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A data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems

CSEE Journal of Power and Energy Systems 2022年第3期8卷 789-800页

作者： Hongjun Gao Zhenyu Liu Youbo Liu Lingfeng Wang Junyong Liu College of Electrical Engineering Sichuan UniversityChengdu 610065China Department of Electrical Engineering and Computer Science University of Wisconsin-MilwaukeeMilwaukeeWI 53211USA

A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system(UIES).Meanwhile,the uncertainties of renewable energy resources(e.g.,wind energy)also bring increased challenges to the operation of *** this study,a typical two-stage datadriven distributionally robust operation(DDRO)model based on finite scenarios is proposed for UIES including power,gas and heat networks to obtain a salient strategy from both an economic and robustness *** the first stage,the forecasted information for wind power is especially included to improve the economic aspect of robust *** worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units(such as gas turbine,power to gas equipment,electric boiler and gas boiler).Moreover,norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind *** whole two-stage model is solved by the column-and-constraint generation(CCG)***,case studies are conducted to show the performance of the proposed model and various *** Terms-data-driven methods,distributionally robust optimization,urban integrated energy system,wind power.

关键词： data-driven methods distributionally robust optimization urban integrated energy system wind power

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Geotechnical data-driven possibility reliability assessment

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Computers and Geotechnics 2025年 185卷

作者： Alessandro Tombari Luciano Stefanini Giovanni Li Destri Nicosia Liam M.J. Holland Marcus Dobbs Department of Engineering University of Exeter North Park Road Exeter EX4 4QF UK DESP University of Urbino Via A. Saffi 42 Urbino (PU) 61029 Italy COWI A/S Parallelvej 2 Køgens Lyngby 2800 Denmark British Geological Survey Nicker Hill Keyworth Nottingham NG12 5GG UK

Managing scarce, incomplete, or corrupted data is a persistent challenge in geotechnical engineering, often leading to conservative designs. However, the ongoing digitalization has enabled access to large, national-scale databases of indirect geotechnical data containing both qualitative and quantitative information, which can be exploited to support optioneering, site characterization, and design. Based on a newly proposed concept of possibilistic data-driven reliability, this Technical Note outlines a practical, fast, and accessible implementation procedure that does not require specialized expertise. Step-by-step guidance is provided for reliability-based assessment and design of geotechnical problems, ensuring consistency with standard code safety prescriptions. The procedure demonstrates how to utilize possibility distributions generated from Big Indirect databases managed by third-party administrators, such as the British Geological Survey, to derive design input values for deterministic evaluations of geotechnical capacity or limit state domains. Engineering judgement is rigorously incorporated through a three-tier ‘degree of understanding’ framework worked example of an axially-loaded pile in bilayer soil, characterized using cone penetration test data, is also provided.

关键词： Possibility theory data-driven methods Fuzzy clustering and partitioning Degree of understanding Reliability assessment Piles

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data-driven state of health and state of safety estimation for alternative battery chemistries — A comparative review focusing on sodium-ion and LFP lithium-ion batteries

Future Batteries

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Future Batteries 2025年 5卷

作者： Erik Vanem Shuai Wang DNV Group Research and Development Høvik Norway Department of Mathematics University of Oslo Oslo Norway

This paper presents a comprehensive survey on data-driven online estimation of state of health (SoH) for alternative battery chemistries for maritime applications, with a particular focus on LFP lithium-ion and sodium-ion types of batteries. In addition, the emerging concept of state of safety (SoS), a critical yet underexplored metric for maritime battery systems, is explored. Building on previous work on nickel–manganese–cobalt (NMC) lithium-ion batteries, this study evaluates the applicability of existing SoH estimation methodologies to alternative chemistries. The findings suggest that similar data-driven approaches, including empirical and semi-empirical methods, physics-based models, machine learning models, and hybrid approaches, can be employed across these chemistries. However, the methods require calibration, fine-tuning, and validation for each specific battery type. It is believed that SoS holds significant potential for maritime applications, provided it incorporates a relevant set of safety sub-functions with properly defined thresholds and warning criteria. Its integration into real-time monitoring systems appears feasible, given continuous measurement of relevant inputs. However, further research is recommended on how to best account for interdependencies between the various safety sub-function and correlations in the input data as well as how to account for the effect of degradation on SoS. Additionally, it seems reasonable to investigate whether some kind of memory could be incorporated in order to account for the experience of previous abusive conditions.

关键词： State of health data-driven methods Battery degradation Sodium-ion batteries Battery diagnostics State of safety

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Concrete corrosion in wastewater systems:Prediction and sensitivity analysis using advanced extreme learning machine

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Frontiers of Structural and Civil Engineering 2021年第2期15卷 444-460页

作者： Mohammad ZOUNEMAT-KERMANI Meysam ALIZAMIR Zaher Mundher YASEEN Reinhard HINKELMANN Department of Water Engineering Shahid Bahonar University of KermanKerman 76169-13439Iran Department of Civil Engineering Hamedan BranchIslamic Azad UniversityHamedan 65181-15743Iran Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam Department of Civil Engineering Technische Universitat BerlinBerlin 13355Germany

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental *** study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning *** models include three different types of extreme learning machines,including the standard,online sequential,and kernel extreme learning machines,in addition to the artificial neural network,classification and regression tree model,and statistical multiple linear regression *** reported values of concrete mass loss for six different types of concrete are the target values of the machine learning *** input variability was assessed based on two scenarios prior to the application of the predictive *** the first assessment,the machine learning models were developed using all the available cement and concrete mixture input variables;the second assessment was conducted based on the gamma test approach,which is a sensitivity analysis ***,the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different *** adopted methodology attained optimistic and reliable modeling *** online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.

关键词： sewer systems environmental engineering data-driven methods sensitivity analysis

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Comparison of auto-tuning methods of PID controllers based on models and closed-loop data 33

Comparison of auto-tuning methods of PID controllers based o...

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33rd Chinese Control Conference (CCC)

作者： Zhang Wei Yang Maying Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9781479947249

Due to changes of operating condition in industrial processes, it is necessary to tune the PID parameters to fulfill the requirement of production. The auto-tuning technology of PID controllers effectively overcomes the shortcomings of human tuning which is time consuming. In this paper several auto-tuning PID methods are discussed and compared, including Ziegler-Nichols tuning Kappa-Tau method, IMC-PID auto-Ruling, auto-tuning of fractional PID controller and data-based FRIT method. Furthermore, the illustrative examples and simulation results are also given. In this way this paper aims to provide useful references and suggestions for practical auto-tuning methods.

关键词： PID Auto-tuning Fractional Order PID Controller Relay Feedback data-driven methods

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Two-scale data-driven design for heat manipulation

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2024年 219卷

作者： Da, Daicong Chen, Wei Boise State Univ Dept Mech & Biomed Engn Boise ID 83725 USA Northwestern Univ Dept Mech Engn Evanston IL 60208 USA

data-driven methods have gained increasing attention in computational mechanics and design. This study investigates a two-scale data-driven design for thermal metamaterials with various functionalities. To address the complexity of multiscale design, the design variables are chosen as the components of the homogenized thermal conductivity matrix originating from the lower scale unit cells. Multiple macroscopic functionalities including thermal cloak, thermal concentrator, thermal rotator/inverter, and their combinations, are achieved using the developed approach. Sensitivity analysis is performed to determine the effect of each design variable on the desired functionalities, which is then incorporated into topology optimization. Geometric extraction demonstrates an excellent matching between the optimized homogenized conductivity and the extraction from the constructed database containing both architecture and property information. The designed heterostructures exhibit multiple thermal meta-functionalities that can be applied to a wide range of heat transfer fields from personal computers to aerospace engineering.

关键词： data-driven methods Thermal metamaterials Design optimization Homogenization Heat manipulation Heat conduction

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A feature-based adaptive combiner for coupling meta-modelling techniques to increase accuracy of river flow prediction

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HYDROLOGICAL SCIENCES JOURNAL 2022年第14期67卷 2065-2081页

作者： Ebrahimi, Ehsan Shourian, Mojtaba Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran

data-driven models are relatively simple but powerful methods for river flow prediction;to use the strengths of each, hybrid model-free methods are proposed. In this study, a feature-based adaptive combiner (FBAC) is introduced that uses features extracted from the data to provide a robust meta-model for river flow prediction. Artificial neural network (ANN), random forest regression (RFR), support vector regression (SVR), and a modified dynamic K-nearest neighbours (DKNN) are used to construct the FBAC. DKNN is incorporated to assess the flexibility of the FBAC in using novel data-driven approaches. The FBAC's performance is evaluated using two years of the daily inflow to the Azad Reservoir in western Iran. Results indicate that the FBAC shows 31.8% and 29.5% improvement in accuracy, in terms of a reduction in the Root Mean Square Error (RMSE) value, compared to the best individual and combined model, respectively. For extreme value prediction, improvements are increased to 35.1% and 32.6%, respectively.

关键词： river flow prediction data-driven methods feature extraction adaptive combination

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A MULTISCALE data-driven STOCHASTIC METHOD FOR ELLIPTIC PDEs WITH RANDOM COEFFICIENTS

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MULTISCALE MODELING & SIMULATION 2015年第1期13卷 173-204页

作者： Zhang, Zhiwen Ci, Maolin Hou, Thomas Y. CALTECH Comp & Math Sci Pasadena CA 91125 USA

In this paper, we propose a multiscale data-driven stochastic method (MsDSM) to study stochastic partial differential equations (SPDEs) in the multiquery setting. This method combines the advantages of the recently developed multiscale model reduction method [M. L. Ci, T. Y. Hou, and Z. Shi, ESAIM Math. Model. Numer. Anal., 48 (2014), pp. 449-474] and the data-driven stochastic method (DSM) [M. L. Cheng et al., SIAM/ASA J. Uncertain. Quantif., 1 (2013), pp. 452-493]. Our method consists of offline and online stages. In the offline stage, we decompose the harmonic coordinate into a smooth part and a highly oscillatory part so that the smooth part is invertible and the highly oscillatory part is small. Based on the Karhunen-Loeve (KL) expansion of the smooth parts and oscillatory parts of the harmonic coordinates, we can derive an effective stochastic equation that can be well-resolved on a coarse grid. We then apply the DSM to the effective stochastic equation to construct a data-driven stochastic basis under which the stochastic solutions enjoy a compact representation for a broad range of forcing functions. In the online stage, we expand the SPDE solution using the data-driven stochastic basis and solve a small number of coupled deterministic partial differential equations (PDEs) to obtain the expansion coefficients. The MsDSM reduces both the stochastic and the physical dimensions of the solution. We have performed complexity analysis which shows that the MsDSM offers considerable savings over not only traditional methods but also DSM in solving multiscale SPDEs. Numerical results are presented to demonstrate the accuracy and efficiency of the proposed method for several multiscale stochastic problems without scale separation.

关键词： stochastic partial differential equations multiscale problems data-driven methods Karhunen-Loeve expansion uncertainty quantification model reduction

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An unsupervised data completion method for physically-based data-driven models

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COMPUTER methods IN APPLIED MECHANICS AND ENGINEERING 2019年 344卷 120-143页

作者： Ayensa-Jimenez, Jacobo Doweidar, Mohamed H. Sanz-Herrera, Jose A. Doblare, Manuel Univ Zaragoza Mech Engn Dept Zaragoza Spain Univ Zaragoza Aragon Inst Engn Res I3A Zaragoza Spain Ctr Invest Biomed Red Bioingn Biomat & Nanomed CI Madrid Spain Univ Seville Sch Engn ETSI Seville Spain

data-driven methods are an innovative model-free approach for engineering and sciences, still in process of maturation. The idea behind is the combination of data analytics techniques, to handle the huge amount of data derived from continuous monitoring or experimental measurements, and of the constraints imposed by universal physical laws, particular to the field in hands. A well-known problem in the former corresponds to the quality and completeness of the available data that, sometimes, are so poor that make the predictions useless. In data-driven simulation-based engineering and sciences (DDSBES), the intrinsic physical constraints may help in completing the missing data in a more precise manner, by forcing them to remain in the manifold defined by the physical laws. In this work, a suitable imputation method to complete incomplete data that preserves the data context-dependent structure is presented. This is accomplished by enforcing the data to fulfill the set of physical constraints, specific to the problem. For this purpose, a generalization of the weighted mean concept is proposed, where the distance to the admissible points (in a physical sense) is used as a weighting function to get the optimal candidate. The method is evaluated in a classical regression problem, where it is compared with other standard methods, showing better results. Then, its application is illustrated in two data-driven problems, where no filling data procedure has been yet proposed, showing good predictive capability, provided that the data are close enough to the actual system state. (C) 2018 Elsevier B.V. All rights reserved.

关键词： data-driven methods data completion Statistical imputation Weighted mean Computational mechanics

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Using a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2022年第13期26卷 6625-6649页

作者： Ghasemlounia, Redvan Istanbul Gedik Univ Dept Civil Engn Engn Fac Istanbul Turkey

In this study, the SCVW (a novel type of Sharp-Crested V-notch weirs) is employed for more experimentally and theoretically investigations. The length of the hydraulic jump at downstream of the SCVW (L-jSCVW) is measured via new supplementary experimental datasets via most popular vertex angles theta (128 degrees and 60 degrees). The experiments are conducted under aerated, steady, and free overflow conditions in an open channel for large physical models. To assess the variations of L-jSCVW versus the theta, extensive laboratory work is performed at different discharges (Q), diverse triangular segments number (N-st) in the SCVW, and various tailgate angles (Phi). Via dimensional analysis and pre-processing method, most effectual parameters on the L-jSCVW were obtained as Fr-1 (approaching Froude number), Re-1 (incoming Reynolds number), and y(2)/y(1) (relative sequent depths). Based on the experimental results, by increasing the values of Fr-1 and Re-1, the values of L-jSCVW noticeably increased. Three types of data-driven models (DDMs), namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) are developed to estimate the L-jSCVW. The suggested hybrid model is a coupled form of SVR with ant colony optimization (ACO) algorithm, which is used to enhance the estimation precision of the L-jSCVW. According to the attained statistical indices (determination coefficient (R-2) = 0.98, root mean square error (RMSE) = 0.191, and value of total grade (TG) = 19.78) and scatter plots, the hybrid SVR-ACO model was determined as the most superior method to estimate the L-jSCVW with high accuracy.

关键词： Experimental model length of hydraulic jump the SCVW open channel flow data-driven methods

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