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Towards the use of data-driven methods for indoor airflow field reconstruction: A systematic review

JOURNAL OF BUILDING ENGINEERING 2025年 106卷

作者： Olivas, Ana Claudinne Yee, Jurng-Jae Dong A Univ Dept ICT Integrated Safe Ocean Smart Cities Engn Busan South Korea

Indoor airflow field reconstruction is vital for ensuring a safe and healthy environment for indoor occupants as people spend more than 90 % of their time indoors. This requires understanding indoor airflow dynamics, which is crucial for efficient and effective heating, ventilation, and air conditioning. This review highlights the emergence, applications, advantages, and limitations of data-driven methods within the context of indoor airflow field reconstruction. Findings show that traditional methods such as computational fluid dynamics, zonal, and multizone modelling remain trusted for indoor airflow field reconstruction. However, in 2019, the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the urgent exploration of data-driven methods (DDMs) to address the concerns of high computational cost and time. This gave rise to new challenges, such as the need for large training datasets and addressing the blackbox nature of data-driven methods, which can affect user trust and the adaptability of these methods to other domains. Furthermore, while adoption of DDMs grows, there is still a lack of standardized evaluation frameworks, given their largely empirical nature. Future research can investigate DDMs towards enhancing the robustness, scalability, and interpretability of datadriven methods for indoor airflow field reconstruction.

关键词： data-driven methods Indoor airflow Review

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A Review of data-driven methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions

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ENERGIES 2024年第18期17卷 4641页

作者： Luo, Shu-Long Shi, Xing Yang, Feng Tongji Univ Coll Architecture & Urban Planning Shanghai 200092 Peoples R China Minist Educ Key Lab Ecol & Energy Saving Study Dense Habitat Shanghai 200092 Peoples R China

In order to reduce the contribution of the building sector to global greenhouse gas emissions and climate change, it is important to improve the building performance through retrofits from the perspective of carbon emission reductions. data-driven methods are now widely used in building retrofit research. To better apply data-driven techniques in low-carbon building retrofits, a better understanding is needed of the connections and interactions in optimization objectives and parameters, as well as optimization methods and tools. This paper provides a bibliometric analysis of selected 45 studies, summarizes current research hotspots in the field, discusses gaps to be filled, and proposes potential directions for future work. The results show that (1) the building-performance optimization (BPO) process established through physical simulation methods combines the site, retrofit variables, and carbon-related objectives, and the generated datasets are either directly processed using multi-objective optimization (MOO) algorithms or trained as a surrogate model and iteratively optimized using MOO methods. When a sufficient amount of data is available, data-driven methods can be used to develop mathematical models and use MOO methods for performance optimization from the perspective of building carbon emission reductions. (2) The benefits of retrofits are maximized by holistically taking environmental, economic, and social factors into account;from the perspectives of carbon emissions, costs, thermal comfort, and more, widely adopted strategies include improving the thermal performance of building envelopes, regulating HVAC systems, and utilizing renewable energy. (3) The optimization process based on data-driven methods, such as optimization algorithms and machine learning, apply mathematical models and methods for automatic iterative calculations and screen out the optimal solutions with computer assistance with high efficiency while ensuring accuracy. (4) Only 2.2% and 6

关键词： carbon emissions reduction building retrofit building-performance optimization data-driven methods bibliometric analysis

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Fault detection of wind turbine system based on data-driven methods: a comparative study

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NEURAL COMPUTING & APPLICATIONS 2024年第17期36卷 10279-10296页

作者： Elshenawy, Lamiaa M. Gafar, Ahmed A. Awad, Hamdi A. Abouomar, Mahmoud S. Menoufia Univ Fac Elect Engn Ind Elect & Control Engn Menoufia 32952 Egypt AlRyada Univ Sci & Technol Fac Comp & Artificial Intelligence Sadat City 32897 Egypt

Fault detection plays a crucial role in ensuring the safety, availability, and reliability of modern industrial processes. This study focuses on data-driven fault detection methods, which have gained significant attention across various industrial sectors due to the rapid development of industrial automation technologies and the availability of extensive datasets. The objectives of this paper are to comprehensively review and present the theoretical foundations of widely used data-driven fault detection approaches. Specifically, these approaches are applied to fault detection in wind turbine systems, with performance evaluation conducted using multiple statistical measures. The data utilized in this study were collected from a simulated benchmark of a wind turbine system. The data-driven methods are tested under the assumption that the wind turbine operates in a steady-state region. Additionally, a comparative study is conducted to identify and discuss the primary challenges associated with the practical application of these methods in real-world scenarios. Simulation results show the effectiveness and efficacy of data-driven approaches concerning the sensitivity and robustness of wind turbine sensor faults as applied in practical industrial environments.

关键词： Fault detection Statistical process monitoring data-driven methods Wind turbine system.

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A Comprehensive Review on data-driven methods of Lithium-Ion Batteries State-of-Health Forecasting

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WILEY INTERDISCIPLINARY REVIEWS-data MINING AND KNOWLEDGE DISCOVERY 2025年第2期15卷

作者： Pham, Thien Bui, Hung Nguyen, Mao Pham, Quang Vu, Vinh Le, Triet Quan, Tho Ho Chi Minh City Univ Technol HCMUT Fac Comp Sci & Engn Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam

Lithium-ion batteries are widely used in moving devices due to their many advantages compared to other battery types. The prevalence of Lithium-ion batteries is evident, playing its clear role in the operation of small devices as well as large systems such as electric vehicles, flying devices, mobile devices, and more. Monitoring lithium-ion battery health is crucial for assessing, minimizing degradation, preventing explosions, and enabling timely replacements. Assessing health often involves predicting state-of-health (SoH) or remaining useful life (RUL), with numerous studies dedicated to this field. Hence, many research studies have been conducted on predicting SoH, with a primary focus on data-driven methods based on machine learning, owing to the recent advancements in artificial intelligence (AI) techniques. To provide a systematic overview of the trends in this emerging problem, we present a comprehensive survey of classified SoH forecasting methods, with a primary focus on data-driven approaches. The paper also offers an in-depth focus on recent advancements in deep learning (DL) models, an area that has not been thoroughly discussed previously. Furthermore, we highlight the importance of input features and emphasize the critical role of temporal attributes incorporated into the models. The insights provided in this paper offer readers a comprehensive understanding of the field, equipping them to effectively advance related future work.

关键词： data-driven methods lithium-ion battery state-of-health time-series forecasting

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State of health estimation of lithium-ion batteries based on data-driven methods with a selected charging voltage interval

AIMS ENERGY

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AIMS ENERGY 2025年第2期13卷 290-308页

作者： Sun, Junguang Zhang, Xiaodong Cao, Wenrui Bo, Lili Liu, Changhai Wang, Bin China Natl Elect Apparat Res Inst Co Ltd State Key Lab Environm Adaptabil Ind Prod Guangzhou 510663 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China China Aviat Lithium Battery Co Ltd Luoyang 471003 Peoples R China

Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance for achieving efficient energy management in the overall battery energy storage system. Traditional data-driven methods for the SOH estimation of lithium-ion batteries usually require an enormous amount of data from the whole charging phase, which leads to poor performance in both computational efficiency and computational cost. To address this issue, this paper proposes the SOH estimation methods for lithium-ion batteries based on the limited data from a selected charging voltage interval. First, this study uses incremental capacity curves and Pearson correlation analysis to select an optimal and limited charging voltage interval that is the most relevant to lithium-ion battery degradation. Then, the SOH estimation based on two typical data-driven methods, including random forest regression (RFR) and support vector regression (SVR), would be implemented with the selected charging voltage interval. Results show that both the RFR and the SVR methods can achieve excellent accuracy, while each has its own irreplaceable advantages. However, compared with other voltage intervals using the two data-driven methods, the corresponding SOH estimation with the selected charging voltage interval shows the best performance. Hence, the data-driven methods based on the selected charging voltage interval have significant potential and advantages in the field of lithium-ion battery SOH estimation.

关键词： lithium-ion battery state of health data-driven methods random forest regression support vector regression

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data-driven methods for Battery SOH Estimation: Survey and a Critical Analysis

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IEEE ACCESS 2021年 9卷 126903-126916页

作者： Oji, Tsuyoshi Zhou, Yanglin Ci, Song Kang, Feiyu Chen, Xi Liu, Xiulan Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen 519071 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Key Lab Graphene Based Mat Engn Lab Functionalized Carbon Mat Shenzhen 518055 Peoples R China Beijing Elect Power Res Inst Beijing 100075 Peoples R China

State-of-health (SOH) estimation is a critical factor in ensuring the efficiency, reliability, and safety of lithium-ion batteries (LIBs) in electric vehicles (EVs). However, due to the complexity of electrochemical processes in batteries and the dynamics of working conditions, it is challenging to estimate SOH accurately, especially in real-world EV application scenarios. Thus, various data-driven methods with robust and adaptive features for SOH estimation have been widely proposed in the current literature. However, there is a lack of a comprehensive investigation and performance comparison of those methods, which makes them hard to be adopted in practice. Hence, in this paper, we have studied current major data-driven methods with real-world EV battery data to evaluate the performance. Besides, we summarize each method's advantages and limitations with the consideration of the critical features required to achieve accurate SOH estimation in real-world applications. Hopefully, this paper provides a practical insight into the related fields.

关键词： Mathematical model Estimation Integrated circuit modeling data models State of charge Kalman filters Degradation Lithium-ion battery state of health data-driven methods state estimation

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Identification of Future Skills Using data-driven methods: A Systematic Literature Review and Directions for Future Research 57

Identification of Future Skills Using Data-Driven Methods: A...

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57th Hawaii International Conference on System Sciences (HICSS)

作者： Brasse, Julia Univ Ulm Ulm Germany

ISBN: (纸本)9780998133171

The labor market is rapidly changing due to swift technological progress, economic disruptions, and societal demands, leading to frequent job transitions and skill disruptions. Understanding the skills required to tackle future challenges, commonly referred to as future skills, is of paramount importance. The availability of accessible data and rapid technological advancements have given rise to an emerging research field focused on data-driven methods for identifying future skills. However, there is a lack of cumulative knowledge. By conducting a systematic review of 118 papers published in diverse interdisciplinary outlets, we examine and describe the applied analysis methods and research objectives, aiming to enhance understanding within this field of research. Furthermore, we identify four distinct directions for future research.

关键词： Future skills data-driven methods job advertisements literature review

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Enhancing Spatial Problem-Solving Through data-driven methods: A Practical Approach Utilizing Immersive Technologies and Multimodal Physical Tracking 29

Enhancing Spatial Problem-Solving Through Data-Driven Method...

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29th ACM Annual International Conference on Intelligent User Interfaces (IUI)

作者： Bendig, Henner Flensburg Univ Appl Sci CIVU Ctr Interact Visualizat & Usabil Flensburg Germany

ISBN: (纸本)9798400705090

This project aims to explore how data-driven methods can be used to enhance humans' problem-solving skills in spatial tasks and how they can be integrated into immersive tutoring systems. The goal is to provide an enhanced, state-dependent approach that is also applicable outside the laboratory, within real-life scenarios. Digital tutoring and learning systems are typically expert systems and follow a predefined training routine. Ongoing research concentrates on adaptive methods within affective- and cognition-aware systems, yet there is comparatively less investigation into recognizing and adapting problem-solving strategies. Spatial skill have been shown to be especially important for STEM (Science, Technology, Engineering, Mathematics) disciplines in general. Due to their design, immersive systems are particularly suitable for training of spatial skills and are thus frequently utilized for this.

关键词： Immersive Technologies Spatial Skills data-driven methods

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An Investigation of Contemporary data-driven methods Applied to Complex Systems

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IEEE ACCESS 2022年 10卷 134744-134757页

作者： Dwivedi, Saumitra Torres, Ricardo Da Silva Hameed, Ibrahim A. Karlsen, Anniken Susanne T. Norwegian Univ Sci & Technol Dept ICT & Nat Sci N-6025 Alesund Norway Norwegian Univ Sci & Technol Dept Comp Sci N-7491 Trondheim Norway Wageningen Univ & Res Wageningen Data Competence Ctr Farm Technol Grp NL-6708 PB Wageningen Netherlands

Understanding complex systems by the help of modeling, simulation, and control is a well-known challenge across several application domains. As such, these type of systems are not amenable to empirical models, typically due to their dynamic and non-linear nature. data-driven methods, have in recent years gained traction regarding their application to such complex systems. This paper presents findings from a systematic literature review of data-driven methods applied to complex systems in recent years. The review is a result of a search strategy resulting in 1106 scientific publications, out of which forty one were identified as primary studies. It is our goal that the review findings will guide researchers with an evidence-base regarding data-driven methods, thereby equipping them with an arsenal of applied methods across various application domains. Also, the objective is to construct a knowledge-base based on these methods' contributions while applied to different types of problems. Additionally, the review findings may also guide researchers to realize potential gaps for future research.

关键词： Complex systems data-driven methods systematic map systematic review

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data-driven methods distort optimal cutoffs and accuracy estimates of depression screening tools: a simulation study using individual participant data

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JOURNAL OF CLINICAL EPIDEMIOLOGY 2021年 137卷 137-147页

作者： Bhandari, Parash Mani Levis, Brooke Neupane, Dipika Patten, Scott B. Shrier, Ian Thombs, Brett D. Benedetti, Andrea Lady Davis Inst Med Res Jewish Gen Hosp Montreal PQ Canada McGill Univ Dept Epidemiol Biostat & Occupat Hlth Montreal PQ Canada Keele Univ Sch Med Ctr Prognosis Res Keele Staffs England Univ Calgary Dept Community Hlth Sci Calgary AB Canada McGill Univ Dept Family Med Montreal PQ Canada McGill Univ Dept Med Montreal PQ Canada McGill Univ Dept Psychiat Montreal PQ Canada McGill Univ Dept Psychol Montreal PQ Canada McGill Univ Dept Educ & Counselling Psychol Montreal PQ Canada McGill Univ Biomed Ethics Unit Montreal PQ Canada

Objective: To evaluate, across multiple sample sizes, the degree that data-driven methods result in (1) optimal cutoffs different from population optimal cutoff and (2) bias in accuracy estimates. Study design and setting: A total of 1,000 samples of sample size 100, 200, 500 and 1,000 each were randomly drawn to simulate studies of different sample sizes from a database (n = 13,255) synthesized to assess Edinburgh Postnatal Depression Scale (EPDS) screening accuracy. Optimal cutoffs were selected by maximizing Youden's J (sensitivity+specificity-1). Optimal cutoffs and accuracy estimates in simulated samples were compared to population values. Results: Optimal cutoffs in simulated samples ranged from >= 5 to >= 17 for n = 100, >= 6 to >= 16 for n = 200, >= 6 to >= 14 for n = 500, and >= 8 to >= 13 for n = 1,000. Percentage of simulated samples identifying the population optimal cutoff (>= 11) was 30% for n = 100, 35% for n = 200, 53% for n = 500, and 71% for n = 1,000. Mean overestimation of sensitivity and underestimation of specificity were 6.5 percentage point (pp) and -1.3 pp for n = 100, 4.2 pp and -1.1 pp for n = 200, 1.8 pp and -1.0 pp for n = 500, and 1.4 pp and -1.0 pp for n = 1,000. Conclusions: Small accuracy studies may identify inaccurate optimal cutoff and overstate accuracy estimates with data-driven methods. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Optimal cutoff Accuracy estimates Bias Cherry-picking data-driven methods Depression

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