-sky image features utilizing detailed knowledge of meteorological and physical phenomena, significantly decreasing the amount of input data and model complexity. We investigate the accuracy of the determined global and diffuse radiation for different combinations of meteorological parameters. The model is evaluated using two years of measurements from an on-site all-sky camera and an adjacent meteorological station. Our findings demonstrate that the model provides comparable accuracy to CNN-based methods, yet at a significantly lower computational cost.

Composition of feature selection techniques for improving the global horizontal irradiance estimation via machine learning models

The rise in photovoltaic capacity installed globally and the sporadic nature of solar resources underline the significance of solar radiation forecasting for grid integration. The choice of relevant climatic variables for estimating global horizontal irradiance (GHI) is crucial because it influences estimation accuracy. To keep this in mind, different feature selection techniques such as filter, wrapper, and embedded are computed and compared to identify a pertinent input variable for the prediction of GHI. In order to reduce the dimensionality of the input features, it is pertinent to remove unnecessary variables using feature selection techniques. This comprehensive study explores the use of tree-based ensemble machine learning (ML) models in the modeling of global solar radiation. Eighteen statistical and ML regressor algorithms, along with ten feature selection methods are evaluated based on statistical comparisons of their computational effectiveness and estimation errors. The lowest mean absolute error (MAE) and root mean squared error (RMSE) of 0.9938 and 2.5327 achieved by the extra-tree regressor algorithm reveals that it outperforms the other models on the test dataset. The integration of feature selection techniques improves predictive capabilities, reducing MAE and RMSE by 13.15% and 5.24%, respectively, for the Mutual Information method. This study also identifies influential features critical to accurate GHI estimation, such as solar zenith angle, diffuse horizontal irradiance, and direct normal irradiance, which provide valuable insights into the dynamics governing solar radiation estimation.

Estimation of Daily Global Solar Radiation with Different Sunshine-Based Models for Some Burundian Stations

Estimation of Daily Global Solar Radiation with Different Sunshine-Based Models for Some Burundian Stations

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

Estimation of Solar Radiation and Feasibility Analysis of a Concentrating Solar Power Plant in Birao, Central African Republic

Estimation of Solar Radiation and Feasibility Analysis of a Concentrating Solar Power Plant in Birao, Central African Republic

Estimation of urban solar radiation for the City of Recife-PE

Urban Global Solar Radiation(RS) is one of the main inputs of urban climatology, and should already be considered from the conception of the construction project of green infrastructures, which mitigate the environmental impacts of urbanization. The objectives of this work were to calibrate and evaluate the performance in estimating the RS, for the capital of Pernambuco, of six existing models in the literature, namely: Hargreaves; Samani (1982), Bristow; Campbell (1984), Jong & Stewart (1993), Chen et al. (2004), Hunt et al. (1998) e Annandale et al. (2002), referring to a rainy period in the year 2023, with data from an automatic meteorological station installed in one of the campuses of the University of Pernambuco, in the city of Recife-PE. The statistical criteria used for the analysis of the calculated data: the root mean square error (RMSE), the Nash-Sulcliffe coefficient (NSE) and the residual mass coefficient (CMR). The models, in ascending order of greater effectiveness are: Hunt et al. (1998), Bristow; Campbell (1984), Jong & Stewart (1993), Chen et al. (2004), Hargreaves; Samani (1982), and Annandale et al. (2002). However, it should be noted that for the use of the model proposed by Hunt et al. (1998), it is necessary to have daily precipitation data, which may not be available, especially in historical series. If only temperature values are available, the best model for estimating RS is the one proposed by Bristow; Campbell (1984), which presented a satisfactory performance and very close to that obtained by the model proposed by Hunt et al. (1998).

Evaluation and performance comparison of different models for global solar radiation forecasting: a case study on five cities

AbstractRecently, solar energy has emerged as the most promising renewable energy source to meet the world’s energy demands. However, to harness the potential of solar energy, accurate data on solar radiation are crucial. It is considered the first step in assessing solar resources for various applications and achieving energy sustainability goals. Due to the unavailability of solar radiation measurements in many parts of the world, several models have been developed to predict global solar radiation (GSR) at these locations. Thus, this study aims to evaluate the proficiency of several GSR models at five new locations and determine the most suitable one for GSR prediction. The study has further developed solar radiation models for these new locations, as well as general ones for the entire region, which does not have any GSR models despite the existence of many planned solar energy facilities in this area. Additionally, the study investigates the effect of changing the length of the validation dataset on models’ performance and accuracy, as well as assesses the introduced models’ generalization capability. To achieve these objectives, the observed data of GSR for approximately 37 years at studied locations are used to develop and validate the proposed models. The study’s findings reveal that Model 1 provides the best performance at all locations, with accuracy, coefficient of determination ($$R^{2}$$ R 2 ), ranging from 95 to 98%, except for the coastal location, where it is from 91 to 95%. The remaining performance indicators of the best models, such as RMSE, MABE, MAPE, and $$r$$ r , are good, and their values range from 0.7863 to 1.9097 (MJ m−2 day−1), from 0.6430 to 1.7060 (MJ m−2 day−1), from 3.4319 to 10.0890 (%), and from 0.9914 to 0.9981, respectively. The length of the validation dataset has a slight effect on the models’ performance, ranging from about 1% to 2%. Therefore, Model 1 is the recommended solar radiation model, which can provide precise and rapid estimates of global solar radiation. This approach could be used in the design and performance evaluation of many solar applications. The primary benefit of this approach in the current investigation is that temperature data are continuously and effortlessly recorded for various purposes.

Estimation of Solar Radiation Using Artificial Neutral Network for Different Climate Zones in Türkiye

Güneş enerjisinden gerek termal gerekse elektrik uygulamalarında elde edilen faydalı enerji yüzeye gelen ışınıma bağlıdır ve bu ışınım miktarı konum, zaman, iklimsel koşullar ve çevre koşullarına göre değişmektedir. Güneş enerji destekli enerji sistemlerinin etkin bir şekilde tasarlanması ve çalıştırılması için ışınım miktarının doğru bir şekilde belirlenmesi önem arz etmektedir. Bu çalışmada Türkiye’nin farklı iklim kuşaklarında yer alan dört il (Kayseri, Rize, Hakkari ve İzmir) için MATLAB uygulamasıyla Yapay Sinir Ağları (YSA) modeli oluşturulup güneş ışınımı tahmini yapılmıştır. Güneş ışınımı tahmini yapılırken illerin enlem, boylam ve rakımdan oluşan coğrafik verilerinin etkisinin yanı sıra ortalama sıcaklık, bağıl nem, bulutluluk ve buhar basıncı gibi meteorolojik verilerin etkisi de ele alınmıştır. Tüm bu etkenler göz önüne alındığında kurulan modelde en iyi tahminleme sonucu R2=0.93 değeriyle Hakkari ilinde olup sırasıyla İzmir, Kayseri ve Rize illeri takip etmektedir. Her bir il için YSA ile tahmin edilen değerler ile ölçülen değerler arasında uyum olduğu görülmüştür.

A mathematical model for rapid estimation of solar radiation in urban canyons with trees and its applications

Street-level solar radiation estimation can help to better understand the influence of solar radiation on urban microclimate, outdoor thermal comfort and health, solar energy utilization, and building energy savings. However, existing urban canyon radiation models based on Monte Carlo ray tracing are challenging to apply in real practice due to limited applicability and high computational efforts. A flexible and robust mathematical model for solar radiation in urban canyons is proposed in this study to estimate solar radiation potential at the street level quickly and can be applied to the urban scale. The newly developed model considers the effect of trees on street canyon radiative transfer based on some assumptions and simplifications. Using the matrix method to resolve the infinite number of multi-reflections, the model accurately predicts solar radiation inside the street canyon compared to experimental data. The maximum root mean square error (RMSE) in the model prediction is 52.7 W/m2 and 14.5 W/m2, respectively, for urban canyons without and with trees in the studied case. And the Pearson correlation coefficient (r) is above 0.987 for all scenarios. An open-source Python script module for the model application is provided and can be integrated with other models or software platforms. The model has been used to estimate solar potential on the street and urban scales taking Chongqing city, China as an example. It is proven that the model is helpful for rapid assessment of solar radiation at the street and urban scales, serving urban design and planning.

Effect of the Ground Albedo on the Estimation of Solar Radiation on Tilted Flat-Plate Surfaces: The Case of Saudi Arabia

This work investigates the influence of ground albedo on the solar radiation obtained by surfaces mounted on fixed-tilt-to-south, one-axis, and two-axis systems. To do this, estimation of the solar radiation difference is performed by applying real albedo and zero albedo. This is achieved within Saudi Arabia at 82 selected sites. Annual, seasonal, and monthly mean solar energy differences are computed as a function of the site’s number, latitude, and local near-real ground albedo. The great variation in the ground-albedo values at the 82 sites (0.1–0.46) could be thought of as having a significant effect on the solar radiation levels received on the three tracking modes. This analysis shows quite the opposite; zero-albedo ground diminishes solar radiation levels by 1.43%, 3.50%, and 3.20%, respectively, for the three modes. Therefore, in most solar engineering applications, a ground albedo of 0.2 (considered a reference) can be used without losing accuracy. This is the main conclusion of the study, which must, however, be applied with caution in areas with snow cover, especially for mode-III tracking systems. In such situations, the increase in solar radiation levels may be up to 15% (but ≈3.5% for mode-I and -II systems instead).