Hybrid Artificial Neural Network Research Articles

Machine-learning-based performance prediction of the energy pile heat pump system

The coefficient of performance (COP) is one of the important indicators for evaluating ground source heat pump systems, and a certain amount of machine learning algorithms have been applied for COP prediction. However, most of the current models use a large number of input variables, and it may be difficult to obtain all the required parameters for diagnosis of the sensors. Most of the influencing factors contain fluid temperature, which is not easy to measure, and there is a risk of leakage when sensors are deployed in the system. In this work, two-year field tests were conducted based on an energy pile heat pump system, where in situ results were used as sample points, and the more easily measured ambient temperature and humidity, room temperature and humidity, and hourly power consumption were used as input parameters. Spearman correlation analysis is used to rank the influencing factors, and the prediction of COP was developed by a hybrid artificial neural network (ANN), optimized via the particle swarm optimization algorithm (PSO), and finally compared with empirical models and their updates. Results showed that among the input variables, the most and least important factors affecting the COP are the ambient temperature and ambient humidity, respectively. The prediction results of PSO-ANN were in the best agreement with the measured values, and the accuracy was higher than that of the empirical regression models. The proposed method noted in this study can be used as a supplement to the current COP prediction.

Hydroisomerisation and Hydrocracking of n-Heptane: Modelling and Optimisation Using a Hybrid Artificial Neural Network–Genetic Algorithm (ANN–GA)

In this paper, the focus is on upgrading the value of naphtha compounds represented by n-heptane (n-C7H16) with zero octane number using a commercial zeolite catalyst consisting of a mixture of 75% HY and 25% HZSM-5 loaded with different amounts, 0.25 to 1 wt.%, of platinum metal. Hydrocracking and hydroisomerisation processes are experimentally and theoretically studied in the temperature range of 300–400 °C and under various contact times. A feedforward artificial neural network (FFANN) based on two hidden layers was used for the purpose of process modelling. A total of 80% of the experimental results was used to train the artificial neural network, with the remaining results being used for evaluation and testing of the network. Tan-sigmoid and log-sigmoid transfer functions were used in the first and second hidden layers, respectively. The optimum number of neurons in hidden layers was determined depending on minimising the mean absolute error (MAE). The best ANN model, represented by the multilayer FFANN, had a 4–24–24–12 topology. The ANN model accurately simulates the process in which the correlation coefficient (R2) was found to be 0.9918, 0.9492, and 0.9426 for training, validation, and testing, respectively, and an average of 0.9767 for all data. In addition, the operating conditions of the process were optimised using the genetic algorithm (GA) towards increasing the octane number of the products. MATLAB® Version 2020a was utilised to complete all required computations and predictions. Optimal operating conditions were found through the theoretical study: 0.85 wt.% Pt-metal loaded, 359.36 °C, 6.562 H2/n-heptane feed ratio, and 3.409 h−1 weight-hourly space velocity (WHSV), through which the maximum octane number (RON) of 106.84 was obtained. Finally, those operating conditions largely matched what was calculated from the results of the experimental study, where the highest percentage of the resulting isomers was found with about 78.7 mol% on the surface of the catalyst loaded with 0.75 wt.% Pt-metal at 350 °C using a feed ratio of 6.5 H2/n-C7 and WHSV of 2.98 h−1.

Socio-economic correlation analysis and hybrid artificial neural network model development for provincial waste electrical and electronic equipment generation forecasting in China

Ability to forecast the waste electrical and electronic equipment (WEEE) generation can help formulate a robust future WEEE management system. Previous studies applied forecasting models, such as the grey model and artificial neural networks, to predict WEEE generation from a country perspective, leading to less accurate forecasts due to huge socio-economic differences in rural and urban areas. Additionally, there has been the incompatibility of a single forecasting model for all WEEE types, and this remained a research gap. Taking advantage of respective forecasting models, this study presents a hybrid model, Grey Artificial Neural Network, to forecast the WEEE generation of 31 province-level regions in China while evaluating the socio-economic analysis of seven WEEE types via Pearson correlation analysis. More than 70% of WEEE from province-level regions strongly correlates (R > ±0.8) with the gross domestic product and the population, whereas some top WEEE-generating province-level regions (i.e., Tianjin and Shanghai) correlate weakly to moderately. The root mean square error and mean absolute error of the developed Grey Artificial Neural Network hybrid model are the lowest at 8.29 and 6.48, compared to the grey model (13.53 and 11.13) and back-propagation neural network (9.21 and 7.22). Though the Grey Artificial Neural Network hybrid model has the lowest error, posterior mean square deviation ratio analysis indicates that this hybrid model is only suitable for washing machines, refrigerators, color televisions, and personal computers (urban area), while the back-propagation neural network is suitable for monochrome televisions, air conditioners, and personal computers (rural area). Compared to 2019, it is projected that an additional 32.92 M units of WEEE will be generated by 2025, suggesting that China should build at least 15 extra recycling centers (14% more based on 2016) to handle the increased WEEE generation. This study provides policy implications for effective WEEE monitoring and collection systems to build resilient WEEE management.

Data-driven modelling of the multiaxial yield behaviour of nanoporous metals

Nanoporous metals, built out of complex ligament networks, can be produced with an additional level of hierarchy. The resulting complexity of the structure makes modelling of the mechanical behaviour computationally expensive and time consuming. In addition, multiaxial stresses occur in the higher hierarchy ligaments. Therefore, knowledge of the multiaxial material behaviour, including the 6D yield surface, is required. Surrogate models, predicting the mechanical behaviour of the lower level of hierarchy, represented by finite element beam models, are a promising approach to overcome such challenges, when existing analytical models are not able to describe the material behaviour. Therefore, as a first step, we studied the elastic behaviour and the yield surfaces of representative volume elements with idealised diamond and Kelvin structure in finite element simulations. The yield surfaces showed pronounced anisotropy and could not be described by the Deshpande-Fleck model for isotropic solid foams. Instead, we used data-driven and hybrid artificial neural networks, as well as data-driven support vector machines and compared them regarding their potential for the prediction of yield surfaces. All considered methods were well suited and resulted in relative errors <4.5%. Support vector machines showed the best generalisation and accuracy in 6D stress space and are suitable for extrapolation outside the range of training data.

A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings

The realistic determination of damage estimation and building performance depends on target displacements in performance-based earthquake engineering. In this study, target displacements were obtained by performing pushover analysis for a sample reinforced-concrete building model, taking into account 60 different peak ground accelerations for each of the five different stories. Three different target displacements were obtained for damage estimation, such as damage limitation (DL), significant damage (SD), and near collapse (NC), obtained for each peak ground acceleration for five different numbers of stories, respectively. It aims to develop an artificial neural network (ANN)-based sustainable model to predict target displacements under different seismic risks for mid-rise regular reinforced-concrete buildings, which make up a large part of the existing building stock, using all the data obtained. For this purpose, a hybrid structure was established with the particle swarm optimization algorithm (PSO), and the network structure’s hyper parameters were optimized. Three different hybrid models were created in order to predict the target displacements most successfully. It was found that the ANN established with particles with the best position revealed by the hybrid models produced successful results in the calculation of the performance score. The created hybrid models produced 99% successful results in DL estimation, 99% in SD estimation, and 99% in NC estimation in determining target displacements in mid-rise regular reinforced-concrete buildings. The hybrid model also revealed which parameters should be used in ANN for estimating target displacements under different seismic risks.

An Improvement of Parameter Estimation Accuracy of Structural Equation Modeling using Hybridization of Artificial Neural Network in the Entrepreneurship Structural Model

Abstract In developing optimal entrepreneurship, several variables such as motivation, knowledge, intensity, and capacity are required to determine their relationship using the Partial Least Square-Structural Equation Modeling (PLS-SEM). The results show that entrepreneurial motivation and knowledge significantly affect intensity. Also, motivation and intensity significantly influenced capacity. The parameter estimator of PLS-SEM can be improved by applying hybridization to the Artificial Neural Network (PLS-ANN) using the 2:32:8:1 architecture in which motivation and intensity were the input while capacity was the output. The comparison parameter accuracy model measured by MSE, RMSE, and MAE shows the improvement accuracy by PLS-ANN better than PLS-SEM.

Power Management Systems for Harmonic Elimination Using Hybrid Artificial Neural Networks Approaches

This study proposes two neural network-based approaches for computing the degrees required to generate pulse-width modulation (PWM) signals. The conventional method of using a lookup table to determine the orientations is replaced by these ANN models, which demonstrate superior performance across a range of modification indices. The first ANN is trained to estimate the phase angle based on input from the Empire Bird Optimizer, eliminating the need for a lookup table. A separate ANN is then trained using these configurations to generate the PWM signals. Experimental data on voltage level, inverter output current, and power flow are collected using a scope and power quality tester. The recommended hybrid ANN-based selective harmonic elimination PWM effectively removes low-order vibrations. The research is implemented in the MATLAB/Simulink platform, and the results indicate that the proposed system is efficient, accurately determining firing angles with minimal repetitions, and effectively addressing local minima values. The practical implementation of an eleven-level modular multilevel converter serves as the basis for this proposal, exemplified by an eleven-level H-bridge inverter.

Accurate Estimation of PEMFC State of Health using Modified Hybrid Artificial Neural Network Models

Accurate Estimation of PEMFC State of Health using Modified Hybrid Artificial Neural Network Models

A novel integrated approach of RUNge Kutta optimizer and ANN for estimating compressive strength of self-compacting concrete

In this study machine learning models are used that forecast the compressive strength of self-compacting concrete (SCC) depending on percentage replacement of cement by supplementary cementitious material. In order to predict the mechanical property of SCC, a hybrid artificial neural network (ANN) along with metaheuristic optimization techniques and two traditional models were employed which comprised of 300 datasets. Several input factors are employed in the modelling process, including cement, the water-binder ratio, coarse aggregate, fine aggregate, fly ash (FA) and the superplasticizer. ANN hybridised with RUNge Kutta optimizer (RUN) was chosen as one of the top predictive models based on coefficient of determination ((R2 = 0.933 (in training) and R2 = 0.9203 (in testing))) findings and model validation. According to the rank analysis, the ANN-RUN surpassed other models with a score of 128. Permutation feature importance, statistical analysis, and comparisons amongst regression models are used to determine the models' efficacy. The best input parameters were selected based on person correlation and density plot. Also, the use of Taylor diagrams and accuracy matrices was also examined for the visual comprehension of the data which has shown similar results as discussed above. The results show that the suggested machine learning models attained consistent accuracy and can be used as a robust technique to predict the compressive strength of SCC.

Why is Ghana losing the war against illegal gold mining (Galamsey)? An artificial neural network-based investigations.

Ghana, like most resource-rich countries, is saddled and inundated with resource curse challenges. Key among them is the problem of illegal small-scale gold mining activities (ISSGMAs), which is mercilessly robbing the nation of its ecological integrity, despite attempts by successive governments to remedy the situation. In the midst of this challenge, Ghana, year in and year out, performs abysmally on environmental governance score (EGC) variables. Against this framework, this study intends to uniquely establish the drivers behind Ghana's failure to overcome ISSGMAs. To achieve this, a total of 350 respondents were sampled through a structured questionnaire, with a mixed method approach from selected host communities, believed to be the epicenters of ISSGMAs in Ghana. The questionnaires were administered from March to August, 2023. AMOS Graphics and IBM SPSS vs 23 were used to analyze the data. In particular, the novel hybrid artificial neural network (ANN) and linear regression techniques were adopted to establish the relational linkages among the constructs of the study and their respective contribution to ISSGMAs in Ghana. The study displays intriguing results that explain why Ghana has failed to be victorious over ISSGMAs. In particular, the findings of the study demonstrate that the three key drivers of ISSGMAs in Ghana, in a sequential and consecutive order are as follows: bureaucratic licensing regime/weak legal framework, political/traditional leadership failures, and corrupt institutional officials. Moreover, socioeconomic factors and proliferation of foreign miners/mining equipment were also observed to contribute significantly to ISSGMAs. While the study contributes to the ongoing debate on ISSGMAs, it also proffers valuable and practical solutions to the menace as well as theoretical implications.

A Novel Hybrid Artificial Bee Colony-Based Deep Convolutional Neural Network to Improve the Detection Performance of Backscatter Communication Systems

Backscatter communication (BC) is a promising technology for low-power and low-data-rate applications, though the signal detection performance is limited since the backscattered signal is usually much weaker than the original signal. When the detection performance is poor, the backscatter device (BD) may not be able to accurately detect and interpret the incoming signal, leading to errors and degraded communication quality. This can result in data loss, slow data transfer rates, and reduced reliability of the communication link. This paper proposes a novel approach to improve the detection performance of backscatter communication systems using evolutionary deep learning. In particular, we focus on training deep convolutional neural networks (DCNNs) to improve the detection performance of BC. We first develop a novel hybrid algorithm based on artificial bee colony (ABC), biogeography-based optimization (BBO), and particle swarm optimization (PSO) to optimize the architecture of the DCNN, followed by training using a large set of benchmark datasets. To develop the hybrid ABC, the migration operator of the BBO is used to improve the exploitation. Moving towards the global best of PSO is also proposed to improve the exploration of the ABC. Then, we take advantage of the proposed deep architecture to improve the bit-error rate (BER) performance of the studied BC system. The simulation results demonstrate that the proposed algorithm has the best performance in training the benchmark datasets. The results also show that the proposed approach significantly improves the detection performance of backscattered signals compared to existing works.

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

AbstractNowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

Traffic flow modelling of long and short trucks using a hybrid artificial neural network optimized by particle swarm optimization

The significance of intelligent transportation systems and artificial intelligence in road transportation networks has made the prediction of traffic flow a subject of discussion among transportation engineers, urban planners, and researchers in the last decade. However, limited research has been done on traffic flow modelling of long and short trucks considering that they are among the major causes of traffic congestions and traffic-related accidents on freeways, especially freeway collisions between them and passengers’ vehicles. This study focused on the traffic flow of long and short trucks on the N1freeway in South Africa due to its high traffic volume and persistent traffic congestions caused by trucks. We obtained traffic data from this freeway using inductive loop detectors and video cameras. Traffic flow variables such as speed, time, traffic density, and traffic volume were identified, and the traffic datasets comprising 920 datasets were divided into 70% for training and 30% for testing. A hybrid ANN-PSO model was used in modelling the truck traffic flow due to its ability to converge to optimization quickly. The PSO's features (accelerating factors and number of neurons) assist in evaluating traffic flow conditions (traffic flow, traffic density, and vehicular speed). Also,PSO algorithms are simple and require few adjustment parameters. The results suggest that the ANN-PSO model can model long and short trucks traffic flow with a R2 training and testing of 0.999 0and0.993 0. This is the first study to undertake a longitudinal analysis of traffic flow modelling of long and short trucks on a freeway using a metaheuristic algorithm (ANN-PSO). The results of this study will provide knowledgeable insights (division of traffic flow variables and analysing of traffic flow data) to transportation planners and researchers when it comes to minimizing truck-related accidents and traffic congestions on freeways.

Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model.

The combination of rice husk ash and common concrete both reduces carbon dioxide emission and solves the problem of agricultural waste disposal. However, the measurement of the compressive strength of rice husk ash concrete has become a new challenge. This paper proposes a novel hybrid artificial neural network model, optimized using a reptile search algorithm with circle mapping, to predict the compressive strength of RHA concrete. A total of 192 concrete data with 6 input parameters (age, cement, rice husk ash, super plasticizer, aggregate, and water) were utilized to train proposed model and compare its predictive performance with that of five other models. Four statistical indices were adopted to evaluate the predictive performance of all the developed models. The performance evaluation indicates that the proposed hybrid artificial neural network model achieved the most satisfactory prediction accuracy regarding R2 (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451). The proposed model also had better predictive accuracy than that of previously developed models on the same data. The sensitivity results show that age is the most important parameter for predicting the compressive strength of RHA concrete.

Optimization of fermentation medium components by response surface methodology (RSM) and artificial neural network hybrid with genetic algorithm (ANN-GA) for lipase production by Burkholderia cenocepacia ST8 using used automotive engine oil as substrate

Bioconversion of used automotive engine oil (UEO) into lipase was conducted via submerged fermentation by Burkholderia cenocepacia ST8, as a strategy for value-added product generation and waste management. Response surface methodology (RSM) and artificial neural network hybrid with genetic algorithm (ANN-GA) were employed to optimize the fermentation medium for enhancing extracellular lipase production by B. cenocepacia ST8. Employing a four-factor-five-level central composite rotatable experimental design (CCRD), a reduced quartic polynomial RSM model and ANN model (4-4-1) trained using Bayesian Regularization were developed to attain the optimized fermentation medium for maximum level of lipase production. The RSM model predicted the following as the optimum media constituents: 2.28% v/v of Tween 80, 2.26% v/v of UEO, 0.79% w/v of nutrient broth, and 1.33% w/v of gum arabic, with an actual lipase yield of 216 U/mL. While, ANN-GA predicted the optimum media constituents to be 3% v/v of Tween 80, 3% v/v of UEO, 0.72% w/v of nutrient broth, and 3.38% w/v of gum arabic, with actual lipase yield of 225 U/mL. In comparison to the unoptimized medium, optimized RSM and ANN-GA systems both demonstrated a 1.6-fold increment in lipase production. Tween 80 and nutrient broth concentrations were the most important variables influencing the lipase production. The findings of this study indicated that the ANN-GA and RSM could be useful for effective optimization of the fermentation medium for enzyme production.

Application of hybrid ANN paradigms built with nature inspired meta-heuristics for modelling soil compaction parameters

For monitoring soil compaction in civil engineering projects, the determination of optimum moisture content (OMC) and maximum dry density (MDD) are very significant. The present method of determining the OMC and MDD through laboratory testing is a time-consuming task and requires skilled workmanship. Thus, in order to replace the conventional approach of OMC and MDD determination in the laboratory, this study presents a hybrid intelligence model of artificial neural network (ANN) and grey wolf optimizer (GWO). These two computational intelligence techniques collaborate to construct the ANN-GWO model for estimating OMC and MDD of soils. A database of 126 experimental results with six influencing factors was employed in the current work. To compare the outcomes of the developed ANN-GWO model, four additional hybrid ANNs were built utilizing particle swarm optimization, slime mould algorithm, Harris hawks optimization, and salp swarm algorithm. Additional laboratory experiments were conducted to investigate the generalization ability of the constructed ANNs. Results for the testing dataset indicate that the ANN-GWO achieves the best accurate estimation in cases of OMC (RMSE = 0.0986, PFI = 1.2569, and R2 = 0.7273) and MDD (RMSE = 0.1017, PFI = 1.2318, and R2 = 0.7147) of soils. The ANN-GWO model yields superior results than other hybrid ANNs constructed in this work. Overall, the suggested ANN-GWO can assist geotechnical engineers to estimate the OMC and MDD of soils in civil engineering works. The developed MATLAB models are also attached as a supplementary material which can readily be used to determine soil compaction parameters along with their ranges for different soil types.

A Genetic Algorithm Optimized ANN for Prediction of Exergy and Energy Analysis Parameters of a Diesel Engine Different Fueled Blends

In this research, a hybrid artificial neural network (ANN) optimized by a genetic algorithm (GA) was used to estimate energy and exergy analyses parameters. This article presents an approach for estimating energy and exergy analyses parameters with optimized ANN model based on GA (GA-ANN) for different ternary blends consisting of diesel, biodiesel and bioethanol in a single-cylinder, water-cooled diesel engine. The data used in the experiments performed at twelve different engine speeds between 1000 and 3000 rpm with 200 rpm intervals for five different fuel mixtures consisting of fuel mixtures prepared by blends biodiesel, diesel and 5% bioethanol in different volumes constitute the input data of the models. Using these input data, engine torque (ET), amount of fuel consumed depending on fuels and speed (AFC), carbon monoxide emission values (CO), carbon dioxide emission values (CO2), hydrocarbon emission values (HC), nitrogen oxides emission values (NOx), the amount of air consumed (AAC), exhaust gas temperatures (EGT) and engine coolant temperatures (ECT) were estimated with the GA-ANN. In examining the results obtained were examined, it was proved that diesel, biodiesel and bioethanol blends were effective in predicting all the results mentioned in engine studies performed at 200 rpm intervals in the 1000-3000 rpm range. A standard ANN model used in the literature was also proposed to measure the prediction performance of GA-ANN model. The predictive results of both models were compared using various performance indices. As a result, it was revealed that the proposed GA-ANN model reached higher accuracy in estimating the exergy and energy analyses parameters of the diesel engine compared to the standard ANN technique.

Factors Affecting the Acceptance of E-learning by Learners in the Context of the Covid-19 Pandemic: A Hybrid Artificial Neural Network - SEM Method

This research aims to determine the factors affecting the student’s intention of E-learning adoption in the context of the Covid-19 pandemic in Vietnam. An online survey was used to assess the proposed psychological determinants of E-learning adoption. Confirmatory factor analysis and structural equation modeling were conducted on the collected data (n=310) using the SPSS 20 and the AMOS 24 statistical software. The reliability and validity of the measurement were examined via Cronbach’s alpha, EFA, CFA while the strength and direction of the hypothesized causal paths among the constructs were analyzed via SEM. Finally, the results from SEM were used as the inputs for an Artificial Neural Network (ANN) model to predict acceptance factors. The results of the study indicated that there was a significant positive relationship between the attitude toward risk (Covid-19), PU, PE and the intention of E-learning adoption. Furthermore, combining SEM and neural networks enabled the capture of linear and complex nonlinear relationships between predictors and the dependent variables.

Optimization of capacitance in supercapacitors by constructing an experimentally validated hybrid artificial neural networks-genetic algorithm framework

Supercapacitors are high power electrochemical energy storage systems that are attractive candidates for use in high power applications. Yet, their widespread adoption has been restricted due to their relatively low energy density. Improving the energy storage performance of supercapacitors is linked to rationally optimizing the key descriptors that affect capacitance. This work presents a systematic approach based on a hybrid artificial neural network (ANN) and genetic algorithm (GA) integrated with the Big M method to efficiently and rationally design carbon-based supercapacitors with improved energy storage performance. By performing structural and electrochemical characterization on systems we fabricate, we experimentally validate the robustness and generalizability of the developed ANN-GA framework. This study takes a step towards the rational design of supercapacitors by implementing the hybrid ANN-GA framework as an optimization tool to provide guidelines for rationally tuning material properties and operational conditions for improved capacitance.

Machine Learning Method Based on Symbiotic Organism Search Algorithm for Thermal Load Prediction in Buildings

This research investigates the efficacy of a proposed novel machine learning tool for the optimal simulation of building thermal load. By applying a symbiotic organism search (SOS) metaheuristic algorithm to a well-known model, namely an artificial neural network (ANN), a sophisticated optimizable methodology is developed for estimating heating load (HL) in residential buildings. Moreover, the SOS is comparatively assessed with several identical optimizers, namely political optimizer, heap-based optimizer, Henry gas solubility optimization, atom search optimization, stochastic fractal search, and cuttlefish optimization algorithm. The dataset used for this study lists the HL versus the corresponding building conditions and the model tries to disclose the nonlinear relationship between them. For each mode, an extensive trial and error effort revealed the most suitable configuration. Examining the accuracy of prediction showed that the SOS–ANN hybrid is a strong predictor as its results are in great harmony with expectations. Moreover, to verify the results of the SOS–ANN, it was compared with several benchmark models employed in this study, as well as in the earlier literature. This comparison revealed the superior accuracy of the suggested model. Hence, utilizing the SOS–ANN is highly recommended to energy-building experts for attaining an early estimation of the HL from a designed building’s characteristics.