[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127390-en":3,"doc-seo-127390-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},127390,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Development of a Machine Learning-Based Framework for Predicting Failures in Heat Supply Networks - Research Article","The study develops a machine learning-driven predictive framework for early detection of emergency conditions in heat supply networks, aiming to reduce economic loss, environmental impact, and safety risks. A LightGBM gradient boosting approach is used with real operational sensor data, covering temperature, pressure, flow, and vibration. The workflow includes data preprocessing, feature engineering with SHAP-based interpretability, and hyperparameter optimization via grid search and 5-fold cross-validation. Results show 85% accuracy, F1 of 0.82, and ROC-AUC of 0.96, outperforming logistic regression and decision trees.","Development of a machine learning-based framework for predicting failures in heat supply networks  \nDauren Darkenbayev1,4, Gulnar Balakayeva2, Uzak Zhapbasbayev3, Mukhit Zhanuzakov2  \n1Department of Computational Sciences and Statistics, Faculty of Mechanics and Mathematics, Al-Farabi Kazakh National University,  \nAlmaty, Kazakhstan  \n2Department of Computer Science, Faculty of Information Technologies,Al-Farabi Kazakh National University, Almaty, Kazakhstan 3Laboratory “Modeling in Energy Sector”, Satbayev University, Almaty, Kazakhstan 4Department of Computer Science, Institute of Physics, Mathematics and Digital Technologies Kazakh National Women's Teacher  \nTraining University, Almaty, Kazakhstan  \nArticle history:  \nReceived Mar 21, 2025 Revised Aug 20, 2025 Accepted Sep 27, 2025  \nKeywords:  \nAnomaly detection Ensemble learning Heat supply Predictive maintenance Real-time monitoring  \nCorresponding Author:  \nThe increasing complexity and scale of heat supply systems leads to a higher risk of failures, which may cause significant economic and environmental consequences. This study develops a predictive mathematical framework for the early detection of emergency conditions in heat supply networks (HSNs) using machine learning (ML) . The proposed approach is based on the LightGBM gradient boosting (GB) algorithm, chosen for its high accuracy and efficiency in handling large datasets. Real operational data (temperature, pressure, flow, and vibration) were considered. Data preprocessing, feature engineering (including SHAP analysis), and hyperparameter tuning with grid search and 5-fold cross-validation improved prediction quality. The model achieved accuracy of 85%, F1-score of 0.82, and receiver operating characteristic (ROC)-area under the curve (AUC) of 0.96, outperforming logistic regression (LR) and decision trees. The framework may be integrated into monitoring systems for predictive maintenance, reducing downtime and optimizing costs.  \nThis is an open access article under the CC BY-SA license.  \nDauren Darkenbayev  \nDepartment of Computational Sciences and Statistics, Faculty of Mechanics and Mathematics Al-Farabi Kazakh National University  \nAlmaty, Kazakhstan  \nEmail: [dauren.kadyrovich@gmail.com](dauren.kadyrovich@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nModern technological systems such as industrial production, transportation, and energy infrastructure are characterized by increasing complexity and automation [1]–[3] . While this improves efficiency, it also increases vulnerability to failures, leading to economic losses, environmental harm, and safety hazards [4]–[6] . Heat supply networks (HSNs), as critical urban infrastructure, are especially prone to risks due to their wide distribution and dynamic operation. Early prediction of pre-emergency states in HSNs is therefore a research priority.  \nTraditional diagnostic and maintenance methods (rule-based systems and statistical models) depend on thresholds and expert knowledge [7]–[10], but they are insufficient for nonlinear and high-dimensional data. For example, Rahal et al. [11] analyzed heat losses but noted scalability limits. Ukoba [12] applied time-series anomaly detection, but robustness was low under changing loads.  \nMachine learning (ML) offers more flexibility. Support vector machines (SVM) and random forests (RF) have been used for anomaly detection with moderate success [13], [14] . Artificial neural networks  \n(ANNs) improve flexibility but require high resources and lack interpretability [15] . Gradient boosting (GB), particularly LightGBM, shows strong predictive ability in power grids and industrial systems. LightGBM efficiently handles large datasets and complex nonlinear dependencies, using gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB) . Despite these strengths, little research applies GB to HSN fault prediction [16] .  \nThis study addresses this gap by proposing a LightGBM-based predictive frame","cbCaisD4dPNk7EgA","https://ap.wps.com/l/cbCaisD4dPNk7EgA","pdf",605459,1,"English","en",105,"# Introduction\n## Motivation and problem statement\n## Limitations of traditional methods\n## Role of machine learning\n# Materials and Methods\n## Predicting failures in heat networks\n## Gradient boosting method\n## Dataset description\n# Results and Discussion","[{\"question\":\"What problem does the proposed framework address in heat supply networks?\",\"answer\":\"It targets early prediction of pre-emergency conditions by detecting likely failures in heat supply networks before they escalate into incidents.\"},{\"question\":\"Which machine learning model is used, and why?\",\"answer\":\"The framework uses LightGBM gradient boosting because it offers high accuracy and efficiency for large datasets and complex nonlinear dependencies.\"},{\"question\":\"How are predictions validated and how well does the model perform?\",\"answer\":\"Performance is improved using grid search and 5-fold cross-validation, achieving 85% accuracy, F1-score 0.82, and ROC-AUC of 0.96.\"}]","Development of a Machine Learning-Based Framework for Predicting Failures in Heat Supply Networks - 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