A New Cloud-Based IoT Solution for Soiling Ratio Measurement of PV Systems Using Artificial Neural Network

Introduction Atmospheric corrosion is the main cause of metallic heritage degradation when they are exposed to relative humidity, temperature cycle and air contaminants. Corrosivity maps are one of the preventive strategies for atmospheric corrosion mitigation, but they do not provide enough information about corrosion mechanism, besides that long experimentation periods are required [1]. Moreover, electrochemical techniques are alternatives to speed up results, but their relationship with constantly changing environmental variables is difficult to control.Currently, Artificial Intelligence has been introduced in corrosion engineering studies, because of its capability to make predictions based on big data. Also, experimental data of Electrochemical Impedance Spectroscopy had been used in Artificial Neural Network training to obtain Nyquist Diagrams [2]. The objective of this work is to obtain computational models of Artificial Neural Networks to predict atmospheric corrosion in Bronze with nanostructured patina with SiO2, in marine environments. Method Environmental parameters were taken from database previously reported [3,4], and from Civil Protection Secretariat of Veracruz. Whilst electrochemical evaluation consists in Polarization Resistance and Electrochemical Impedance Spectroscopy; they were carried out in a Gamry Interface 1000 Potentiostat with a Saturated Calomel Reference Electrode (SCE) and a graphite bar as counter electrode in an agar gelled cell. Meanwhile, working electrode was bronze with nanostructured patina with SiO2. Two patina were prepared, the first with CuSO4 solution (0.015 mol L-1) and the second applying a CuNO3 solution (20% wt).The 13 variables used in the database for Artificial Neural Networks (ANN) training were: exposure time, chloride concentration, sulfur compounds concentration, relative humidity, precipitation level, windspeed, temperature, nanocoating presence, corrosion potential, corrosion rate, frequency, real and imaginary component of impedance. The values of each variable were treated with measures of central tendency and dispersion, as well as correlation matrix and histograms, which allowed to find a relationship between variables, and choosing the inputs for ANN. The prediction models consisted in 5, 9, 10 and 12 neurons in the input layer (R5E, R9E, R10E and R12E, respectively), a hidden layer, and Zima as only neuron in the output layer. The number of neurons in hidden layer varied from 1 to 8 until the highest coefficient of determination was achieved. The computational model was performed using the toolbox in MATLAB with a feedforward ANN. It is worth mention that the hidden and output layers used a hyperbolic tangent and a linear transfer function, respectively, and a Levenberg-Marquardt algorithm as training function was used in the models. Results and Conclusions Figure 1 presents the coefficient of determination (R2) and root mean squared error (RMSE) with different ANN architectures. In the four models, the highest value was reached with 8 neurons in the hidden layer. Then, the linear regressions for R5E, R9E, R10E and R12E models and 8 neurons in the input layer were compared in Figure 2. In that sense, the Nyquist Diagrams in Figure 3 were simulated with the R9E and R12E models. Also, they were compared with experimental Nyquist Diagrams for Bronze with patina of CuSO4 and CuNO3, with and without SiO2 nanoparticles, at 56 days of exposure in marine atmospheres. The simulations demonstrated how an ANN model with 12 neurons in the input layer, 8 neurons in the hidden layer and 1 neuron in the output layer can be used as a corrosion prediction model of bronze with nanostructured patina with SiO2.

The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m² (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.

This study intended to use optimized artificial neural network (ANN) for the design of pure cohesive slopes (by means of considering sufficient safety factors (SF) of stability). A total of 630 finite element limit equilibrium analysis were completed to provide datasets of training and testing for preparation of the ANN-based predictive models. The database consisted of 504 training datasets (80% of the database) and 126 testing datasets (20% of the database). To construct an optimized ANN structure, variables of ANN algorithm such as a number of neurons in hidden layer and the number of hidden layers were changed (e.g. with a series of trial and error process). The input parameters that used in the analysis include setback distance ratio (b/B), the undrained shear strength of the cohesive soil (Cu), applied stresses on the slope (Fy) and slope angle (β) while the final output was the value of SF. The predicted results for datasets from ANN models were assessed based on several well-known statistical indices namely, R2, RMSE and VAF. Also, in order to evaluate the performance of predicted network two ranking systems of (1) colour intensity rating (CIR) and (2) total ranking method (TRM), i.e. based on the result of statistical indices, were utilized. The optimal architecture of 4 × 6 × 1 was found for the ANN structure. After the performance of the optimized ANN model, the results are provided as a simple tansig formula which can be used for further application of engineering purposes. In the optimized ANN network and based on R2, RMSE and VAF, values of (0.999, 0.0350 and 99.9978) and (0.999, 0.0379 and 99.998) were found, respectively, for both of the normalized training and testing datasets. This proves the excellent performance of the proposed ANN model in estimating the SF of the slope. As the final outputs and to show the reliability of the proposed ANN formula, a series of optimized ANN design solution charts are compared to a similar example of measured charts.

This study aimed to train and validate two artificial neural network (ANN) models, one with four hidden layers and the other with five hidden layers, to predict the daily photovoltaic power output of a 20 Kw photovoltaic power plant with bifacial photovoltaic modules with tilt angle variation from 0° to 90° in two Moroccan cities, Ouarzazate and Oujda. To validate the two proposed models, photovoltaic power data calculated using the System Advisor Model (SAM) software version 2023.12.17 were employed to predict the average daily power of the photovoltaic plant for December, utilizing MATLAB software Version R2020a 9.8, and for the tilt angles corresponding to the latitudes of the two cities studied. The results differ from one model to another according to their mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2) values. The artificial neural network model with five hidden layers obtained better results with a R2 value of 0.99354 for Ouarzazate and 0.99836 for Oujda. These two proposed models are trained using the Levenberg Marquardt (LM) optimizer, which is proven to be the best training procedure.

Number of distributed generation (DG) plants integrated to utility network increases due to rising of electricity consumption. DG plants have a big impact on voltage level of network in accordance with this increasing. Especially DG plants based on renewable resources may cause voltage rise and drop because of their intermittently energy generation. Generally, the voltage level is regulated by reactive power control technique and On-load tap changer (OLTC). Generally, HV/MV transformers use OLTC to regulate the voltage level under load. The conventional voltage control techniques depend on current of branch at the main substation end. If the rising of DG installation is taken into consideration, the conventional methods can be inadequate. Artificial intelligence techniques can be used to mitigate this problem. In this paper, an Artificial Neural Networks (ANN) model is designed for determination of OLTC best tap position. ANN model is trained by using different values of network voltage level, DG generation values and amount of load demand. Designed model is applied on electrical network of Selcuk University. The electrical network of Selcuk University is modeled by using PSCAD/EMTDC. 1MW Photovoltaic (PV) power plant is determined as DG plant.

Removal of spoiling materials from solar panel surfaces by applying surface acoustic waves

This study intends to better solar photovoltaic (PV) panel performance by employing anti-reflective coating and explore how dust affects solar panel effectiveness. Three equivalent solar PV panels were compared, having one of them being uncoated, the next one having a TiO2 nanomaterial coating, and the very last one having a SiO2 nanomaterial coating. PV panel surfaces are coated with superhydrophilicity TiO2 as well as superhydrophobic SiO2 nanomaterials using a cloth made of microfibers. With the aid of a photovoltaic (PV) analyser, the power output of each and every PV panel has been monitored during the month of November 2021. After one month of being exposed to the environment, the percentage improvement in efficiency for TiO2-coated panels was 7.66% and for SiO2 coated panels was 19.73% as compared to uncoated PV panels. Results demonstrate that SiO2 covered PV panels outperform the other two scenarios in terms of efficiency and power output. The frequency of photovoltaic panel washing is reduced by the application of coating. Different amounts of dust are evenly scattered on the surface of the PV panel in order to observe the effect of the dust. Additionally, as the amount of dust increases, the effectiveness of PV panels declines considerably. When 20g of dust is dispersed across the surface of a PV panel, its efficiency falls by 34.6 percent.

The generalization ability of artificial neural networks (ANNs) and M5 model tree (M5Tree) in modeling reference evapotranspiration (ET 0 ) is investigated in this study. Daily climatic data, average temperature, solar radiation, wind speed, and relative humidity from six different stations operated by California Irrigation Management Information System (CIMIS) located in two different regions of the USA were used in the applications. King-City Oasis Rd., Arroyo Seco, and Salinas North stations are located in San Joaquin region, and San Luis Obispo, Santa Monica, and Santa Barbara stations are located in the Southern region. In the first part of the study, the ANN and M5Tree models were used for estimating ET 0 of six stations and results were compared with the empirical methods. The ANN and M5Tree models were found to be better than the empirical models. In the second part of the study, the ANN and M5Tree models obtained from one station were tested using the data from the other two stations for each region. ANN models performed better than the CIMIS Penman, Hargreaves, Ritchie, and Turc models in two stations while the M5Tree models generally showed better accuracy than the corresponding empirical models in all stations. In the third part of the study, the ANN and M5Tree models were calibrated using three stations located in San Joaquin region and tested using the data from the other three stations located in the Southern region. Four-input ANN and M5Tree models performed better than the CIMIS Penman in only one station while the two-input ANN models were found to be better than the Hargreaves, Ritchie, and Turc models in two stations.

Artificial neural networks solution to display residual hoop stress field encircling a split-sleeve cold expanded aircraft fastener hole

The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., sodium adsorption ratio (SAR), percent sodium (%Na) residual, Kelly’s ratio (KR), and residual sodium carbonate (RSC) in the groundwater of the Pratapgarh district of Southern Rajasthan, India. This study focuses on verifying the suitability of water for irrigational purpose, wherein more groundwater decline coupled with water quality problems compared to the other areas are observed. The southern part of the Rajasthan State is more populated as compared to the rest of the parts. The southern part of the Rajasthan is more populated as compared to the rest of the Rajasthan, which leads to the industrialization, urbanization, and evolutionary changes in the agricultural production in the southern region. Therefore, it is necessary to propose innovative methods for analyzing and predicting the water quality (WQ) for agricultural use. The study aims to develop an optimized artificial neural network (ANN) model to predict the sodium hazardness of groundwater for irrigation purposes. The ANN model was developed using ‘nntool’ in MATLAB software. The ANN model was trained and validated for ten years (2010–2020) of water quality data. An L-M 3-layer back propagation technique was adopted in ANN architecture to develop a reliable and accurate model for predicting the suitability of groundwater for irrigation. Furthermore, statistical performance indicators, such as RMSE, IA, R, and MBE, were used to check the consistency of ANN prediction results. The developed ANN model, i.e., ANN4 (3-12-1), ANN4 (4-15-1), ANN1 (4-5-1), and ANN4 (3-12-1), were found best suited for SAR, %Na, RSC, and KR water quality indicators for the Pratapgarh district. The performance analysis of the developed model (3-12-1) led to a correlation coefficient = 1, IA = 1, RMS = 0.14, and MBE = 0.0050. Hence, the proposed model provides a satisfactory match to the empirically generated datasets in the observed wells. This development of water quality modeling using an ANN model may help to useful for the planning of sustainable management and groundwater resources with crop suitability plans as per water quality.

BackgroundThis study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.MethodsA total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.ResultsFor distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.ConclusionsOur study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.

Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

Purpose – This paper aims to predict the needle penetration force (NPF) in denim fabrics using the artificial neural network (ANN) and multiple linear regression (MLR) models based on the effects of various sewing parameters. Design/methodology/approach – In order to design the ANN and MLR models, four parameters including fabric weight, number of fabric layers, weave pattern, and sewing needle size are taken into account as the input parameters and NPF as the output parameter. According to these parameters, 140 samples of data were resulted. Each sample was tested five times. From these 140 data (input-output data pairs), 112 were used for training the ANN and MLR models and 28 samples were used to test the performance of ANN and MLR. Also, the NPF was measured on the Instron tensile tester to simulate sewing process. Findings – The results indicated that the NPF in denim fabrics can be well predicted in terms of sewing parameters by using ANN and MLR models, in which the ANN model exhibits greater performance than MLR (RANN=0.989> RMLR=0.901). Research limitations/implications – The NPF measurement method is limited at low speed. Originality/value – Using the ANN model for forecasting NPF in denim fabrics can help the garment manufactures to produce high-quality denim products and improve the sewing process through reducing seam damage. The NPF could be also measured in the cycle loading conditions similar to sewing machine process by using a special designed tools mounted on the Instron tensile tester.

ABSTRACTStatistical methods such as Poisson distribution, negative binomial regression (NB), and zero inflated negative binomial regression (ZINB) have generally been used in road safety studies to establish the complex relationships between variables. Over the last few years, the artificial neural networks (ANN) model has also been used. The ANN model does not have any prior limitations such as the equality condition of mean and variance observed in Poisson regression. However, though the ANN model has been used in the analysis of different accident types, to the best of our knowledge, no study has used the ANN model for establishing this relationship with truck accident data on divided multilane interurban roads. In this study, the road sections D750/07–D750/15 in Ankara–Aksaray–Eregli, Turkey, were considered and truck accident data from 2008 to 2011 were analyzed using NB and ANN. The analysis show that the ANN model has lower errors and higher R2 values than NB and performs slightly better than NB for predicting the number of trucks involved in accidents. Based on a comparison of performances the study concludes, that ANN could be used as an alternative model for analyzing truck accident data on divided multilane interurban roads in Turkey.