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Stability Prediction in Smart Grid Using PSO Optimized xgboost Algorithm with Dynamic Inertia Weight Updation

Computer Modeling in Engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis Mohemmed Sha Department of Software Engineering College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized xgboost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with xgboost due to its scalability and faster computational speed *** hyperparameters of xgboost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization xgboost dynamic inertia weight update

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xgboost-based model for predicting hydrogen content in electroslag remelting

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Journal of Iron and Steel Research International 2023年第5期30卷 887-896页

作者： Yu-xiao Liu Yan-wu Dong Zhou-hua Jiang Yu-shuo Li Wei Zha Yao-xin Du Shu-yang Du School of Metallurgy Northeastern UniversityShenyang 110819LiaoningChina Key Laboratory of Ecological Metallurgy of Multimetallic Mineral Northeastern UniversityShenyang 110819LiaoningChina State Key Laboratory of Rolling and Automation Northeastern UniversityShenyang 110819LiaoningChina

An Xtreme Gradient Boosting(xgboost)-based endpoint hydrogen content prediction model was proposed for the electroslag remelting process,the data collected in the field were pre-processed,and the characteristic variables of the physical parameters related to the variation of hydrogen content in the electroslag remelting process were selected by machine learning analysis and metallurgical *** kernel ridge regression model,ridge regression model,xgboost model,support vector regression model and gradient boosting regression model were developed and validated using the electroslag remelting data collected from the steel mills,and the model structure and parameters were adjusted several *** prediction accuracy of hydrogen content was compared *** xgboost model was validated for the test set with the following hit rates:70.59%,82.35% and 100% for the endpoint hits at the allowable hydrogen content error of ±0.05×10^(-6),±0.10×10^(-6) and ±0.50×10^(-6),respectively.

关键词： Electroslag remelting Hydrogen content Machine learning xgboost Prediction model

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Preventive Control for Power System Transient Security Based on xgboost and DCOPF with Consideration of Model Interpretability

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 279-294页

作者： Songtao Zhang Dongxia Zhang Ji Qiao Xinying Wang Zhijian Zhang China Electric Power Research Institute Beijing 100192China State Grid Beijing Electric Power Dispatching and Control Center Beijing 100031China

This paper proposes a new approach for online power system transient security assessment(TSA)and preventive control based on xgboost and DC optimal power flow(DCOPF).The novelty of this proposal is that it applies the xgboost and data selection method based on the 1-norm distance in local feature importance evaluation which can provide a certain model *** method of SMOTE+ENN is adopted for data *** contingency-oriented xgboost model is trained with databases generated by time domain simulations to represent the transient security constraint in the DCOPF model,which has a relatively fast speed of *** transient security constrained generation rescheduling is implemented with the differential evolution algorithm,which is utilized to optimize the rescheduled generation in the preventive *** and effectiveness of the proposed approach are demonstrated on an IEEE 39-bus test system and a 500-bus operational model for South Carolina,USA.

关键词： DC optimal power flow(DCOPF) model interpretability preventive control transient security assessment(TSA) xgboost

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CPSO-xgboost segmented regression model for asphalt pavement deflection basin area prediction

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Science China(Technological Sciences) 2022年第7期65卷 1470-1481页

作者： LI ZhuoXuan SHI XinLi CAO JinDe WANG XuDong HUANG Wei School of Mathematics Southeast UniversityNanjing 210096China School of Cyber Science&Engineering Southeast UniversityNanjing 210096China Yonsei Frontier Lab Yonsei UniversitySeoul 120-749Korea Research Institute of Highway Ministry of TransportBeijing 100088China Intelligent Transportation System Research Center Southeast UniversityNanjing 210096China

The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the xgboost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and *** experiments demonstrate the superiority of the xgboost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.

关键词： FWD pavement management systems RIOHTrack deflection basin area CPSO xgboost segmented regression model

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Predicting the Need for ICU Admission in COVID-19 Patients Using xgboost

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Computers, Materials & Continua 2021年第11期69卷 2077-2092页

作者： Mohamed Ezz Murtada K.Elbashir Hosameldeen Shabana College of Computer and Information Sciences Jouf UniversitySakakaKSA Faculty of Engineering Al Azhar UniversityCairoEgypt Faculty of Mathematical and Computer Sciences University of GeziraWad MadaniSudan Faculty of Medicine Al Azhar UniversityCairoEgypt College of Medicine Shaqra UniversityKSA

It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are *** in ICU admissions is associated with negative outcomes such as mortality and ***,early identification of patients with a high risk of respiratory failure can prevent complications,enhance risk stratification,and improve the outcomes of severely-ill hospitalized *** this paper,we develop a model that uses the characteristics and information collected at the time of patients’admissions and during their early period of hospitalization to accurately predict whether they will need ICU *** use the data explained and organized in a window-based manner by the Sírio-Libanês hospital team(published on Kaggle).Preprocessing is applied,including imputation,cleaning,and feature *** the cleaning process,we remove zero-variance,redundant,and/or highly correlated(measured by the Pearson correlation coefficient)*** use Extreme Gradient Boosting(xgboost)with early stopping as a predictor in our developed *** run the experiment in four stages starting from the features of Window 1 in Stage 1 and then incrementally add the features of Windows 2–4 in Stages 2–4 *** achieve AUCs of 0.73,0.92,0.95,and 0.97 in those four stages.

关键词： COVID-19 ICU admission xgboost

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Bayesian optimization+xgboost based life cycle carbon emission prediction for residential buildings—An example from Chengdu,China

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Building Simulation 2023年第8期16卷 1451-1466页

作者： Haize Pan Chengjin Wu School of Civil Engineering and Geomatics Southwest Petroleum UniversityChengdu 610500China

The large amount of carbon emissions generated by buildings during their life cycle greatly impacts the environment and poses a considerable challenge to China’s carbon reduction *** building design phase has the most significant potential to reduce building life-cycle carbon emissions(LCCO_(2)).However,the lack of detailed inventory data at the design stage makes calculating a building’s LCCO_(2) very difficult and ***,accurate prediction of building LCCO_(2) at the design stage using relevant design factors is essential to reduce carbon *** paper proposes an ensemble learning algorithm combining Bayesian optimization and extreme gradient boosting(BO-xgboost)to predict LCCO_(2) accurately in residential ***,this study collected and calculated the LCCO_(2) of 121 residential buildings in Chengdu,***,a carbon emission prediction model was developed using xgboost based on 15 design factors,and hyperparameter optimization was performed using the BO ***,the model performance was evaluated using two evaluation metrics,coefficient of determination(R2)and root mean square error(RMSE),and the prediction performance of other models was compared with that of the BO-xgboost *** results show that the RMSE of the proposed BO-xgboost for predicting LCCO_(2) in residential buildings is at least 40%lower compared to other *** method adopted in this study can help designers accurately predict building LCCO_(2) at the early design stage and provide methodological support for similar studies in the future.

关键词： building carbon emissions life cycle xgboost Bayesian optimization predictive model

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Interpretable modeling of metallurgical responses for an industrial coal column flotation circuit by xgboost and SHAP-A “conscious-lab” development

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International Journal of Mining Science and Technology 2021年第6期31卷 1135-1144页

作者： S.Chehreh Chelgani H.Nasiri M.Alidokht Minerals and Metallurgical Engineering Dept.of CivilEnvironmental and Natural Resources EngineeringLuleåUniversity of TechnologySE-97187 LuleåSweden Department of Computer Engineering Amirkabir University of TechnologyTehranIran Tabas Parvardeh Coal Company(TPCCO) BirjandIran

Surprisingly,no investigation has been explored relationships between operating variables and metallurgical responses of coal column flotation(CF) circuits based on industrial databases for under operation *** a novel approach,this study implemented a conscious-lab "CL" for filling this *** this approach,for developing the CL dedicated to an industrial CF circuit,SHapley Additive explanations(SHAP) and extreme gradient boosting(xgboost) were powerful unique machine learning systems for the first time *** explainable artificial intelligence models could effectively convert the dataset to a basis that improves human capabilities for better understanding,reasoning,and planning the *** could provide precise multivariable correlation assessments between the CF dataset by using the Tabas Parvadeh coal plant(Kerman,Iran),and showed the importance of solid percentage and washing water on the metallurgical responses of the coal CF *** could predict metallurgical responses(R-square> 0.88) based on operating variables that showed quite higher accuracy than typical modeling methods(Random Forest and support vector regression).

关键词： SHAP xgboost Explainable AI Coal flotation Separation efficiency

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Estimation of the TBM advance rate under hard rock conditions using xgboost and Bayesian optimization

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Underground Space 2021年第5期6卷 506-515页

作者： Jian Zhou Yingui Qiu Shuangli Zhu Danial Jahed Armaghani Manoj Khandelwal Edy Tonnizam Mohamad School of Resources and Safety Engineering Central South UniversityChangsha 410083China Department of Civil Engineering Faculty of EngineeringUniversity of Malaya50603 Kuala LumpurMalaysia School of Engineering Information Technology and Physical SciencesFederation University AustraliaBallaratAustralia Centre of Tropical Geoengineering(GEOTROPIK) School of Civil EngineeringFaculty of EngineeringUniversiti Teknologi Malaysia81310 Johor BahruMalaysia

The advance rate(AR)of a tunnel boring machine(TBM)under hard rock conditions is a key parameter in the successful implementation of tunneling *** this study,we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting(xgboost)with Bayesian optimization(BO)to model the TBM *** develop the proposed models,1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in *** database consists of rock mass and intact rock features,including rock mass rating,rock quality designation,weathered zone,uniaxial compressive strength,and Brazilian tensile *** specifications,including revolution per minute and thrust force,were considered to predict the TBM *** accuracies of the predictive models were examined using the root mean squares error(RMSE)and the coefficient of determination(R^(2))between the observed and predicted yield by employing a five-fold cross-validation *** showed that the BO algorithm can capture better hyper-parameters for the xgboost prediction model than can the default xgboost *** robustness and generalization of the BO-xgboost model yielded prominent results with RMSE and R^(2) values of 0.0967 and 0.9806(for the testing phase),*** results demonstrated the merits of the proposed BO-xgboost *** addition,variable importance through mutual information tests was applied to interpret the xgboost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.

关键词： TBM performance Advance rate xgboost Bayesian optimization Predictive modeling

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Cocrystal virtual screening based on the xgboost machine learning model

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Chinese Chemical Letters 2023年第8期34卷 398-403页

作者： Dezhi Yang Li Wang Penghui Yuan Qi An Bin Su Mingchao Yu Ting Chen Kun Hu Li Zhang Yang Lu Guanhua Du Beijing City Key Laboratory of Polymorphic Drugs Center of Pharmaceutical PolymorphsInstitute of Materia MedicaChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100050China Shandong Soteria Pharmaceutical Co. Ltd.Laiwu 271100China Beijing City Key Laboratory of Drug Target and Screening Research National Center for Pharmaceutical ScreeningInstitute of Materia MedicaChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100050China

Co-crystal formation can improve the physicochemical properties of a compound,thus enhancing its ***,artificial intelligence-based co-crystal virtual screening in the early stage of drug development has attracted extensive attention from ***,the complexity of developing and applying algorithms hinders it wide *** study presents a data-driven co-crystal prediction method based on the xgboost machine learning model of the scikit-learn *** simplified molecular input line entry specification(SMILES)information of two compounds is simply inputted to determine whether a co-crystal can be *** data set includs the co-crystal records presented in the Cambridge Structural Database(CSD)and the records of no co-crystal formation from extant literature and *** molecular descriptors are adopted as the features of a compound in the data *** developed model shows excellent performance in the proposed co-crystal training and validation sets with high accuracy,sensitivity,and F1 *** prediction success rate of the model exceeds 90%.The model therefore provides a simple and feasible scheme for designing and screening co-crystal drugs efficiently and accurately.

关键词： Cocrystal Machine learning xgboost Molecular descriptor Praziquantel Nefiracetam

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Image Fusion Using Wavelet Transformation and xgboost Algorithm

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Computers, Materials & Continua 2024年第4期79卷 801-817页

作者： Shahid Naseem Tariq Mahmood Amjad Rehman Khan Umer Farooq Samra Nawazish Faten S.Alamri Tanzila Saba Department of Information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： Image fusion max-min average CWT xgboost DCT inclusive innovations spatial and frequency domain

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