Accuracy Of Artificial Neural Network Research Articles

Strategic Selection of Machine Learning Models for Short-term Trading Optimization

Stock market prediction is a key aspect for a financial analyst and investor, as it facilitates better decisions and improved investment strategies. Towards this end, this project compares how machine learning (ML) models perform in predicting stock market prices with relatively small dataset sizes so as to get the best performing one. This study evaluates the efficiency and accuracy of artificial neural networks, support vector regression, LSTM and decision trees in capturing market trends and forecasting stock movements. The importance of our proposed paper lies in its potential to demystify the complexities of financial markets for students and new entrants in the field of finance. Participants can garner practical insights on how the stock market operates and the application of theoretical ML concepts to financial analytics by identifying the best-performing ML models with limited datasets, which is a common occurrence in real-life situations. This paper aims to analyse the commonly present machine learning models in stock market prediction and choose the most effective model depending upon their performance, which is achieved using limited information (short term data). This would provide an edge in taking small (less-risky) informed financial decisions without extensive study of the stock market.

Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review

High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.

Artificial neural network and discrete wavelet transform for inter-turn short circuit and broken rotor bars faults diagnosis under various operating conditions

Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine. References 31, table 1, figures 9.

Predicted risk factors associated with secondary infertility in women: univariate and multivariate logistic regression analyses.

Infertile women are those who regularly engage in unprotected intercourse for a period of at least 1 year and are unable to become clinically pregnant. Primary infertility means the inability of couples to conceive, without any previous successful pregnancies. Secondary Infertility refers to the inability to get pregnant for 12 months, after having a previous pregnancy for one time at least. The objectives of the current study were to analyze risk factors for secondary infertility and compare the predictive accuracy of artificial neural network (ANN) and multiple logistic regression models. The study was conducted at The University Institute of Public Health collecting data from Gilani Ultrasound Center 18 months after approval of synopsis. A total of 690 women (345 cases and 345 controls) were selected. The women selected for the case group had to be 20-45 years of age, had any parity, and had a confirmed diagnosis of secondary infertility. Multiple logistic regression (MLR) and ANN were used. The chance of secondary infertility was 2.91 times higher in women living in a joint family [odds ratio (OR) = 2.91; 95% confidence interval (CI) (1.91, 4.44)] and was also 2.35 times higher for those women who had relationship difficulties with their husband [OR = 2.35; 95% CI (1.18, 4.70)]. Marriage at an earlier age was associated with secondary infertility with β being negative and OR being < 1 [OR = 0.94; 95% CI (0.88, 0.99)]. For the logistic regression model, the area under the receiver operative characteristic curve (ROC) was 0.852 and the artificial neural network was 0.87, which was better than logistic regression. Identified risk factors of secondary infertility are mostly modifiable and can be prevented by managing these risk factors.

Hybrid AI modeling techniques for pilot scale bubble column aeration: A comparative study

With increased accessibility of process data from the production lines in chemical and biochemical production plants, the use of data-based modeling methods is gaining interest. In this work, three different data-based modeling approaches are applied for modeling aeration in a pilot scale bubble column. In all three modeling approaches the same serial hybrid-model structure is used, combining a species conservation balance based on first-principles with different data-based models for the overall volumetric mass transfer coefficient (KLa). Simple empirical correlations with parameters fit to process data provide transparent models but lack the accuracy of Artificial Neural Networks (ANNs). ANNs provide models with high accuracy within the operation regimes used for training, however, the models are prone to overfitting, and their black-box nature results in models that are difficult to interpret. As an alternative, a symbolic regression-inspired technique is used for discovering symbolic equations, resulting in interpretable models with accuracy that is comparable to that of the ANN.

Differences in running technique between runners with better and poorer running economy and lower and higher milage: An artificial neural network approach.

Prior studies investigated selected discrete sagittal-plane outcomes (e.g., peak knee flexion) in relation to running economy, hereby discarding the potential relevance of running technique parameters during noninvestigated phases of the gait cycle and in other movement planes. Investigate which components of running technique distinguish groups of runners with better and poorer economy and higher and lower weekly running distance using an artificial neural network (ANN) approach with layer-wise relevance propagation. Forty-one participants (22 males and 19 females) ran at 2.78 m∙s-1 while three-dimensional kinematics and gas exchange data were collected. Two groups were created that differed in running economy or weekly training distance. The three-dimensional kinematic data were used as input to an ANN to predict group allocations. Layer-wise relevance propagation was used to determine the relevance of three-dimensional kinematics for group classification. The ANN classified runners in the correct economy or distance group with accuracies of up to 62% and 71%, respectively. Knee, hip, and ankle flexion were most relevant to both classifications. Runners with poorer running economy showed higher knee flexion during swing, more hip flexion during early stance, and more ankle extension after toe-off. Runners with higher running distance showed less trunk rotation during swing. The ANN accuracy was moderate when predicting whether runners had better, or poorer running economy, or had a higher or lower weekly training distance based on their running technique. The kinematic components that contributed the most to the classification may nevertheless inform future research and training.

Technology Focus: Artificial Lift (March 2024)

The industry is now in its second year beyond the COVID-19 pandemic, and the workforce has changed in many ways, but issues facing production performance remain. Major conferences saw 116 submissions from 28 countries, with 64% regarding electrical submersible pumps (ESPs), and 18% and 8% devoted to gas lift and rod lift, respectively. Many of the papers dealt with case history successes. That is all well and good, but it doesn’t necessarily drive reader interest if it can’t be applied to their asset. The top three papers were selected on the guidelines of clarity of the abstract in addressing the scope, methods/procedures/process, results/observations/conclusions, and novel or additive information. The papers described here focus on novel unconventional horizontal downhole pump card signatures, carbon reduction through alternative energy to pump wells, and predicting downhole-discharge pressure of ESPs. Paper SPE 214723 presents a discussion of the design, evaluation, and field testing of a new type of mechanical gas separator with what the authors term a “hydrohelical” design. The separator, in Permian Basin tests, almost doubled the flow capacity of conventional gas separators with a high level of efficiency. The authors draw particular attention to the improvement of ESP operations under multiphase conditions. Carbon reduction is a hot topic, and paper OTC 32671 does not disappoint as it looks at the use of a 10-MW floating solar photovoltaic power system to meet the energy needed for 10 ESP wells on an oil platform in the Brazil equatorial region. Collaboration with the National Renewable Energy Laboratory and its System Advisor Model was used with extensive meteorological and wind data to examine the sustainability of green energy use for the petroleum industry and offer valuable guidance for future renewable energy projects in regions with similar characteristics. Finally, paper SPE 216598 comes from Sudan and Saudia Arabia and focuses on digital solutions for the challenge of knowing the downhole-discharge pressure of an ESP system. Most ESP systems do not have downhole gauges, so an artificial neural network (ANN) approach is used to predict the ESP discharge pressure for performance optimization. Forty wells and more than 12,000 data points were reduced to 16 inputs plus one hidden layer of seven neurons to provide a high degree of accuracy in prediction of discharge pressure. The authors state that the ability to accurately predict discharge pressure can lead to the early detection of possible anomalies, which can prevent costly failures and reduce downtime. Artificial intelligence technology may improve the accuracy of ANNs, but, for now, ANNs are worth a look to obtain much-needed ESP discharge pressure. Other notable papers dealt with gas separation, ESP reliability, and the increasing challenges of low rates, low pressures, and high gas fractions. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214913 Solved: ESP Shutdowns and Restart Issues—Novel Pump Protection and Active 3D Dispersion System Improve ESP Performance in Multiple Wells by A. Bhattacharjee, SLB, et al. SPE 215474 A New Artificial Lift Approach in Telisa Low-Productivity Reservoir: Utilized Very Low-Rate ESP by Agus Aryanto, Medco E&amp;P Indonesia, et al. SPE 215170 A Comprehensive Study of a Novel Submersible Pump by Hattan Banjar, Saudi Aramco, et al. SPE 214722 Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence and Physics-Based Hybrid Models by Shejuti Silvia, Baker Hughes, et al. SPE 214733 Gas-Flow-Management Technology Designed To Decrease Downtime and Improve ESP Efficiency by S. Fulwider, ConocoPhillips, et al.

Stepwise Regression for Increasing the Predictive Accuracy of Artificial Neural Networks: Applications in Benchmark and Advanced Problems

Stepwise Regression for Increasing the Predictive Accuracy of Artificial Neural Networks: Applications in Benchmark and Advanced Problems

Optimization of GFRP-concrete-steel composite column based on genetic algorithm - artificial neural network

Genetic algorithms (GA) and artificial neural networks (ANN) as intelligent algorithms have been widely applied in engineering fields such as structural design. The GFRP-concrete-steel composite column (GCS), known for its corrosion resistance, has garnered significant attention in its design. However, the application of GA and ANN methods in the context of GCS has primarily been confined to enhancing the predictive accuracy of ANN. This paper introduces a novel approach for the structural optimization of GCS using GA and ANN. A total of 1050 finite element models were generated and analyzed using ANSYS for the purpose of training ANN. The validity of these finite element models was confirmed through comparative analysis with experimental data. The trained ANN was then utilized to predict the loading capacity of GCS for each generation in the GA. By collecting pricing data for GFRP tubes, steel tubes, and concrete under various parameters, pricing fitting formulas for the three materials were derived. Subsequently, the cost of GCS under different parameter sets was calculated. The maximization of the ratio of GCS loading capacity to its cost (LP) was established as the optimization objective. Through the iterative optimization process of the GA-ANN algorithm, the structural optimization was achieved to maximize the economic efficiency of GCS. Comparing the optimization results with numerical simulation results showed that errors were mostly contained within 10 % or even as low as 5 %, thereby validating the accuracy of the GA-ANN algorithm. The applicability of the GA-ANN algorithm to GCS structural optimization was demonstrated by altering a single concrete variable. The method proposed in this paper can be effectively applied to the structural design and optimization of composite columns with a sandwich structure, such as GCS.

Artificial neural networks outperform linear regression in estimating 9-month patient-reported outcomes after upper extremity fractures with increasing number of variables.

To compare performance between linear regression (LR) and artificial neural network (ANN) models in estimating 9-month patient-reported outcomes (PROs) after upper extremity fractures using various subsets of early mental, social, and physical health variables. We studied 734 patients with isolated shoulder, elbow, or wrist fracture who completed demographics, mental and social health measures, and PROs at baseline, 2-4 weeks, and 6-9 months postinjury. PROs included 3 measures of capability (QuickDASH, PROMIS-UE-PF, PROMIS-PI) and one of pain intensity. We developed ANN and LR models with various selections of variables (20, 23, 29, 34, and 54) to estimate 9-month PROs using a training subset (70%) and internally validated them using another subset (15%). We assessed the accuracy of the estimated value being within one MCID of the actual 9-month PRO value in a test subset (15%). ANNs outperformed LR in estimating 9-month outcomes in all models except the 20-variable model for capability measures and 20-variable and 23-variable models for pain intensity. The accuracy of ANN versus LR in the primary model (29-variable) was 83% versus 73% (Quick-DASH), 68% versus 65% (PROMIS-UE-PF), 66% versus 62% (PROMIS-PI), and 78% versus 65% (pain intensity). Mental and social health factors contributed most to the estimations. ANNs outperform LR in estimating 9-month PROs, particularly with a larger number of variables. Given the otherwise relatively comparable performance, aspects such as practicality of collecting greater sets of variables, nonparametric distribution, and presence of nonlinear correlations should be considered when deciding between these statistical methods.

Influence of the ANN Hyperparameters on the Forecast Accuracy of RAC's Compressive Strength.

The artificial neural networks (ANNs)-based model has been used to predict the compressive strength of concrete, assisting in creating recycled aggregate concrete mixtures and reducing the environmental impact of the construction industry. Thus, the present study examines the effects of the training algorithm, topology, and activation function on the predictive accuracy of ANN when determining the compressive strength of recycled aggregate concrete. An experimental database of compressive strength with 721 samples was defined considering the literature. The database was used to train, validate, and test the ANN-based models. Altogether, 240 ANNs were trained, defined by combining three training algorithms, two activation functions, and topologies with a hidden layer containing 1-40 neurons. The ANN with a single hidden layer including 28 neurons, trained with the Levenberg-Marquardt algorithm and the hyperbolic tangent function, achieved the best level of accuracy, with a coefficient of determination equal to 0.909 and a mean absolute percentage error equal to 6.81%. Furthermore, the results show that it is crucial to avoid the use of overly complex models. Excessive neurons can lead to exceptional performance during training but poor predictive ability during testing.

Classifying Alzheimer’s disease and normal subjects using machine learning techniques and genetic-environmental features

Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated. A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features for multilayer ANN modeling. Random Forest (RF) and Support Vector Machine with RBF kernel (SVM) were also employed for comparison. Model results were verified using traditional statistics. The ANN achieved high accuracy (0.98), sensitivity (0.95), and specificity (0.96) in the intrinsic test for AD classification. Excluding age and genetic data still yielded favorable results (accuracy: 0.97, sensitivity: 0.94, specificity: 0.96). The assigned weights to ANN features highlighted the importance of mental evaluation, years of education, and specific genetic variations (CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650) for AD classification. Receiver operating characteristic analysis revealed AUC values of 0.99 (intrinsic test), 0.60 (TWB-GWA), and 0.72 (CG-WGS), with slightly lower AUC values (0.96, 0.80, 0.52) when excluding age in ANN. The performance of the ANN model in AD classification was comparable to RF, SVM (linear kernel), and SVM (RBF kernel). The ANN model demonstrated good sensitivity, specificity, and accuracy in AD classification. The top-weighted SNPs for AD prediction were CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650. The ANN model performed similarly to RF and SVM, indicating its capability to handle the complexity of AD as a disease entity.

Prediction of entire thermal degradation process of polymethyl methacrylate infiltrated with kerosene by a modified artificial neural network

AbstractPredicting the entire thermal degradation process of solid combustibles infiltrated with flammable liquids is a challenge at present. In the current study, a novel artificial neural network (ANN) framework containing data preprocessing, data normalization and data transformation is proposed to predict the entire thermal degradation process of polymethyl methacrylate infiltrated with kerosene at three scenarios: (1) fixed kerosene mass fraction with various heating rates, (2) fixed heating rate with various kerosene mass fractions, and (3) various kerosene mass fractions with various heating rates. The entire thermal degradation process of scenario (1) can be accurately predicted using the ANN with 2‐4‐2‐1 topology. Using the data transformation formula exp(x1(1 + x2) to generate a new input variable based on temperature and kerosene mass fraction, a new ANN with 3‐4‐2‐1 topology can accurately predict the entire thermal degradation process of scenario (2). Two new input variables are generated using the data transformation formula 1/(1 + log((1 + x1)(1 + x2))) based on two data sets: (1) kerosene mass fraction and temperature, and (2) heating rate and temperature. The new ANN with 5‐4‐2‐1 topology can accurately predict the entire thermal degradation process at all three scenarios. The new ANN with Levenberg–Marquardt training function and Tanh activation function possesses the best prediction performance.Highlights Data preprocessing can significantly improve the prediction accuracy of ANN. ANN with a suitable hidden layer structure has high prediction accuracy. The new ANN can predict the entire pyrolysis process in various scenarios. ANN with LM and Tanh has the highest prediction accuracy.

An upscaling minute-level regional photovoltaic power forecasting scheme

Along with the increasing penetration of photovoltaic (PV) power generation, regional power forecasting becomes more and more critical for stable and economical operation of power system. The key challenge of regional PV power forecasting technology is the lack of complete and accurate historical power data since not all PV plants are equipped with the precise real-time output power monitoring system. Besides, the computation burden will be heavy when the number of PV plants in the target region is large. This paper therefore proposes an upscaling minute-level regional PV power forecasting scheme using the data of the selected reference PV plants. In this paper, a novel method of reference PV plants selection is proposed by comprehensively considering the prediction accuracy of artificial neural network (ANN) as well as Pearson correlation coefficient. The reference PV plant selection coefficient μ is introduced as the comprehensive indicator for reference PV plant selection, which incorporates Pearson correlation coefficient and MAPE. In addition, a PV output power correction method is assumed to guarantee the proper operation of regional power forecasting. Besides, this paper proposes a flexible approach to effectively decrease the accumulated error of rolling forecasting by integrating the forecasting results under different temporal resolutions. In specific, the power forecasting results in temporal resolutions of 1 min, 5 min and 15 min are simultaneously derived and the performance between the traditional rolling forecasting and the proposed method is compared. The validity of the proposed method is finally verified using the collected historical power data of PV plants installed in a city of Eastern China. For time resolution of 1 min, 5 mins and 10 mins, the corresponding RMSE are 6.56, 5.73 and 4.85 and corresponding MAPE are 4.04%, 3.45% and 2.86%.

Prediction of maximum temperature of fluid-lubricated bearing based on machine learning algorithm

The fluid-lubricated bearing, whose maximum temperature is one of the key indicators to determine whether major equipment operates normally, is widely used as the bearing part of major equipment. For temperature prediction, numerical calculation method is commonly used, but not the most accurate method. In this work, a comprehensive test rig for the sliding bearing is used to study the influences of journal linear speed, bearing specific pressure, lubricant viscosity and inlet temperature on bearing temperature. Three machine learning algorithms with different complexity, artificial neural network (ANN), k nearest neighbors (KNN) and random forest (RF) were applied to the test data to predict the maximum temperature. Furthermore, the optimal algorithm was obtained and validated. The results show that the bearing temperature is nonlinearly positively correlated with the four operating parameters. The three algorithms have satisfactory accuracy in the low-to-medium temperature range (20 °C–72 °C), but the accuracy of ANN and RF is significantly reduced in the high temperature region (72 °C–95 °C). It is found that KNN is the optimal algorithm because of its high precision and insensitivity to abnormal data. This study provides a relatively accurate and effective method for quickly and accurately predicting the maximum temperature of fluid-lubricated bearings.

Occupancy estimation using IoT sensors and machine learning: Incorporating ventilation system operating state and preprocessed differential pressure data

Indoor occupancy should be measured to reduce energy consumption in buildings and predict infection risk. Various studies used machine learning to measure occupancy using carbon dioxide concentration, Wi-Fi probes, and camera image data. The carbon dioxide concentration depends on factors such as whether doors and windows are open and whether ventilation systems are running. Carbon dioxide concentrations fluctuate depending on variables, even with the same occupants in the room. We estimated the number of occupants using indoor environmental data for considering the changes in the carbon dioxide concentration caused by these variables. The differential pressure and operating state of a ventilation system were used as input variables for machine learning. Data were acquired from a living lab, which is used for seminars, lectures, etc. For up to 50 people. We used 85,366 data for training and 8418 data for validation without training. The accuracies of Artificial Neural Network (ANN), random forest, and support vector machine models were compared for different input variables. This study considered the carbon dioxide concentration fluctuation due to leakage as an input variable coupled with differential pressure sensor data. We compared whether the accuracy increases when differential pressure data are used. In the ANN model, the lowest root mean squared error is obtained when the carbon dioxide concentration, differential pressure data, and operating state of the ventilation system are used as input variables. In the future, we plan to increase the accuracy by utilizing various Internet of Things sensors and adopt diversify the input variables.

Mapping sugarcane residue burnt areas in smallholder farming systems using machine learning approaches

Satellite remote sensing methods have been proven to quantify the burnt areas resulting from different fire activities reliably. However, they were reported to perform poorly to identify crop residue burnt areas, particularly in smallholder systems due to lack of frequent and high spatial resolution satellite data. In this study, we used Harmonized Landsat Sentinel-2 (HLS) observations and evaluated two machine learning classifiers (i.e., Support Vector Machines (SVM) and Artificial Neural Networks (ANN)) to map sugarcane burnt areas for the 2019–20 season in a smallholder farming region in Thailand.Results showed that both classifiers performed well in identifying the spatial patterns of sugarcane burnt areas in the region. The ANN outperformed SVM at both pixel and regional scales. At pixel level, ANN accuracy was 93.4 % while SVM's best-performing Polynomial kernel accuracy was 82.7 %. The ANN estimated average percent burnt area (51.1 %) in the region was closer to reported value (48.7 %) by Thailand's Office of Cane and Sugar Board (OCSB), compared to the SVM estimate (62.9 %). The total estimated burnt areas by ANN and SVM (315 and 418 thousand ha, respectively) deviated more from OCSB's area (240 thousand ha) compared to percent burnt area. However, area estimates from classifiers had significantly better accuracy than the estimates of MODIS burnt products.Overall, this study demonstrated that HLS observations provided required spectral information to build promising models to map burnt areas in smallholder systems with higher accuracy than global products. Our mapping algorithm using the ANN classifier showed the potential to monitor sugarcane burnt areas reliably, and contribute to the successful implementation of regulatory policies in Thailand.

Long-term performance prediction of ground source heat pump system based on co-simulation and artificial neural network

Ground source heat pump (GSHP) systems are efficient and widely used form of shallow geothermal energy utilization. In practical application, GSHP systems face the problem in predicting the long-term performance and energy efficiency variations in GSHP systems in regions with thermal imbalances. In this study, a coupling heat transfer model of vertical ground heat exchanger (GHE), heat pump, and building load was established to achieve refined simulation calculations of the long-term operation of GSHP systems. Parametric modeling was conducted by employing the co-simulation of COMSOL and MATLAB, enabling dynamic simulations of 600 sets of GSHP system models throughout the entire year. A database was established by extracting the numerical results, and a prediction model was developed using artificial neural network (ANN) methodology to learn from the database. The model predicted the ground temperature, seasonal energy efficiency ratio (SEER), seasonal coefficient of performance (SCOP), annual electricity consumption, and annual energy savings of the GSHP system and the prediction model was valid. The results were used to estimate the ground temperature, coefficient of performance (COP), payback period for static investment, and carbon dioxide emission reduction. Finally, the prediction model was applied to a GSHP system engineering project in Yancheng, China, forecasting the operation performance of the system for 10 years. The accuracy of ANN in predicting 10 years of long-term performance is verified.

Artificial Intelligence Application in Bioethanol Production

Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a complex process of known challenges in the area of handling, optimizing, and future forecasting. The existence of modelling techniques like artificial intelligence (AI) is, therefore, necessary in the design, handling, and optimization of bioethanol production. The flexibility and high accuracy of artificial neural network (ANN), a machine learning technique, to solve intricate processes is beneficial in modelling pretreatment, fermentation, and conversion stages of a bioethanol production system. This paper reviews various AI techniques in bioethanol production giving emphasis on published articles in the past decade.

Predicting stream water temperature with artificial neural networks based on open‐access data

AbstractPredictions of stream water temperature are an important tool for assessing potential impacts of climate warming on aquatic ecosystems and for prioritizing targeted adaptation and mitigation measures. Since predictions require reliable baseline data, we assessed whether open‐access data can serve as a suitable resource for accurate and reliable water temperature prediction using artificial neural networks (ANNs). For this purpose, we trained and tested ANNs in 16 small () headwater streams of major types located in Bavaria, Germany. Between four and eight different combinations of input parameters were trained and tested for each stream ANN, based on data availability. These were air temperature (mean, minimum and maximum), day of the year, discharge, water level and sunshine duration per day. We found that the input combination with the highest accuracy (lowest RMSE) was stream‐specific, suggesting that the optimal input combination cannot be generalized across streams. Using a reasonable, but random, input combination resulted in an increase in error (RMSE) of up to &gt;100% compared to the stream‐specific optimal combination. Hence, we conclude that the accuracy of water temperature prediction strongly depends on the availability of open‐access input data. We also found that environmental parameters such as hydrological characteristics and the proportion of land use in the 5 m riparian strip and the entire catchment are important drivers, affecting the accuracy and reliability of ANNs. ANNs' prediction accuracy was strongly negatively related to river length, total catchment area and water level. High proportions of semi‐natural and forested land cover correlated with a higher accuracy, while open‐canopy land use types such as grassland were negatively associated with ANN accuracy. In conclusion, open‐access data were found to be suitable for accurate and reliable predictions of water temperature using ANNs. However, we recommend incorporating stream‐specific environmental information and tailor the combination of input parameters to individual streams in order to obtain optimal results.