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Optimizing solar photovoltaic system performance: Insights and strategies for enhanced efficiency

ENERGY 2025年 319卷

作者： Gawusu, Sidique Zhang, Xiaobing Yakubu, Sufyan Debrah, Seth Kofi Das, Oisik Bundela, Nishant Singh Nanjing Univ Sci & Technol Sch Energy & Power & Engn Nanjing Peoples R China Bolgatanga Tech Univ Dept Elect & Elect Engn Bolgatanga Ghana Univ Ghana Sch Nucl & Allied Sci Dept Nucl Engn Atom Energy Accra Ghana Ghana Atom Energy Commiss Nucl Power Inst Accra Ghana Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Struct & Fire Engn Div S-97187 Lulea Sweden Johns Hopkins Univ Whiting Sch Engn Baltimore MD 21211 USA Univ Energy & Nat Resources UENR Sch Energy Dept Renewable Energy Engn Sunyani Ghana Univ Energy & Nat Resources UENR Reg Ctr Energy & Environm Sustainabil RCEES Sunyani Ghana

This study analyzes the performance and predictive modeling of solar photovoltaic (PV) systems at the Bui Generating Station in Ghana using the xgboost (Extreme Gradient Boosting) algorithm. The predictive model, validated through Monte Carlo simulations, demonstrates measured stability across perturbation scenarios. Distribution analysis confirms appropriate parameter bounds, while error analysis demonstrates consistent pattern preservation across simulation scenarios. The study quantifies the relative influences of environmental factors, particularly the interplay between temperature, irradiance, and humidity (correlations ranging from -0.33 to 0.36). These findings provide insights for system operation while acknowledging the complex, often weak coupling between environmental parameters. Seasonal performance analysis reveals distinct optimization windows, with the Post-Rainy season showing the highest stability (PR: 0.986 +/- 0.082) and optimal enhancement potential. Sensitivity analysis identifies critical operational thresholds, including performance transitions at 80 % relative humidity and optimal temperature ranges below 32 degrees C, where each 1 degrees C reduction yields 0.45 % efficiency gain. The study establishes specific optimization strategies including automated cleaning systems triggered at 85 % peak irradiance, yielding 2.5 % efficiency improvement, and enhanced inverter response protocols during peak generation periods, achieving a 3.2 % performance gain. These findings inform practical implementation frameworks for performance optimization, contributing to improved energy generation efficiency and system reliability.

关键词： Monte Carlo simulations Predictive modeling Renewable energy optimization xgboost algorithm Solar photovoltaic systems Energy policy

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Machine learning-aided screening framework for wound healing peptides

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BULLETIN OF MATERIALS SCIENCE 2024年第4期47卷 263页

作者： Gunaseelan, Sathish Kumar Khandelwal, Yashi Dutta, Arnab Mitra, Debirupa Biswas, Swati Birla Inst Technol & Sci BITS Pilani Dept Chem Engn Hyderabad Campus Hyderabad 500078 India Birla Inst Technol & Sci BITS Pilani Dept Pharm Hyderabad Campus Hyderabad 500078 India

Chronic wounds characterized by prolonged inflammation and persistent infection pose a significant burden to global healthcare systems. Currently, antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs) are administered to patients. Prolonged use of antibiotics is severely discouraged owing to the rapid rise in antimicrobial resistance, and the use of NSAIDs can also increase the risk of infection. Thus, the discovery of novel therapeutics for chronic wounds is crucial. Antimicrobial peptides (AMPs) are an emerging class of therapeutics, which are effective and has no known mechanism of inducing resistance. Anti-inflammatory peptides (AIPs) are another class of therapeutic peptides that can reduce inflammation by eliciting anti-inflammatory cytokine response. A single peptide possessing both AMP and AIP activities can be an ideal therapeutic for the treatment of chronic wounds. However, the discovery of peptides with multiple properties via experimental testing is a daunting task. In this work, we propose a classification framework using machine learning for the identification of wound healing peptides (WHPs) i.e., which possess both AMP and AIP activities. The proposed framework uses xgboost algorithm with amino-acid composition, sequence analysis and physicochemical properties as feature representation methods (FRMs) to develop binary classifiers. The model developed by combining all the three FRMs, resulted in the highest accuracy of 93.3 and 76.2% for AMP and AIP classifications, respectively. An easy-to-use freely accessible web tool (WHP-Pred) has also been developed.

关键词： Bifunctional peptides antimicrobial activity anti-inflammatory activity chronic wound binary classification xgboost algorithm

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RC-xgboost-Based Mechanical Parameters Back Analysis of Rock Mass in Heavily Fractured Tunnel: A Case in Yunnan, China

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ROCK MECHANICS AND ROCK ENGINEERING 2024年第4期57卷 2997-3019页

作者： Zhu, Menglong Peng, Hao Liang, Ming Song, Guanxian Huang, Nenghao Xie, Weiwei Han, Yu Guangxi Rd & Bridge Engn Grp Co Ltd Nanning 530000 Peoples R China Guangxi Univ Coll Civil Engn & Architecture Nanning 530000 Peoples R China

The rock mechanics parameters are important indicators for risk assessment in tunnel construction. However, rock mechanical parameters estimated by traditional mechanical testing in the field are subject to considerable uncertainties due to limitations testing technology, which can directly lead to unreliable risk assessment results. Deformation data for parametric back analysis is a reliable and efficient method for estimating mechanical parameters of rock masses. Deformation monitoring data used for parameter back analysis is an efficient and reliable method for estimating rock mechanical parameters, but the current research generally suffers from inaccurate constitutive models, confusing selection of input data, and failure to consider the intrinsic correlation of mechanical parameters. In this paper, a deformation-based back analysis method for rock mechanical parameters was proposed by xgboost with RC. Taking the Changning Tunnel in Yunnan Province, China, as an example, the construction process of the back analysis model was described in detail. To improve the reliability of the sample data, the Ubiquitous-Joint constitutive model was used and a more characteristic deformed data selection method was proposed. Furthermore, the performance of the back analysis model was improved through Bayesian Optimization of hyperparameters and order filtering of Regressor Chain, and the intrinsic correlation of the parameters was effectively considered through the Regressor Chain. The results showed that applying the rock mechanics parameters obtained from the back analysis model to numerical simulation can effectively predict deformation during tunnel construction, and the error rate of deformation prediction was within 10%, which demonstrates the reliability of the method in this paper. This method can be extended to other heavily fractured tunnels. Summarized the technical process of back analysis of mechanics parameters based on machine learning, analyzed the current res

关键词： Tunnel Heavily fractured rock masses Numerical simulation xgboost algorithm Back analysis of mechanical parameters

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Suitability of Mechanics-Based and Optimized Machine Learning-Based Models in the Shear Strength Prediction of Slender Beams Without Stirrups

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BUILDINGS 2024年第12期14卷 3946-3946页

作者： David, Abayomi B. Olalusi, Oladimeji B. Awoyera, Paul O. Simwanda, Lenganji Stellenbosch Univ Dept Civil Engn ZA-7602 Matieland South Africa Durban Univ Technol Dept Civil Engn ZA-3209 Pietermaritzburg South Africa Prince Mohammad Bin Fahd Univ Dept Civil Engn Al Khobar 31952 Saudi Arabia Czech Tech Univ Klokner Inst Dept Struct Reliabil Prague 16000 Czech Republic

Accurate shear capacity estimation for reinforced concrete (RC) beams without stirrups is essential for reliable structural design. Traditional code-based methods, primarily empirical, exhibit variability in predicting shear strength for these beams. This paper assesses the effectiveness of mechanics-based and optimized machine learning (ML) models for predicting shear strength in stirrup-less, slender beams using a dataset of 784 tests. Seven ML models-artificial neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), AdaBoost, gradient boosting (GBR), and extreme gradient boosting (XGB)-were compared against three mechanics-based models: the Tran's NLT Model (2020), the Multi-Action Shear Model (MASM), and the Compression Chord Capacity Model (CCC). Among the ML models, XGB and GBR demonstrated the highest predictive accuracy, with coefficients of determination (R2) of 0.974 and 0.966, respectively, indicating strong correlation with experimental data. Performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE) showed that XGB and GBR consistently outperformed other models, yielding lower error margins. Statistical analysis revealed minimal bias and variability in the predictions of XGB and GBR, with a coefficient of variation (CoV) of 14%, ensuring high reliability. The NLT model, the most accurate of the mechanical-based models, achieved a mean of 1.02 and a CoV of 16% for its model error, demonstrating reasonable prediction reliability but falling behind XGB and GBR in accuracy. With Shapley additive explanations (SHAPs), the beam width and depth were identified as primary predictors of shear strength, providing critical insights for enhancing design and construction practises.

关键词： beams shear strength machine learning model uncertainty Shapley additive explanation method xgboost algorithm

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Predictive modeling for design of low elastic modulus and biocompatible β-Ti alloys: A comprehensive machine learning-data driven approach with xgboost as the key predictor

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第19期238卷 9338-9360页

作者： Kanapaakala, Gouthamaraj Subramani, Venkatesan Vellore Inst Technol Sch Mech Engn Katpadi Tiruvalam Rd Vellore 632014 Tamil Nadu India

Mechanical compatibility and biocompatibility are critical considerations in the design of beta-Ti alloys. The former hinges on achieving a reduced elastic modulus, while the latter is contingent on the toxic alloying element's absence. Although conventional alloy design methods have been employed for developing low-modulus beta-Ti alloys, these theoretical approaches encounter limitations due to the high non-linear relationship between composition and elastic modulus. To address these challenges and improve the design of low-modulus and biocompatible beta-Ti alloys, an alternative approach utilizing machine learning (ML) techniques has been explored. ML leverages sophisticated algorithms to discern intricate connections between material composition and elastic modulus, making it a promising avenue for alloy design. In this study, seven supervised ML regression models were developed and compared to identify the most effective model for the given dataset. Notably, the xgboost model exhibited superior performance compared to the other models, boasting a remarkable accuracy with an R-squared (R2) value of 0.962 and an RMSE (Root Mean Square Error) of 3.16 GPa for the test data. Due to the inherent complexity of comprehending the structures and patterns present within datasets and simultaneously formulating effective predictive models, this study pursued the exploration of dependable models through the avenue of statistical inference analysis, a novel approach yet unexplored in prior research studies. Furthermore, a feature importance analysis was conducted to ascertain crucial input parameters significantly influencing the output, based on the best-performing model. The validity of the best model's results was confirmed by comparing them with experimental data, ensuring the reliability and applicability of the ML-based alloy design method.

关键词： beta-Ti alloys elastic modulus biocompatibility machine learning xgboost algorithm

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The potential of machine learning to predict melting response time of phase change materials in triplex-tube latent thermal energy storage systems

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APPLIED ENERGY 2025年 390卷

作者： Yan, Peiliang Wen, Chuang Ding, Hongbing Wang, Xuehui Yang, Yan Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China Univ Reading Sch Built Environm Reading RG6 6AH England Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Coll Dublin Sch Mech & Mat Engn Dublin D04 V1W8 Ireland Univ Exeter Fac Environm Sci & Econ Exeter EX4 4QF England

Accurate prediction of the melting response time is vital for optimizing thermal energy storage systems, which play a key role in addressing the temporal mismatch between thermal energy demand and supply in the built environment. This study aims to quantitatively predict the melting response time of a novel triplex-tube thermal energy storage system incorporating phase change materials and Y-shaped fins to enhance heat transfer. A numerical model based on the enthalpy-porosity method was developed to simulate the melting process, resulting in a dataset comprising 60 cases with melting response times ranging from 15 to 45 min under varying design and operational conditions. The key parameters investigated include fin angle (10 degrees-30 degrees), fin width (5-15 mm), and heat transfer fluid temperature (60 degrees C-80 degrees C). Prior to model development, variable independence was validated to ensure robust predictions. Four machine learning algorithms-polynomial regression, support vector regression, random forest regression, and extreme gradient boosting (xgboost)-were employed, with hyper-parameter optimization performed using a Bayesian approach. The xgboost model demonstrated superior predictive capability, achieving an accuracy of 92 %. Feature importance analysis revealed that fin width and heat transfer fluid temperature were the dominant factors, contributing 51 % and 47 % to the prediction variance, respectively, whereas fin angle had a marginal influence of 2 %. This work provides a novel application of machine learning techniques to the design and optimization of thermal energy storage systems, offering valuable insights into improving their melting performance and operational efficiency.

关键词： Phase change material Thermal energy storage Machine learning Melting response time xgboost algorithm

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Prediction of cardiovascular disease based on multiple feature selection and improved PSO-xgboost model

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Cao, Kerang Liu, Chang Yang, Siqi Zhang, Yuxin Li, Lili Jung, Hoekyung Zhang, Shuo Shenyang Univ Chem Technol Coll Comp Sci & Technol Shenyang 110142 Peoples R China Key Lab Intelligent Technol Chem Proc Ind Liaoning Shenyang 110142 Peoples R China Shenyang Matern & Child Hlth Hosp Shenyang 110014 Peoples R China Paichai Univ Comp Engn Dept Daejeon 35345 South Korea

Cardiovascular disease is a common disease that threatens human health. In order to predict it more accurately, this paper proposes a cardiovascular disease prediction model that combines multiple feature selection, improved particle swarm optimization algorithm, and extreme gradient boosting tree. Firstly, the dataset is preprocessed, and an xgboost cardiovascular disease prediction model is constructed for model training and compare it with other algorithms. Then, combined with two factor Pearson correlation analysis and feature importance ranking, multiple feature selection is performed, with the optimal feature subset as the feature input. Finally, the improved particle swarm optimization algorithm is used to adjust the hyperparameters of the extreme gradient boosting tree algorithm, and selecting the optimal hyperparameter combination to construct the MFS-DLPSO-xgboost model. The recall, precision, accuracy, F1 score, and area under the ROC curve (AUC) of the MFS-DLPSO-xgboost model reached 71.4%, 76.3%, 74.7%, 73.6%, and 80.8%, respectively, which increased by 3.6%, 3.2%, 2.7%, 3.2%, and 2.3% compared to xgboost. The results indicate that the model proposed in this article has good classification performance and can provide assistance for doctors and patients in predicting and preventing heart disease.

关键词： Cardiovascular disease Machine learning xgboost algorithm Multi feature selection Particle swarm optimization algorithm Model prediction

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AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders

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SCHIZOPHRENIA RESEARCH 2025年 275卷 42-51页

作者： Zhu, Zheng Roy, Dooti Feng, Shaolei Vogler, Brian Boehringer Ingelheim Pharmaceut Inc 900 Ridgebury Rd Ridgefield CT 06877 USA AiCure LLC 214 Sullivan St New York NY 10012 USA

Objective: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event. Methods: Patients treated with BI 409306 were recruited from two Phase II randomized, placebo-controlled trials in schizophrenia (NCT03351244) and attenuated psychotic disorders (NCT03230097). A machine-learning model was optimized to predict overall trial adherence using AiCure data collected over three monitoring periods (7/10/14 days), adherence cut-offs (0.6/0.7/0.8) and timepoints (Start/Mid/End). Area under the curve (AUC), false negative rate, and false omission rate averaged across 10 model cross-validations were analyzed. In NCT03351244, post hoc analyses compared time to first relapse in patients observed as adherent versus those predicted adherent by the model. Results: Of 235 patients, 60.4 % demonstrated >= 80 % adherence. At an adherence cut-off of 0.8, the 14-day model performed best (AUC: 0.81 versus 0.79 [10-day], 0.77 [7-day]). Within the 14-day model, 0.6 cut-off was optimal (AUC: 0.87 versus 0.85 [0.7 cut-off], 0.81 [0.8 cut-off]). The Trial-End timepoint yielded the most accurate prediction (AUC: 0.92 versus 0.87 [Start], 0.85 [Mid]). Despite NCT03351244 not meeting the primary endpoint, a reduction in risk of first relapse with BI 409306 versus placebo was observed when analyzed with adherent completers (>= 80 % across trial;HR = 0.485) and patients with predicted adherence >= 60 % (HR = 0.510). Conclusions: Adherence data with longer monitoring durations (14 days), lower adherence cut-offs (0.6), and later timepoints (Trial-End) produced most accurate adherence predictions. Accurate adherence prediction provides insights about medication adherence patterns that may help clinicians improve individual adherence.

关键词： Medication non-adherence Machine-learning prediction model xgboost algorithm Clinical trials Psychosis Schizophrenia

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Optimization of Product Distribution for MIP Units Using Data Mining

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China Petroleum Processing & Petrochemical Technology 2024年第2期26卷 146-157页

作者： Wang Qing Zhang Xiaoguo Mei Junwei Gao Zhibo Yang Kuizhi Yang Dawei Ouyang Fusheng International Joint Research Center of Green Energy Chemical Engineering East China University of Science and TechnologyShanghai 200237China SINOPEC Changling Petrochemical Co. LtdYueyang 414012China

Based on data from a petrochemical company’s MIP unit over the past three years,19 input variables and 2 output variables were selected for modeling using the maximum information coefficient and Pearson correlation coefficient among 155 variables,which included properties of feedstock oil and spent catalyst,operational variables,and material *** distillation range variables were reduced using factor analysis,and the feedstock oils were clustered into three types using the K-means++*** feedstock oil type was then used as an input variable for *** xgboost model and a back propagation(BP)neural network model with a structure of 20-15-15-2 were developed to predict the combined yield of gasoline and propylene,as well as the coke *** the test set,the BP neural network model demonstrated better fitting and generalization abilities with a mean absolute percentage error and determination coefficient of 1.48%and 0.738,respectively,compared to the xgboost *** was therefore chosen for further optimization *** genetic algorithm was utilized to optimize operational variables in order to increase the combined yield of gasoline and propylene while controlling the growth of coke *** commercial test results in the MIP unit showed an average increase of 1.39 percentage points for the combined yield of gasoline and propylene and an average decrease of 0.11 percentage points for coke *** results indicate that the model effectively improves the combined yield of gasoline and propylene while controlling the increase in coke yield.

关键词： MIP process K-Means++ BP neural network model xgboost algorithm genetic algorithm

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Data-driven performance analysis of an active chilled beam air conditioning system: A machine learning approach for energy efficiency and predictive maintenance

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RESULTS IN ENGINEERING 2024年 23卷

作者： Amin, Nima Hajimirza Etemad, Alireza Abdalisousan, Ashkan Islamic Azad Univ Dept Energy Engn Sci & Res Branch Tehran Iran Univ Coll Dublin UCD Energy Inst Sch Mech & Mat Engn Dublin Ireland Islamic Azad Univ Dept Technol & Engn Astara Branch Astara Iran

This study aims to enhance energy performance estimation and maintenance planning for office buildings in Tehran. Machine learning and data mining techniques were employed, utilizing Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Narrow Neural Networks, and optimizable SVMs. A comprehensive dataset of 2500 experimental samples was utilized with five input parameters (building size, occupancy patterns, environmental conditions, and ACB system parameters) and one output parameter (electricity consumption). GPR models performed better in estimating electricity consumption, achieving a Root Mean Square Error (RMSE) of 297.01 and an impressive R-squared value of 0.99. To optimize energy performance and design more energy- efficient buildings, this model accurately predicts energy consumption. Additionally, this study addressed maintenance planning for ACB systems, simulating maintenance issues with 2500 synthetic samples. The dataset included building age, size, number of floors, usage intensity, previous maintenance history, location, and environmental factors. Maintenance time was estimated using these features, and xgboost was employed to predict ACB system maintenance time. Low MSE, RMSE, and R-squared values in the xgboost model allow for accurate predictions. Using this model, facility managers can proactively plan maintenance activities, allocate resources, and minimize downtime. Through proactive measures, ACB performance and maintenance costs are optimized. The study accurately predicts ACB system energy consumption and maintenance time using the rational quadratic GPR model, applicable in Tehran and beyond. Additionally, MATLAB version R2022b was used for prediction, while DesignBuilder and EnergyPlus were utilized for technical analysis.

关键词： HVAC systems Active chilled beam system Data-driven modeling xgboost algorithm Machine learning Predictive maintenance analysis GPR Gaussian Process Regression

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