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Developing window behavior models for residential buildings using xgboost algorithm

ENERGY AND BUILDINGS 2019年 205卷

作者： Mo, Hao Sun, Hejiang Liu, Junjie Wei, Shen Tianjin Univ Sch Environm Sci & Engn Tianjin Key Lab Indoor Air Environm Qual Control Tianjin 300072 Peoples R China UCL Bartlett Sch Construct & Project Management 1-19 Torrington Pl London WC1E 7HB England

Buildings account for over 32% of total society energy consumption, and to make buildings more energy efficient dynamic building performance simulation has been widely adopted during the buildings' design to help select most appropriate HVAC (Heating Ventilation and Air Conditioning) systems. Due to the lack of good behavioral models in current simulation packages, many researchers have tried to develop useful behavioral models to improve simulation accuracy, including window behavior models, using field data collected from real buildings. During this work, many mathematical and machine learning methods have been used, and some level of prediction accuracy has been achieved. xgboost is a recently introduced machine learning algorithm, which has been proven as very powerful in modeling complicated processes in other research fields. In this study, this algorithm has been adopted to model and predict occupant window behavior, aiming to further improve the modeling accuracy from a globally accepted modeling approach, namely, Logistic Regression Analysis. Field data in terms of both occupant window behavior and relevant influential factors were collected from real residential buildings during transitional seasons. Both xgboost and Logistic Regression Analysis were used to build window behavior models, after a feature selection work, and their prediction performances on an independent dataset were compared. The comparison revealed that xgboost has solid advantages in modeling occupant window behavior, over Logistic Regression Analysis, and it is expecting that the same finding would be obtained for other behavioral types, such as blind control and air-conditioner operation. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Behavior modeling Window behavior Logistics regression xgboost algorithm Residential buildings

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Robust Classification of UWB NLOS/LOS Using Combined FCE and xgboost algorithms

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IEEE ACCESS 2024年 12卷 151030-151045页

作者： Wang, Shoude Ahmad, Nur Syazreen Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Penang Malaysia Weifang Univ Sci & Technol Shouguang 262700 Peoples R China

Ultra-wideband (UWB) positioning systems are widely applied in localization. However, in complex environments with numerous obstacles, Non-Line-of-Sight (NLOS) propagation of UWB signals can occur, significantly affecting accuracy. Effective identification of Line-of-Sight (LOS) and NLOS signals is essential for precise localization. In this study, we propose a novel NLOS/LOS recognition method based on Extreme Gradient Boosting (xgboost). The method begins with feature selection using the Pearson Correlation Coefficient to filter less correlated features of the UWB Channel Impulse Response (CIR) data, followed by outlier handling. Preliminary NLOS identification and classification are performed using Fuzzy Comprehensive Evaluation (FCE), with subsequent optimization of FCE weights via the Pattern Search algorithm (PSa). Final classification and recognition are then achieved through xgboost. This approach, initially trained in one scenario, demonstrates seamless transition and strong generalization across six additional scenarios. Compared to traditional machine learning and neural networks, it offers lower system requirements. Experimental results on an open-source dataset covering seven scenarios show average accuracy, precision, recall, and F1 score of 93.2%, 94.8%, 90.3%, and 92.5%, respectively, highlighting the method's superior performance and robustness in diverse environments.

关键词： UWB CIR FCE xgboost algorithm xgboost algorithm machine learning machine learning NLOS NLOS indoor positioning indoor positioning machine learning NLOS indoor positioning

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Intelligent prediction model of mechanical properties of ultrathin niobium strips based on xgboost ensemble learning algorithm

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COMPUTATIONAL MATERIALS SCIENCE 2024年 231卷

作者： Wang, Zhen Hua Liu, Yun Fei Wang, Tao Wang, Jian Guo Liu, Yuan Ming Huang, Qing Xue Minist Educ Engn Res Ctr Adv Met Composites Forming Technol & Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Shanxi Peoples R China Natl Key Lab Met Forming Technol & Heavy Equipment Xian 710018 Shaanxi Peoples R China

Ultrathin niobium strips with different thicknesses are prepared by an accumulative rolling process. The tensile test of the ultrathin niobium strips is carried out, and the microstructure of each niobium strip is characterized by electron backscattered diffraction (EBSD). The process parameters, mechanical properties and microstructure characterization data of rolled ultrathin niobium strips with different thicknesses are collected, analyzed and sorted. A data-driven intelligent prediction model for the mechanical properties of ultrathin niobium strips is established by deeply integrating the mechanical properties of ultrathin niobium strips with the microstructure evolution mechanism and combining the integrated data with the xgboost ensemble learning algorithm. The optimal parameters of the xgboost model are determined by a grid search and used for mechanical performance prediction. The overall generalization performance of the model is evaluated by the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). To reflect the advancement of the proposed model, the prediction results of this model are compared with those of the random forest (RF), multi-layer perceptron (MLP) and gradient boosting decision tree (GBDT) prediction model. The research results show that, on the test set, the R2 in terms of the tensile strength and yield strength predicted values based on the xgboost algorithm were higher than those of other models, reaching 0.944 and 0.964, respectively. The three error indicators corresponding to the xgboost model were also at the lowest level. This means that the model based on the xgboost algorithm has the optimal generalization performance and can thus realize the accurate prediction of the mechanical properties of ultrathin niobium strips. This research provides a new method and idea for the optimal design of rolling process and the prediction of their mechanical properti

关键词： Ultrathin niobium strip Mechanical properties prediction xgboost algorithm Data-driven model

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Ship Intrusion Detection Technology Based on Bayesian Optimization algorithm and xgboost 3

Ship Intrusion Detection Technology Based on Bayesian Optimi...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Zhou, Aobo Zhu, Qi Zhang, Jundong Meng, Ke School of Marine Engineering Dalian Maritime University Liaoning Dalian China

ISBN: (纸本)9798350382426

Cybersecurity is a prerequisite for maritime safety. This study focuses on intelligent ship integrated automation system, analyzing the principles and shortcomings of these system. We propose a machine learning-based intrusion detection solution that utilizes Recursive Feature Elimination (RFE) as a feature selection algorithm and xgboost as a classification algorithm. The Bayesian optimization algorithm is applied to optimize the hyperparameters of the xgboost model for detecting anomalous network data. Results indicate that, compared to decision trees, logistic regression, random forests, KNN, and recent research findings, the proposed method exhibits smaller estimation errors with an overall accuracy of 99.8%. It effectively reduces vulnerabilities in ship networks, achieving a F1-score of 96.37% and a recall rate of 96.33% under sufficient dataset conditions. This approach enhances network security performance and elevates the cybersecurity level of intelligent ships. © 2023 IEEE.

关键词： Anomaly detection intelligent ship integrated automation system shipboard network security xgboost algorithm

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Springback analysis of Q960 ultra-high-strength structural steel considering degenerated chord modulus

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THIN-WALLED STRUCTURES 2025年 209卷

作者： Shang, Mingxu Yang, Hua Hou, Chuan-Chuan Zhao, Yang Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Tsinghua Univ Xiangcheng Suzhou Automot Res Inst Suzhou 215000 Peoples R China

Ultra-high-strength steels (UHSS), as one of the difficult-to-form building materials, pose more challenges in accurate springback prediction of cold-bending design, which are attributed to the high yield stress, the uncertainty of hardening response and the varying chord modulus. A total of 11 specimens made of Q960 UHSS involving the cyclic loading-unloading-reloading tensile test and the springback followed by three-point bending test, were designed to investigate the degeneration behavior of chord modulus and the springback response. Subsequently, a constitutive model set, including the Voce isotropic hardening model, the updated VoceChaboche combined hardening model with modified continuum damage mechanics model, and the aforementioned combined hardening model with Yoshida chord modulus model, was applied in the springback analysis with the user-defined material subroutine VUMAT. The verification results indicate that the type of hardening model (isotropic or kinematic) has negligible influence on springback, while the steels with higher strength and more degeneration of chord modulus have more amount of springback. Finally, with a database consisting of 365 data lists built up, the xgboost machine learning model was utilized and verified in terms of the capacity of predictability for the bending and springback performance with different geometrical parameters, providing a basis about the feasibility design and mould design of cold bending for Q960 UHSS.

关键词： Q960 Ultra-high-strength steel Springback Chord modulus xgboost algorithm

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A novel tool wear monitoring approach based on attention mechanism and gated recurrent unit

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MACHINING SCIENCE AND TECHNOLOGY 2025年第2期29卷 187-211页

作者： Zhang, Lei Zhao, Zhengcai Zheng, Shichen Qian, Ning Li, Yao Xu, Jiuhua Huan, Haixiang Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Peoples R China Yancheng Inst Technol Sch Mech Engn Yancheng Peoples R China

Obtaining tool wear value quickly and accurately during machining is crucial for maintaining the surface quality of machined parts, enhancing machining efficiency and reducing operational costs. To improve the accuracy of the tool wear monitoring, this article proposes an Attention - GRU tool wear monitoring approach, which considers the importance of features extracted from multi-sensor signals and the previous machining processes in achieving accurate monitoring. Firstly, signals from the machining process are acquired, segmented and sliced. Second, an Attention - GRU tool wear monitoring model consisting of a 6-layer gated recurrent unit neural network optimized by the attention mechanism is constructed. Third, feature extraction is performed for the preprocessed signals, and the features which have a strong correlation with tool wear condition are selected as the inputs of the constructed approach using the extreme gradient boosting algorithm. Finally, the tool wear monitoring accuracy of the proposed approach is experimentally verified, and the monitoring performance is compared with back propagation neural networks and support vector regression. The results demonstrate that the proposed approach can achieve a tool wear monitoring accuracy of 97%.

关键词： Attention - GRU model monitoring accuracy tool wear monitoring xgboost algorithm

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Practical machine learning model selection and interpretation for organophosphorus flame retardancy in Epoxy resin

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POLYMER DEGRADATION AND STABILITY 2025年 234卷

作者： Li, Jiajun Zou, Bin Bekeshev, Amirbek Akhmetova, Marzhan Orynbassar, Raigul Wang, Xin Hu, Yuan Univ Sci & Technol China State Key Lab Fire Sci 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China China Acad Safely Sci & Technol Beijing 100012 Peoples R China K Zhubanov Aktobe Reg State Univ Lab Polymer Composites Aliya Moldagulova Ave 34 Aktobe 030000 Kazakhstan K Zhubanov Aktobe Reg State Univ Dept Phys Aliya Moldagulova Ave 34 Aktobe 030000 Kazakhstan K Zhubanov Aktobe Reg State Univ Dept Chem & Chem Technol Aliya Moldagulova Ave 34 Aktobe 030000 Kazakhstan

The traditional trial-and-error method for developing organophosphorus flame retardants is time-consuming and expensive. This work constructed machine learning models for limiting oxygen index (LOI), peak heat release rate (PHRR), and UL-94 for epoxy resin on the basis of the collected multifactor database, including the structure of organophosphorus flame retardants, addition amounts, matrix combustion performance, and flux. The training and test sets were divided on the basis of molecular groups to avoid data leakage within the same molecule group, in contrast to conventional random splitting. The best results for LOI and PHRR prediction were achieved via the xgboost algorithm and ECFP4 fingerprints, with mean absolute errors of 1.61% and 125.5 kW/m2 on the test set, respectively. For UL-94 classification, the MACCS with xgboost achieved 79% accuracy. The Shapley additive explanation for the model indicated that the addition amount and matrix combustion performance data were the two most important features. This work could help develop a more accurate and reliable prediction model for flame retardancy.

关键词： Machine learning Organophosphorus flame retardant Epoxy resin xgboost algorithm

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An integrated framework utilizing machine learning to accelerate the optimization of energy-efficient urban block forms

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Building Simulation 2024年第11期17卷 2017-2042页

作者： Ke Liu Xiaodong Xu Ran Zhang Lingyu Kong Xi Wang Deqing Lin School of Architecture Southeast UniversitySipailou 2Xuanwu DistrictNanjing210096Jiangsu ProvinceChina

Urban block form significantly impacts energy and environmental ***,optimizing urban block design in the early stages contributes to enhancing urban energy efficiency and environmental ***,widely used multi-objective optimization methods based on performance simulation face the challenges of high computational loads and low *** study introduces a framework using machine learning,especially the xgboost model,to accelerate multi-objective optimization of energy-efficient urban block forms.A residential block in Nanjing serves as the case *** framework commences with a parametric block form model driven by design variables,focusing on minimizing building energy consumption(EUI),maximizing photovoltaic energy generation(PVE)and outdoor sunlight hours(SH).Data generated through Latin Hypercube Sampling and performance simulations inform the model *** training and hyperparameter tuning,xgboost’s predictive accuracy was validated against artificial neural network(ANN),support vector machine(SVM),and random forest(RF)***,xgboost replaced traditional performance simulations,conducting multi-objective optimization via the NSGA-II *** showcase the framework’s significant acceleration of the optimization process,improving computational efficiency by over 420 times and producing 185 Pareto optimal solutions with improved performance *** analysis highlighted shape factor(SF),building density(BD),and building orientation(BO)as key morphological parameters influencing EUI,PVE,and *** study presents an efficient approach to energy-efficient urban block design,contributing valuable insights for sustainable urban development.

关键词： urban form machine learning xgboost algorithm multi-objective optimization performance-driven urban design urban building energy modeling

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Designing a cryptocurrency trading system with deep reinforcement learning utilizing LSTM neural networks and xgboost feature selection

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Ghadiri, Hamidreza Hajizadeh, Ehsan Amirkabir Univ Technol Tehran Polytech Dept Ind Engn & Management Syst Hafez St Tehran 158754413 Iran

This paper aims to present a cryptocurrency trading strategy that addresses market volatility and decision-making challenges using advanced machine learning techniques and a wide range of predictor variables. Specifically, the proposed method is designed to enhance trading decisions by improving the accuracy of market trend forecasts. The approach consists of two primary steps. First, the xgboost algorithm is applied to identify the most relevant features from market variables, technical indicators, macroeconomic factors, and blockchainspecific data for each cryptocurrency. In the second step, these selected features are fed into a Double Deep QNetwork (DDQN) algorithm incorporating LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), and GRU (Gated recurrent units) layers to generate trading signals (buy, hold, sell). The model's performance, tested on Bitcoin and Ethereum data from July 2021 to March 2023, demonstrates that blockchain variables provide crucial insights for trading strategies. Furthermore, combining xgboost for feature selection with the DDQN model improves all key trading performance metrics, highlighting the significance of feature selection in optimizing deep reinforcement learning agents.

关键词： algorithmic trading Deep Reinforcement Learning xgboost algorithm Cryptocurrency Blockchain

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An Improved YOLOv8-xgboost load rapid identification method based on multi-feature fusion

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 166卷

作者： Wang, Jianyuan Cheng, Long Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Peoples R China

Existing non-intrusive load monitoring (NILM) approaches face challenges including limited identification accuracy, computationally intensive architectures, and constrained generalization performance. To address these issues, this paper proposes an Improved YOLOv8-xgboost rapid load identification method based on multifeature fusion. First, the temporal features of load current data are extracted using the Markov Transition Field (MTF) algorithm through image encoding methods. Additionally, the Fast Fourier Transform (FFT) is employed to extract the maximum frequency component and average frequency amplitude as corresponding frequency-domain features. Subsequently, the Improved YOLOv8 network is utilized for preliminary recognition of temporal domain images. Finally, to resolve the low discriminability issue of similar loads, the recognition results of the Improved YOLOv8 network are fused with selected frequency-domain features using the eXtreme Gradient Boosting (xgboost) algorithm for training to achieve the final identification results. Validation on both high-frequency and low-frequency datasets demonstrates that the proposed method exhibits strong generalization capability, achieving an identification accuracy of over 99%.

关键词： Non-Intrusive Load Identification MTF Encoding Feature Fusion YOLOv8 Neural Network xgboost algorithm

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