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data driven optimization for electric vehicle charging station locating and sizing with charging satisfaction consideration in urban areas

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IET RENEWABLE POWER GENERATION 2022年第12期16卷 2630-2643页

作者： Hu, Dandan Huang, Liu Liu, Chen Liu, Zhi-Wei Ge, Ming-Feng South Cent Univ Nationalities Sch Management Wuhan 430074 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3001 Australia Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China

The lagging development of charging infrastructure seriously restricts the penetration of electric vehicles. Inadequate and unreasonably setting of charging stations (CS) aggravates charging anxiety of electric vehicle(EV) users. Location and sizing model is developed to expand existing CS to maximize EV charging service satisfaction in urban areas. The model captures charging behavior from massive GPS trajectory extraction data of electric taxis. Satisfaction from EV users with charging service is measured by whether the respond time from an electric vehicle driver issuing charging demand signal to completion of charging is within a threshold value. The respond time is considered to include seeking time, queuing time and charging time, which are determined by the decisions of the CS location, capacity and charging types. Heuristic algorithms, including the greedy, greedy-substitute and two-layer genetic algorithms, are designed to solve the problem. Algorithms are simulated in a large scale random computational environment. Finally, a practical case of Shenzhen is investigated. It is found that charger pooling is more effective than decentralized layout. Moreover, it is critical to weigh the cost and charging rate of different types of chargers with budget restriction. High charging rate chargers are not necessarily effective than slow ones in reducing charging respond time.

关键词： CS location GPS trajectory extraction data lagging development two-layer genetic algorithms electric taxis Shenzhen greedy-substitute sizing model station aggravates charging anxiety electric vehicle charging data driven optimization high charging rate chargers electric vehicle driver greedy algorithms EV charging service satisfaction genetic algorithms

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A Hybrid Approach to Implement data driven optimization into Production Environments 21st

A Hybrid Approach to Implement Data Driven Optimization into...

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21st International Conference on Business Information Systems (BIS)

作者： Ghabri, Rachaa Hirmer, Pascal Mitschang, Bernhard Univ Stuttgart Inst Parallel & Distributed Syst Univ Str 38 D-70569 Stuttgart Germany

ISBN: (纸本)9783319939315;9783319939308

The potential of data analytics to improve business processes is commonly recognized. Despite the general enthusiasm, the implementation of data-driven methods in production environments remains low. Although established models, such as CRISP-DM, offer a structured process in order to deploy data analytics in the industry, manufacturing companies still need to choose a starting point, assess the business benefit, and determine a pragmatic course of action. In this paper, we introduce an approach to handle these issues based on a case study from automotive manufacturing. The results are discussed based on a set of requirements derived from the case study.

关键词： data driven optimization Production environment Top-down Bottom-up

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A data driven optimization for Multi-energy Adaptive Scheduling in Microgrid

A Data Driven Optimization for Multi-energy Adaptive Schedul...

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58th IEEE/IAS Industrial and Commercial Power Systems Technical Conference Asia (IEEE I and CPS Asia)

作者： Wang, Lei Yan, Ziyi Chen, Yu CCCC Mech & Elect Engn Co Ltd Dept Sci Technol & Digitizat Beijing Peoples R China Qingdao Univ Coll Comp Sci Technol Qingdao Peoples R China

ISBN: (数字)9781665450669

ISBN: (纸本)9781665450669

This paper deals with energy management in a microgrid dedicated to the power consumption in highway. A data driven optimization method, INNGA, is applied to self-consistent energy scheduling. The goal of optimization is to find the best ratio of consumption of each type energy over time. Meanwhile, the adaptive scheduling performed in INNGA will use as much renewable energy as possible to reduce carbon emissions.

关键词： Integrated Multi-Energy Conversion data driven optimization Adaptive Scheduling

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Parametric data-driven optimization approach on plasmonic based ring resonator

Parametric data-driven optimization approach on plasmonic ba...

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3rd International Conference on Advancement in Nanoelectronics and Communication Technologies (ICANCT)

作者： Sharma, Priyanka Zafar, Rukhsar Pandey, Rahul Swami Keshvanand Inst Technol Management & Gramoth Dept Elect & Commun Engn Jaipur India

In this paper, a plasmonic based ring resonator is being investigated. The metallic portion of plasmonic waveguide is chosen to be silver while the insulating material is air. The structure is numerically analyzed through Finite Difference in Time Domain (FDTD) method. To accurately model the relationship between the design parameter (radius of the ring) and the resonance condition of the resonator, a Machine Learning (ML) approach is applied. The ML model is trained on a dataset and tested for obtained results. The simulated results show that the proposed model is able to achieve the accuracy of prediction. The data driven optimization technique is being used for finding out the appropriate resonance condition of ring resonator. The proposed model is able to achieve good prediction accuracy (>90 %). The predicted result can be utilized in designing desired plasmonic devices like sensors, splitter, optical gates etc. (c) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International Con-ference on "Advancement in Nanoelectronics and Communication Technologies".

关键词： Plasmonics Waveguide Ring resonator Machine learning data driven optimization

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Virtualization resource scheduling and optimization method based on swarm intelligent systems

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2025年 25卷

作者： Zhao, Jun Tiangong Univ Tianjin 300387 Peoples R China

Efficient scheduling of virtualized resources can not only meet the service needs of users, but also achieve the optimal allocation of resources and the stable operation of the system. However, due to the dynamic and diversity of virtualized resources, the traditional scheduling methods have been difficult to meet the actual needs. Therefore, a virtual resource scheduling and optimization method based on Swarm Intelligence System (SIS) is proposed in this paper. The core idea of this method is to transform the Virtualized Resource Scheduling (VRS) problem into a multi-objective optimization problem, and use the particle swarm optimization algorithm of SIS to search for the optimal solution. By updating the speed and position of the particles, the scheduling scheme is optimized iteratively to maximize the utilization of resources and optimize the performance of the system. The experimental results show that the SIS-based virtual resource scheduling method can significantly improve the resource utilization and system performance while meeting the needs of users. Compared with other scheduling methods, this method has better adaptability and robustness, and provides a new solution for virtualization resource scheduling in cloud computing environment.

关键词： Virtualized resource scheduling Swarm intelligence systems optimization method data driven optimization Feature learning

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Learning from data's evolution in normal equations: the DECK-principle and equations' adjustment

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STATISTICS AND COMPUTING 2025年第4期35卷 1-7页

作者： Yatracos, Yannis G. Limassol Cyprus

An algorithmic framework is proposed to improve stochastic equations obtained with principle/method, P.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal{P}.$$\end{document} The framework is motivated by the way neurons are processing information. In each neuron, data Evolves Creating Knowledge (DECK) transmitted to the next neuron for processing. In stochastic equations, DECK is observed when new information, i.e. obtained solutions, replace parameters and the remaining, evolved equations cannot be derived with P,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal{P},$$\end{document} deviate from zero and need adjustment. In statistical inference, the DECK principle was not allowed by Fisher due to the unique distribution used in MLE. However, equations' adjustment with an evolved distribution due to DECK improved maximum likelihood and method of moments estimates, that are either biased or inconsistent. The Neyman-Scott (Econometrica 16(1):1-32, 1948) problem for the MLE of the common variance, psi,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\psi ,$$\end{document} for m normal models which holds also for the moments' estimate for all models, was not solved so far with Deep Learning, and is due to neglecting DECK and the adjustment of the psi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek

关键词： data driven optimization Deep learning Evolutionary algorithms Nuisance parameters Updated likelihood equations Updated moments equations

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Enhanced MDT-Based Performance Estimation for AI driven optimization in Future Cellular Networks

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IEEE ACCESS 2020年 8卷 161406-161426页

作者： Qureshi, Haneya Naeem Imran, Ali Abu-Dayya, Adnan Univ Oklahoma Elect & Comp Engn Dept Tulsa OK 74135 USA Qatar Univ Dept Elect Engn Doha Qatar

Minimization of drive test (MDT) allows coverage estimation at a base station by leveraging measurement reports gathered at the user equipment (UE) without the need for drive tests. Therefore, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in future cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. We characterize three key types of errors in MDT-based coverage estimation that stem from inaccurate user positioning, scarcity of user reports and quantization. For the first time, the presented analysis shows existence of joint interplay between these errors on coverage estimation that result from inter-dependency between positioning error and bin width. The analysis also shows that there exists an optimal bin width for given user positioning inaccuracy and user density that minimizes the overall error in MDT-based estimated coverage. Utility of our framework is presented by addressing two applications from network optimization perspective: determining optimal bin width to maximize accuracy of MDT-based coverage estimation and its calibration to further improve its accuracy.

关键词： Estimation Quantization (signal) Global Positioning System Base stations Artificial intelligence optimization Uncertainty Minimization of drive test autonomous coverage estimation optimal bin width sparse data coverage calibration data driven optimization

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Development of a dynamic model for natural ventilated photovoltaic components and of a data driven approach to validate and identify the model parameters

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SOLAR ENERGY 2016年 129卷 310-331页

作者： Cipriano, J. Houzeaux, G. Mor, G. Eicker, U. Carbonell, J. Danov, S. CIMNE Bldg Energy & Environm Grp CIMNE UdL Classroom C Pere de Cabrera S-N Lleida 25001 Spain Barcelona Supercomp Ctr BSC CNS Barcelona 08034 Spain Univ Appl Sci Stuttgart Res Ctr Sustainable Energy Technol Schellingstr 24 D-70174 Stuttgart Germany CIMNE Bldg Energy & Environm Grp C Rambla St Nebridi 22 Terrassa 08222 Spain

In the development of dynamic models for the energy performance evaluation of building integrated natural ventilated PV components there are still many open questions regarding the uncertainty of the estimated parameters of the models. Traditionally, the dynamic models for these complex components are derived from the heat transfer balance equations, and the unknown heat transfer coefficients (convection and radiation), the solar properties of the materials or the pressure coefficients for the air mass flow rate balance, are assigned based on literature or on manufacturer prescriptions. However, there is a lack of systematic methods able to validate the simulation outputs with the measured data, taking into consideration the uncertainty of the parameters and their effect over the results. This research is focused on the development of a dynamic simulation model for a PV ventilated component, and on the application of a data-driven iterative approach to identify the unknown parameters, to evaluate their influence in the simulation outputs and finally, to determine the deviations of the simulations outputs against the measured data. During the identification process, 43 unknown parameters are detected and 13 of them are categorized as strong parameters. The implemented data driven approach is able to achieve high goodness of fit with the measured data and it is recommended to analyses which aim at evaluating the influence of some component parameters or the thermal and electrical energy produced by these natural ventilated PV components. (C) 2016 Published by Elsevier Ltd.

关键词： Building integrated photovoltaics Dynamic simulation modeling data driven optimization Sensitivity analysis Latin Hypercube Monte Carlo

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Teaching a generative model: Mathematical formulation and solution framework

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 130卷 119-126页

作者： Wang, Honggang Zhang, Bo Univ La Verne Dept Appl Business Sci & Econ Coll Business & Publ Management La Verne CA 91750 USA IBM Thomas J Watson Res Ctr Yorktown Hts NY USA

We develop the mathematical formulation for teaching generative models to a learner whose learning processes and cognitive behaviors may be analytically intractable, but can be simulated by numerical processes. The model considers the learner's bias (prior knowledge) or memory process by using stochastic models. We also present an optimization framework for solving the involved non-convex, stochastic optimization problems associated with machine teaching. The algorithm design and the conditions and analysis are discussed for local convergence properties of the proposed optimization algorithms. In the paper, we discuss a number of example cases to illustrate the algorithmic ideas and demonstrate their efficiency.

关键词： Statistical learning Machine teaching data driven optimization Computer simulation Numerical algorithms

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data-driven Distributionally Robust Hierarchical Coordination for Home Energy Management

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IEEE TRANSACTIONS ON SMART GRID 2021年第5期12卷 4090-4101页

作者： Saberi, Hossein Zhang, Cuo Dong, Zhao Yang Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Home energy management (HEM) is conventionally formulated using central control or local control methods assuming known accurate probability distributions of uncertainties. Without coordination between the central and the local control hierarchies, mutual impacts cannot be properly considered and addressed, which undermine operation efficiency of HEM. Moreover, the probability distributions always have errors, even in emerging data-based methods. To address these unsolved issues, firstly, this paper proposes a new hierarchical coordination method for HEM. On the central hierarchy, a central controller optimizes schedule of non-thermal loads to minimize daily operating cost. On the local hierarchy, automatic local controllers respond to real-time variations of their corresponding thermal zone temperatures to fulfill customer thermal comfort requirements. Secondly, using a finite training dataset, a data-driven distributionally robust optimization (DRO) method is proposed to guarantee solution robustness against worst probability distribution of multiple uncertainties. The hierarchical coordination method is formulated as a DRO model using the Wasserstein metric, which can be solved with high computing efficiency. Numerical simulations validate high solution robustness and overall optimum of the proposed method, fully addressing mutual impacts and uncertainties. Moreover, the proposed method could efficiently save the daily electricity bill by 11%, compared to conventional methods.

关键词： Thermal loading Uncertainty Load modeling HVAC optimization Water heating Temperature control data driven optimization distributionally robust optimization flexible load hierarchical coordination home energy management Wasserstein metric uncertainty

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