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Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water systems in Smart Grids

engineering 2021年第8期7卷 1101-1114页

作者： Junjie Hu Huayanran Zhou Yihong Zhou Haijing Zhang Lars Nordströmd Guangya Yang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University) Beijing 102206China Division of Electric Power and Energy Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholm 10044Sweden Center for Electric Power and Energy Department of Electrical Engineering Technical University of DenmarkKgs Lyngby 2800Denmark

With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency *** flexibility of demand response(DR)resources has become a valuable solution to this ***,existing research indicates that problems on flexibility prediction of DR resources have not been *** study applied the temporal convolution network(TCN)-combined transformer,a deep learning technique to predict the aggregated flexibility of two types of DR resources,that is,electric vehicles(EVs)and domestic hot water system(DHWS).The prediction uses historical power consumption data of these DR resources and DR signals(DSs)to facilitate *** prediction can generate the size and maintenance time of the aggregated *** accuracy of the flexibility prediction results was verified through simulations of case *** simulation results show that under different maintenance times,the size of the flexibility *** proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

关键词： Load flexibility Electric vehicles Domestic hot water system Temporal convolution network-combined transformer Deep learning

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The Role of the Essential Manifold in Data Mining – An Introductory Approach 23rd

The Role of the Essential Manifold in Data Mining – An I...

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23rd International Conference on Computational Science and Its Applications, ICCSA 2023

作者： de Fátima Pina, Maria Andrade, Marina Alexandra ISCTE - IUL University Institute of Lisbon Av. das Forças Armadas Lisbon1649-026 Portugal ISLA - Santarém Superior Institute of Management and Administration of Santarém Rua Teixeira Guedes 31 Santarém2000-029 Portugal ISR - UC Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Rua Silvio Lima - Pólo II Coimbra3030-290 Portugal ISTAR - IUL Information Sciences and Technologies and Architecture Research Center University Institute of Lisbon Av. das Forças Armadas Lisbon1649-026 Portugal

ISBN: (纸本)9783031371288

Interpolating data and the application of data mining techniques in nonlinear manifolds plays a significant role in different areas of knowledge, ranging from computer vision and robotics, to industrial and medical requests, and these growing number of applications have sparked the research interest of the scientific community to these topics. The Generalized Essential manifold, briefly, Essential manifold, consisting of the product of the Grassmann manifold of all k-dimensional subspaces of Rn and the Lie group of rotations in Rn, for instance, plays an important role in the problem of recovering the structure and motion from a sequence of images, also known as stereo matching, which is a crucial problem in image processing and computer vision. A well-known recursive procedure to generate interpolating polynomial curves in Euclidean spaces is the classical De Casteljau algorithm, which is a simple and powerful tool widely used in the field of computer Aided Geometric Design, particularly because it is essentially geometrically based. This algorithm has been generalized to geodesically complete Riemannian manifolds. Thus, having this in mind, in this work we present all the ingredients for a detailed implementation of the generalized De Casteljau algorithm to generate geometric cubic polynomials in the Essential manifold preparing the ground to solve different real interpolation problems in this manifold. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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MOBILE DEVICE-BASED VIBRATION MEASUREMENT FOR SEA TRIAL OF SHIP 29

MOBILE DEVICE-BASED VIBRATION MEASUREMENT FOR SEA TRIAL OF S...

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29th International Congress on Sound and Vibration, ICSV 2023

作者： Eom, Sung-Min Shin, Yun-Ho Na, Woong-Jae Sun, Kyung-Ho Park, Jeong Hee Jo, Hye Young Nam, Daeho Oh, Hwan-Youp Dept. of Naval Architecture and Ocean Engineering Pusan National University Busan Korea Republic of Global Core Research Center for Ships and Offshore Plants Pusan National University Busan Korea Republic of Department of System Dynamics Korea Institute of Machinery and Materials Daejeon Korea Republic of Hull Design Department Hyundai Samho Heavy Industries Mokpo Korea Republic of Innovative Machinery Systems Research Department Advanced Research Center Korea Shipbuilding and Offshore Seoul Korea Republic of

ISBN: (纸本)9788011034238

Measuring vibrations on ships for sea trials can be a challenging task in certain conditions, such as high altitude areas, confined spaces, and remote locations from shipboard equipment or structure. As a result, field engineers and ship trial teams are continuously seeking more convenient, simple, yet reliable measurement systems. To address this issue and provide a better measurement environment, a mobile device-based vibration measurement system for ships has been proposed. It includes a vibration extraction algorithm and signal processing techniques by using a video of the target. Among various image processing techniques, the phase-based template matching method was selected and applied due to its advantages for vibration measurement. The measurement procedure by the mobile device was also suggested, from recording the video to performing post-signal processing. The proposed system was realized through Android operating system-based software and its effectiveness was examined through actual vibration measurements. © 2023 Proceedings of the International Congress on Sound and Vibration. All rights reserved.

关键词： Template matching

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Data-Driven Learning and Control with Event-Triggered Measurements

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IEEE Transactions on Automatic Control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Implementing Green Infrastructure for Urban Stormwater Management: A systematic Review to Identify Future Challenges for Japan

SSRN

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SSRN 2023年

作者： Uchiyama, Shun Bhattacharya, Yasmin Nakamura, Hitoshi Course of System Engineering and Science Graduate School of Engineering and Science Shibaura Institute of Technology Fukasaku 307 Minuma-ku Saitama-shi Saitama-ken 330-0073 Japan Department of Planning Architecture and Environmental Systems Shibaura Institute of Technology Fukasaku 307 Minuma-ku Saitama-shi Saitama-ken 330-0073 Japan

In recent years, Green Infrastructure (GI) has been widely advocated as a measure against urban flooding due to urbanization and climate change. Decentralized small-scale GI in urban areas has been widely promoted as a measure for urban stormwater management for more than 20 years, mainly in Europe and the United States, and examples of its practice have gradually emerged in recent years. However, it is still a new concept in Japan and is pending wide-scale implementation. The purpose of this study is to identify the challenges and suggest a process of GI implementation for the purpose of urban stormwater management in Japan. This study consists of two phases: First, key factors and a basic process for GI implementation are identified based on the experiences of some countries (i.e., China, Australia, the United Kingdom, and the United States). Second, by comparing them with the current situation in Japan, we identified the challenges for GI implementation in Japan in the future. As a result, eight key factors, including "quantitative performance analysis", "land use policy/law", and "interdisciplinary approach/ watershed-scale stakeholder collaboration", were identified. Moreover, we propose a basic process for GI implementation with an interdisciplinary approach, which starts with "selection of GI implementation areas", followed by "determination of implementation strategy with technical and financial support to stakeholders", and "long-term operation". The most important part of the process is how to incorporate GI into the existing urban development strategy, taking into account the topographical and climatic characteristics, social challenges, urban planning systems, and existing stormwater management plans. © 2023, The Authors. All rights reserved.

关键词： Climate change

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An Efficient Implementation for Bayesian Manifold Regularization Method

An Efficient Implementation for Bayesian Manifold Regulariza...

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IEEE Conference on Decision and Control

作者： Junpeng Zhang Yue Ju Bo Wahlberg Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Instituite of Technology Stockholm Sweden Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China

When applying the Bayesian manifold regularization method to function estimation problem with manifold constraints, the direct implementation has computational complexity $\mathcal{O}(N^{3})$ , where $N$ is the number of input-output data measurements. This becomes particularly costly when $N$ is large. In this paper, we propose a more efficient implementation based on the Kalman filter and smoother using a state-space model realization of the underlying Gaussian process. Moreover, we explore the sequentially semi-separable structure of the Laplacian matrix and the posterior covariance matrix. Our proposed implementation has computational complexity $\mathcal{O}(N)$ and thus can be applied to large data problems. We exemplify the effectiveness of our proposed implementation through numerical simulations.

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Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research

arXiv

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arXiv 2024年

作者： Hille, Tobias Stubbemann, Maximilian Hanika, Tom Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany Institute of Computer Science University of Hildesheim Hildesheim Germany

Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which verifies the reliability of research findings using the same code and data. This promotes open and accessible research, robust experimental workflows, and the rapid integration of new findings. Evaluating the degree to which research publications support these different aspects of reproducibility is one goal of the present work. For this we introduce an ontology of reproducibility in machine learning and apply it to methods for graph neural networks. Building on these efforts we turn towards another critical challenge in machine learning, namely the curse of dimensionality, which poses challenges in data collection, representation, and analysis, making it harder to find representative data and impeding the training and inference processes. Using the closely linked concept of geometric intrinsic dimension we investigate to which extend the used machine learning models are influenced by the intrinsic dimension of the data sets they are trained *** Codes 68T01 68T07 68T09 51F99 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

Safety Guaranteed Power-Delivered-to-Load Maximization for M...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods system performance Inductive charging Prototypes Wireless power transfer Parallel processing

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Multi-microgrid Energy Management systems: architecture, Communication, and Scheduling Strategies

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Journal of Modern Power systems and Clean Energy 2021年第3期9卷 463-476页

作者： Bin Zhou Jianting Zou Chi Yung Chung Huaizhi Wang Nian Liu Nikolai Voropai Daosen Xu the College of Electrical and Information Engineering Hunan UniversityChangsha 410082China the Department of Electrical and Computer Engineering University of SaskatchewanSaskatoonSK S7N5A9Canada the College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China the Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences Irkutsk 664033Russia

The increasing penetration of various distributed and renewable energy resources at the consumption premises,along with the advanced metering,control and communication technologies,promotes a transition on the structure of traditional distribution systems towards cyber-physical multi-microgrids(MMGs).The networked MMG system is an interconnected cluster of distributed generators,energy storage as well as controllable loads in a distribution *** its operation complexity can be decomposed to decrease the burdens of communi-cation and control with a decentralized ***,the multi-microgrid energy management system(MIVIGEIV1S)plays a significant role in improving energy efficiency,power quality and reliability of distribution systems,especially in enhancing system resiliency during contingencies.A comprehensive overview on typical functionalities and architectures of MMGEMS is ***,the emerging communication technologies for information monitoring and interaction among MMG clusters are ***,various energy scheduling and control strategies of MMGs for interactive energy trading,multi-energy management,and resilient operations are thoroughly analyzed and ***,some challenges with great importance in the future research are presented.

关键词： Energy management system(EMS) microgrid communication renewable energy multi-energy system

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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