Developed Artificial Neural Network Model Research Articles

ANN based Fire Resistance Prediction of FRP-Strengthened RC Slabs with Fireproof Panel Including Air Layer

In this study, an Artificial Neural Network (ANN) model to predict the fire resistance of Fiber Reinforced Plastic (FRP) strengthened Reinforced Conrete (RC) slabs with fireproof panel including air layer exposed to fire is developed. FRP-strengthened RC slabs with fireproof panel including air layer has a structure that improves structural performance and fire resistance by placing FRP and a fireproof panel including air at the bottom of the RC slab. First, fire test is performed using the air layer as a variable, and the validity of the software ABAQUS heat transfer analysis is verified by comparing it with the fire test results. Through this heat transfer analysis, the temperature distribution of the slab is determined, and 1521 data sets are obtained by calculating the strength reduction coefficient and elastic coefficient reduction coefficient of each material. The data consist of eight input parameters: the type of fire curve, fire exposure time, insulation thickness, air layer thickness, concrete covering thickness, and FRP thickness. The output parameter is the value of the strength and elastic modulus reduction coefficient for each material. The average values of MSE, RMSE, R, and SSE for all output parameters, which are performance indicators of the ANN model, are 0.00202, 0.03978, 0.98235, and 1.10445, respectively, and the developed ANN model has high computational accuracy and high generalization ability. Therefore, the ANN model developed here can determine the mechanical properties of each material in FRP-strengthened RC slabs with fireproof panel including air layer exposed to fire and evaluate the fire resistant without complex calculations. Through contribution and sensitivity analysis, the effect of input data on output data is confirmed.

An Artificial Neural Network Method for Forecasting the Stability of Soil Slopes

Artificial neural network (ANN) methods, based on sophisticated models, have been developed recently that can predict slope stability. In this study, we have developed a genetic algorithm (GA) based on ANN to assess the stability of soil slope. Firstly, an ANN-based genetic algorithm was trained for nonlinear input-output mapping of the slope. A total of 190 soil slopes with unique values of shear strength properties (friction angle, cohesion, and unit weight), geometric parameters (slope angle and slope height), and corresponding factor of safety (FS) have been collected to give a neural network training dataset. Then, a three-layer neural network model is established based on GA. The prediction and performance ability of the established model is assessed using the correlation coefficient (R2). By the outcomes, the trained ANN model with the R2 value of 0.98 is reliable, valid, and simple for evaluating the soil slope stability and estimating the FS. Additionally, the proposed neural network model is applied to a case of soil slope from prior studies. Findings show that the developed ANN model can be versatile in studying the stability of soil slopes.

Developing a hybridized thermodynamic and data-driven model for catalytic supercritical water gasification of biomass for hydrogen production

Supercritical water gasification (SCWG) of biomass offers a promising pathway for sustainable energy production. This study presents a novel approach that combines an artificial intelligence (AI) technique with the Gibbs minimization (GM) method to enhance the GM approach by involving the catalyst effect on the gasified yield. The proposed strategy incorporates a modified Gibbs energy calculation, incorporating a delta term that quantifies the influence of the catalyst’s properties on the Gibbs free energy. The delta term is determined using experimental data, including characteristics of biomass and the employed catalysts. An artificial neural network (ANN) model is developed to predict the delta value and is integrated with the GM method to improve the estimation of gasified product yields in the presence of a catalyst. The ANN model establishes correlations between the catalyst specifications such as pore volume, specific surface area, and pore diameter, and delta value. The trained model can predict the delta term, allowing the catalyst’s role to be considered within the GM technique. This integration addresses a limitation of the conventional thermodynamic model, which typically ignores the catalyst’s influence. The developed ANN model exhibits high accuracy, with an R2 value of 0.92 and a mean squared error (MSE) of 0.042. The hybridized GM approach substantially improves the hydrogen yield prediction compared to the basic model, enhancing (reducing) the MSE and mean absolute error by 98% and 93%, respectively. Furthermore, sensitivity analysis reveals that increasing temperature and reducing pressure contribute to improved hydrogen production. Additionally, lower biomass concentrations are shown to be beneficial for enhancing the hydrogen yield.

Prediction of hospital mortality among critically ill patients in a single centre in Asia: comparison of artificial neural networks and logistic regression-based model.

This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong Kong. This retrospective analysis included all patients admitted to the intensive care unit of Pamela Youde Nethersole Eastern Hospital from January 2010 to December 2019. The ANN model was constructed using parameters identical to the APACHE IV model. Discrimination performance was assessed using area under the receiver operating characteristic curve (AUROC); calibration performance was evaluated using the Brier score and Hosmer-Lemeshow statistic. In total, 14 503 patients were included, with 10% in the validation set and 90% in the ANN model development set. The ANN model (AUROC=0.88, 95% confidence interval [CI]=0.86-0.90, Brier score=0.10; P in Hosmer-Lemeshow test=0.37) outperformed the APACHE II model (AUROC=0.85, 95% CI=0.80-0.85, Brier score=0.14; P<0.001 for both comparisons of AUROCs and Brier scores) but showed performance similar to the APACHE IV model (AUROC=0.87, 95% CI=0.85-0.89, Brier score=0.11; P=0.34 for comparison of AUROCs, and P=0.05 for comparison of Brier scores). The ANN model demonstrated better calibration than the APACHE II and APACHE IV models. Our ANN model outperformed the APACHE II model but was similar to the APACHE IV model in terms of predicting hospital mortality in Hong Kong. Artificial neural networks are valuable tools that can enhance real-time prognostic prediction.

Maximizing energy efficiency in wastewater treatment plants: A data-driven approach for waste heat recovery and an economic analysis using Organic Rankine Cycle and thermal energy storage

Maximizing energy efficiency through waste heat recovery (WHR) processes is crucial for sustainable and eco-friendly operations across multiple industries, notably in wastewater treatment plants (WWTPs). This work proposes a comprehensive approach for assessing the WHR feasibility in WWTPs, structured in two main objectives. Firstly, an Artificial Neural Network (ANN) model is developed to accurately predict WHR based on operational data, including biogas temperature, biogas pressure, daily production in kWh, and WHR values in kWhth. The second objective focuses on economically evaluating the WHR feasibility based on the estimated WHR values obtained by the ANN model, and then realistically assessing the economic feasibility of integrating the Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage (STES) systems. With an application to the As-Samra WWTP located in Jordan, the developed ANN model demonstrates promising results in the validation phase, with a root mean square error (RMSE) of 2206 kWh/day, a mean absolute error (MAE) of 1674 kWh/day, and an R-squared (R2) value of 68%. On the other hand, the economic analysis reveals that an optimal ORC system of 412.14 kWe capacity yields a Net Present Value (NPV) of 2.09 million US dollars, a Levelized Cost of Energy (LCOE) of 0.0749 USD/kWh, a Payback Period (PBP) of 4.8 years, and annual revenues of 428 kUSD. This work also investigates the techno-economic feasibility of integrating ORC-STES. Results indicate that the LCOE and PBP are highly affected by the ORC's capital cost, and integrating STES increases the LCOE to 0.0824 USD/kWh, rendering its integration with ORC infeasible. This study aims to advance the understanding and application of WHR in WWTPs, paving the way for more efficient and sustainable practices in the field.

Tuning the catalytic active sites of TiO2/CeO2 interactions to improve oxygen vacancy and lattice parameters for CO2 conversion to green fuel additive

In recent years, the conversion of carbon dioxide (CO2) to value-added products has become an area of great interest, both from an industrial and environmental perspective. Here we have successfully synthesized TiO2/CeO2-based catalysts by exo- and endo-template and use for direct conversion of CO2 to dimethyl carbonate (DMC). It was found that the catalytic activity of TiO2/CeO2 catalysts depends on the acidic and basic sites present in the catalyst. The study also investigated the optimization of reaction conditions in a batch reactor. It was found that the catalytic activity of TiO2/CeO2-based catalysts significantly enhances the CO2 conversion and achieves a high DMC yield compared to the performance of pure CeO2 and TiO2. The reusability of the TiO2/CeO2 catalysts was examined as well. Additionally, the heat of the reaction and Gibbs free energy were calculated using chemical equilibrium models. The experimental response data was validated using artificial neural networks (ANN). The R2 values between the expected and experimental results for DMC yield and CO2 conversion were 0.9953 and 0.9969, respectively. The developed ANN model successfully predicts the percentage of CO2 conversion and DMC yield under the given reaction conditions, with prediction accuracy MSE of 0.0013 and 0.0008, respectively.

Modeling spatial groundwater level patterns of Bangladesh using physio-climatic variables and machine learning algorithms

Groundwater monitoring is essential for sustainable groundwater resource management in a country like Bangladesh, where this precious resource is gradually declining due to over-extraction. However, traditional methods of acquiring groundwater level (GWL) data over a large area are time-consuming and expensive. To address this, this study proposes an alternative approach using freely available daily groundwater storage (GWS) gridded data from the Global Land Data Assimilation System (GLDAS) and other data sources such as population, rainfall, temperature, irrigation, and elevation. By employing different regression and machine learning models like multiple linear regression (MLR), regression trees, support vector machines (SVM), Gaussian process regression (GPR), and artificial neural networks (ANN), the study aimed to model GWL data for Bangladesh at a spatial resolution of 0.25° × 0.25°. In-situ weekly GWL data collected from 844 locations across the country were used for model development. The results indicated that GWS data alone was insufficient to estimate the spatial variability and trend of groundwater in Bangladesh. However, the comparison of different models showed that the ANN model performed better, with an overall correlation coefficient (R) of 0.95 and mean squared error (MSE) of 0.64 m2 when estimating GWL using GWS and other data. The study also identified population and rainfall as the most influential factors in determining GWL. The developed ANN model can be utilized to estimate GWL at locations where observation data are unavailable, enabling the monitoring of GWL for sustainable groundwater management in Bangladesh.

Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions.

This paper tackles the issue of moisture variation in wood-based materials, explicitly focusing on melamine-coated particleboard (hereafter referred to as melamine) and medium-density fiberboard (MDF) used in the third phase of wood industry transformation. The approach involves a comprehensive strategy for predicting moisture content variation, incorporating numerical simulation, experimental testing, and the application of artificial neural network (ANN) technology to enhance accuracy in furniture manufacturing. The developed ANN models are tailored to predict moisture content changes under specific thermal comfort conditions. Remarkably, these models demonstrate high precision, with an average error margin of only 1.40% for 8% moisture content (MC) and 2.85% for 12% MC in melamine, as well as 1.42% for 8% MC and 2.25% for 12% MC in MDF. These levels of precision surpass traditional models, emphasizing this study's novelty and practical relevance to the industrial context. The findings indicate that ANN models adapt to diverse environmental conditions, presenting a robust tool for optimizing moisture management in wood-based materials. This research contributes valuable insights for improving the reliability and efficiency of moisture content predictions in the wood industry.

Prediction of Specific Fuel Consumption of a Tractor during the Tillage Process Using an Artificial Neural Network Method

In mechanized agricultural activities, fuel is particularly important for tillage operations. In this study, the impact of seven distinct parameters on fuel usage per unit of draft power was examined. The parameters are tractor power, soil texture index, plowing speed, plowing depth, width of implement, and both initial soil moisture content and soil bulk density. This study investigated the construction of an artificial neural network (ANN) model for tractor-specific fuel consumption predictions for two tillage implements: chisel and moldboard plows. The ANN model was created based on the collection of related data from previous research studies, and the validation was performed using actual field experiments in clay soil using a chisel plow. The developed ANN model (9-22-1) was confirmed by graphical assessment; additionally, the root-mean-square error (RMSE) was computed. Based on the RMSE, the results demonstrated a good agreement for specific fuel consumption per draft power between the observed and predicted values, with corresponding RMSE values of 0.08 L/kWh and 0.075 L/kWh for the training and testing datasets, respectively. The novelty of the work presented in this paper is that, for the first time, a farm machinery manager can optimize tractor fuel consumption per draft power by carefully controlling certain parameters, such as initial soil moisture content, tractor power, plowing speed, implement width, and depth of plowing. The results show that the input parameters make a significant contribution to the output over the used data with different percentages. Accordingly, the contribution analysis showed that the implement width had a high impact on tractor-specific fuel consumption for both plows at 30.13%; additionally, the chisel and moldboard plows contributed 4.19% and 4.25% in predicting tractor fuel consumption per draft power. This study concluded that practical useful advice for agricultural production can be achieved through optimizing fuel consumption rate by selecting the proper levels of affecting parameters to reduce fuel costs. Moreover, an ANN model could be used to develop future tractor fuel-planning schemes for tillage operations.

Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg–Marquardt method in Elazig, Turkey

 The phenomenon of evapotranspiration (ET) is closely linked to the issue of water scarcity, as it involves water loss through both evaporation and plant transpiration. Accurate prediction of evapotranspiration is of utmost importance in the strategic planning of agricultural irrigation, effective management of water resources, and precise hydrological modeling. The current investigation aims to predict the monthly ET values in the Elazig province by developing an artificial neural network (ANN) model utilizing the Levenberg–Marquardt method. Consequently, the values of temperature, precipitation, relative humidity, solar hour, and mean wind speed were utilized in forecasting evapotranspiration values by implementing ANN algorithms. This research makes a valuable contribution to the existing body of literature by utilizing an ANN model developed with the Levenberg–Marquardt method to estimate evapotranspiration. It has been discovered that evapotranspiration values are impacted by various factors such as temperature (minimum, average, maximum), relative humidity (minimum, average, maximum), wind speed, solar hour, and precipitation values, which are taken into consideration for prediction. The findings indicated that Elazig, Keban, Baskil, and Agin sites had R values of 0.9995, 0.9948, 0.9898, and 0.9994 in the proposed model. It was found that Elazig’s MAPE ranged from 0 to 0.2288, Keban’s was 0.0001 to 0.3703, Baskil’s was between 0 and 0.4453, and Agin’s was both 0 and 0.2784. The findings obtained from the proposed model are compatible with evapotranspiration values computed from the Hargreaves method (R2 = 0.996). The study’s findings provide significant insights for planners and decision-makers involved in the planning and managing water resources and agricultural irrigation.

Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabs

With the emergence of fiber-reinforced polymer (FRP) reinforcement as a substitute for conventional steel reinforcement, different design codes have been developed to account for the different mechanical properties of the FRP, specifically the elastic modulus. The ACI 440.11-22, CSA/S806-12, and JSCE-97 are well-known standards for FRP-reinforced concrete structures. In particular, these design standards show significant variations in estimating the punching shear resistance and accounting for the elastic modulus. This study provides a statistical and machine learning-based evaluation of the punching shear models in design standards. An Artificial neural network (ANN) framework is used to develop a generalized punching resistance model of flat slabs reinforced with steel and FRP bars utilizing a large experimental dataset with 539 tests. The study presents a parametric study to examine the effect of different factors on the punching shear strength. The parametric study provided a graphical presentation and comparison between the design models and the developed ANN model.

Artificial Neural Network Models for Determining the Load-Bearing Capacity of Eccentrically Compressed Short Concrete-Filled Steel Tubular Columns

Artificial neural networks (ANN) have a great promise in predicting the load-bearing capacity of building structures. The purpose of this work was to develop ANN models to determine the ultimate load of eccentrically compressed concrete-filled steel tubular (CFST) columns of circular cross-sections, which operated on the widest possible range of input parameters. Short columns were considered for which the amount of deflection does not affect the bending moment. A feedforward network was selected as the neural network type. The input parameters of the neural networks were the outer diameter of the columns, the thickness of the pipe wall, the yield strength of steel, the compressive strength of concrete and the relative eccentricity. Artificial neural networks were trained on synthetic data generated based on a theoretical model of the limit equilibrium of CFST columns. Two ANN models were created. When training the first model, the ultimate loads were determined at a given eccentricity of the axial force without taking into account additional random eccentricity. When training the second model, additional random eccentricity was taken into account. The total volume of the training dataset was 179,025 samples. Such a large training dataset size has never been used before. The training dataset covers a wide range of changes in the characteristics of the pipe metal and concrete of the core, pipe diameters and wall thicknesses, as well as eccentricities of the axial force. The trained models are characterized by high mean square error (MSE) scores. The correlation coefficients between the predicted and target values are very close to 1. The ANN models were tested on experimental data for 81 eccentrically compressed samples presented in five different works and 265 centrally compressed samples presented in twenty-six papers.

Flowing Bottomhole Pressure during Gas Lift in Unconventional Oil Wells

Summary We present artificial neural network (ANN) models for predicting the flowing bottomhole pressure (FBHP) of unconventional oil wells under gas lift operations. Well parameters, fluid properties, production/injection data, and bottomhole gauge pressures from 16 shale oil wells in Permian Basin, Texas, USA, are analyzed to determine key parameters affecting FBHP during the gas lift operation. For the reservoir fluid properties, several pressure-volume-temperature (PVT) models, such as Benedict-Webb-Rubin (BWR); Lee, Gonzalez, and Eakin; and Standing, among others, are examined against experimentally tuned fluid properties (i.e., viscosity, formation volume factor, and solution gas-oil ratio) to identify representative fluid (PVT) models for oil and gas properties. Pipe flow models (i.e., Hagedorn and Brown; Gray, Begs and Brill; and Petalas and Aziz) are also examined by comparing calculated FBHP against the bottomhole gauge pressures to identify a representative pipe flow model. Training and test data sets are then generated using the representative PVT and pipe flow models to develop a physics-based ANN model. The physics-based ANN model inputs are hydrocarbon fluid properties, liquid flow rate (qL), gas-liquid ratio (GLR), water-oil ratio (WOR), well true vertical depth (TVD), wellhead pressure (Pwh), wellhead temperature (Twh), and temperature gradient (dT/dh). A data-based ANN model is also developed based on only TVD, Pwh, qL, GLR, and WOR. Both physics- and data-based ANN models are trained through hyperparameter optimization using genetic algorithm and K-fold validation and then tested against the gauge FBHP. The results reveal that both models perform well with the FBHP prediction from field data with a normalized mean absolute error (NMAE) of around 10%. However, a comparison between results from the physics- and data-based ANN models shows that the accuracy of the physics-based model is higher at the later phase of the gas lift operation when the steady-state pipe flow is well established. On the contrary, the data-based model performs better for the early phase of gas lift operation when transient flow behavior is dominant. Developed ANN models and workflows can be applied to optimize gas lift operations under different fluid and well conditions.

Modeling of Triphenyl Phosphate Surfactant Enhanced Drying of Polystyrene/p-Xylene Coatings Using Artificial Neural Network

The drying process of polymeric coatings, particularly in the presence of surfactants, poses a complex challenge due to its intricate dynamics involving simultaneous heat and mass transfer. This study addresses the inherent complexity by employing Artificial Neural Networks (ANNs) to model the surfactant-enhanced drying of poly(styrene)-p-xylene coatings. A substantial dataset of 16,258 experimentally obtained samples forms the basis for training the ANN model, showcasing the suitability of this approach when ample training data is available. The chosen single-layer feed-forward network with backpropagation adeptly captures the non-linear relationships within the drying data, providing a predictive tool with exceptional accuracy. Our results demonstrate that the developed ANN model achieves a precision level exceeding 99% in predicting coating weight loss for specified input values of time, surfactant amount, and initial coating thickness. The model’s robust generalization capability eliminates the need for additional experiments, offering reliable predictions for both familiar and novel conditions. Comparative analysis reveals the superiority of the ANN over the regression tree, emphasizing its efficacy in handling the intricate dynamics of polymeric coating drying processes. In conclusion, this study contributes a valuable tool for optimizing polymeric coating processes, reducing production defects, and enhancing overall manufacturing quality and cost-effectiveness.

Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV Module

In this work, an Artificial Neural Network (ANN) with a backward-propagation technique was used to predict the power generation of the Photovoltaic (PV) module in weather conditions of Baghdad city-Iraq. Experiment tests were investigated in the summer of 2022. Three weather parameters, including: (solar radiation, ambient temperature, and wind speed), the output electrical characteristics of the PV module (voltage, current, power), and module temperature (were measured. Therefore, the dataset of the ANN system consists of four input and one output parameter. Furthermore, the structure of ANN includes a single hidden layer with a backward propagation technique. The main goal of this study was to optimize the number of neurons in the training process. The evaluation of the ANN model depended on the determination coefficient (R) and Root Mean Squared Error (RMSE).  The obtained results show that the architecture of ANN is appropriate for predicting the power generated from the PV module. The developed ANN model has good accuracy. Where the MSE is 0.002747 at epoch 9 in the model. Furthermore, the R is recorded as 0.99078, 0.98254, 0.99125, and 0.99005 for training, testing, validation, and all respectively in the proposed model. In addition, the optimization number of neurons in the hidden layer gave sufficient accuracy without referring to the choice of the number of neurons by using the trial-and-error method that most researchers relied.

Experimental investigation and artificial neural network-based modelling of thermal barrier engine performance and exhaust emissions for methanol-gasoline blends

In recent years, due to environmental concerns and the depletion of fossil fuels, alternative fuel use and alternative emission reduction methods have gained importance in the automotive industry. In addition, methanol is used as an alternative fuel in gasoline engines with coated piston engines. This study first presents an experimental investigation of engine performance and exhaust emissions for a partially thermal barrier lined piston engine operating on methanol-gasoline blends. In the second phase the obtained data is then used to develop an Artificial Neural Network (ANN) based model to predict engine performance and exhaust emissions for methanol-gasoline blends. The developed ANN model was trained and validated using MATLAB. The results of the experimental study showed that the use of methanol-gasoline blended fuel in the engine provides better engine performance and reduced exhaust emissions compared to gasoline fuel. According to the results obtained, an increase of 3.7 % in effective power and a decrease in NOx and HC emissions by 19 % and 18 %, respectively, compared to the STD case when both coating and alternative fuel are used in the engine. With the established ANN models, engine performance parameters and exhaust emission parameters were predicted with 99 % and 98 % accuracy respectively.

Prediction of the Bond Strength of Externally Bonded FRP Sheets Applied to Concrete via Grooves Technique Using Artificial Neural Networks

The present study aims to fill a gap in the literature on the estimation of the bond strength of fiber reinforced polymer sheets bonded to concrete, via the externally bonded reinforcement on grooves (EBROG) technique, employing the curve-fitting on existing datasets in the literature and the methodology of Artificial Neural Networks (ANNs). Therefore, a dataset of 39 experimental results derived from EBROG technique is collected from the literature. A mathematical equation for the bond strength of FRP sheets applied on concrete via the EBROG technique was suggested using curve-fitting and general regression. The proposed mathematical equation is compared and validated with experimental results. The developed ANN model was constructed after testing diverse hidden layers and neurons to find the optimal predictions. The validation of the model is carried out using the experimental results and a statistical analysis is applied to assess the proposed mathematical equation and the proposed ANN model. Furthermore, a parametric study using the ANN model was also performed to investigate the influence of various factors on the bond strength of FRP sheets bonded to concrete. The parametric study proves that the bond strength increases with increasing the tensile stiffness per width, the FRP sheet width, and the concrete compressive strength; however, the effect of the Groove’s width and depth is found to be not monotonous.

Ultrasonic pulse velocity and artificial neural network prediction of high-temperature damaged concrete splitting strength

To examine the integrity of any structure following a fire, assessments of the impact of high temperatures on concrete are essential, particularly its decreased in tensile strength. Destructive examinations, such as the extraction of concrete cores, can pose significant cost and safety challenges, particularly when applied to structures that have already sustained damage. Consequently, for assessing damaged concrete, non-destructive in-situ tests are the favored approach. This study aims to develop an artificial neural network model utilizing data from ultrasonic pulse velocity measurements. The model's purpose is to assess the tensile splitting strength of concrete subjected to elevated temperatures, ranging from 200 to 800 °C. The splitting strength investigation showed that increasing the exposure temperature from 200 to 800°C results in splitting strength reduction of 15 to 75% respectively. Also, the ultrasonic pulse velocity experienced a reduction of 85% when the exposure temperature reaches 800 °C. In addition, the results of the artificial neural network model indicated that ultrasonic pulse velocity and temperature data were sufficient to reasonably forecast the tensile splitting strength of concrete. The developed artificial neural network model has a coefficient of determination (R2) of 0.943, a mean absolute relative error (MARE) of 5.028, and an average squared error (ASE) of 0.000907.

Artificial neural network modeling of solar photovoltaic panel energy output

Solar panel energy output is an essential parameter for the design and operation of renewable energy systems. Previously, little was known about the precise relationship between the energy outputs of solar panels with various meteorological, radiometric, and weather conditions in the southern California region. Without precise modeling or prediction systems, solar energy can potentially be wasted due to grid energy fluctuation. Thus, it is intended to use an artificial neural network (ANN) to develop solar panel energy output prediction model with a high degree of accuracy. A self-developed feedforward ANN model utilizing the Rectified linear unit (ReLu) activation function was used in the present study. Meteorological, weather, and sun irradiation data collected throughout the last year from a residential location have been used to train the models. The model’s performance was identified based on the minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R2). Further, the present self-developed ANN model was consistent with other solar energy experimental results and theoretical analysis. The developed ANN model using the Python programming language achieved a high R2 of more than 85% which ascertains the accuracy and suitability of the model to predict the daily solar energy output in local southern California area. This ANN modeling approach can be extended to many other applications such as SCORE, commercial, and residential building design.

A novel COVID-19 infection-forecasting model based on artificial neural networks

The COVID-19 surge has mostly affected people and wreaked havoc on multiple sectors of the global economy. This study uses artificial neural networks (ANN) to develop COVID-19 prediction models to minimize the perilous situation. With positive infection data, these hybrid artificial neural network models looked at COVID-19 cases in Andhra Pradesh, India. Then, COVID-19 data were divided into training and testing for simulation. The developed model that takes the previous 14 days into account outperforms the others, depending on the results. According to the developed ANN models for Andhra Pradesh districts, the prediction model that works well and yields positive results is the one that receives lower values of error metrics like MSE, RMSE, MAE, and MAPE and higher values of R2. The hybrid neural network model that considers the previous 14 days for prophecy has suggested anticipating daily positive suffering, notably in areas of Andhra Pradesh, as a result of the collected data. Linear regression, ARIMA, and LSTM have been used for model assessment. The proposed 14-day model statistically surpasses all metrics in RMSE, MAE, MAPE, and R2. This study showed that an ANN-based model can predict the COVID-19 outbreak as well as other epidemics.