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共 17266 条记录，以下是491-500 订阅

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LSTM-based Distributed Photovoltaic Output Interval Prediction Method for Source-load Collaboration 8

LSTM-based Distributed Photovoltaic Output Interval Predicti...

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Li, Bo Shi, Zhan Wu, Zhentian Yang, Zhihua Guangdong Power Grid Co. Ltd. Power Dispatching Control Center Communication Management Department Guangdong China Guangdong Electric Power Communication Technology Co. Ltd. Science Innovation Department Guangdong China

ISBN: (纸本)9798350302455

With the continuous development of distributed energy, photovoltaic (PV) power emerges as a significant construction to tradeoff the balance of power supply and demand. Since the prediction of PV output is unavoidable, interval prediction becomes a powerful method to describe the output curve accurately. To this end, this paper proposes a long short term memory (LSTM)-based distributed PV output interval prediction method for source-load collaboration. It can correct the prediction error according to the spatiotemporal characteristic of PV power stations, and then calculates the interval prediction results under different confidence levels. Simulation results show that the proposed algorithm has the superior performance in the average width of the forecast interval, and the coverage width. © 2023 ieee.

关键词： Long short-term memory

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Feature as a Service (FaaS): Customized Service in Cloud computing 1

Feature as a Service (FaaS): Customized Service in Cloud Com...

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1st ieee international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Aditi Kaushik, Deepti Maurya, Santosh Kumar Malik, Suraj Kumar, Gaurav Asim, Mohammad IIMT University Computer Science and Engineering Department Uttar Pradesh Meerut India Sharda University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350356816

The world is changing due to technological changes. Like the business approach, there is a need to maintain tremendous pressure for reliable service architecture. Features of trust may include cost-effectiveness, reliability for services, pay per use, etc. Cloud services are very much appreciated as it is giving tremendous market to the business audience. Inspired by this, processing and storage capacity is also improving. By proposing a new service FaaS (Feature as a Service), more measuring factors can be obtained to achieve greater growth in technology and will also benefit the business audience. The offered service is directly related to both the things i.e. server and user related, in terms of system optimization and user satisfaction. The measuring function is also proposed in the paper. Ultimately the new customized service model will yield benefits. As the world has now become digital, the system efficiency should be further increased by designing these types of services which can cover the entire cloud system in all aspects. © 2024 ieee.

关键词： Cloud computing architecture

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An SGX and Blockchain-Based System for Federated Learning in Classifying COVID-19 Images 21

An SGX and Blockchain-Based System for Federated Learning in...

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21st ieee international Symposium on Parallel and Distributed Processing with Applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Qin, Sihang Gu, Xianjun Jun, Xing Li, Haitao Huang, Mengqi Dai, Weiqi Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China State Grid Wuhan Power Supply Company China

ISBN: (纸本)9798350329223

Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inability of model-generating nodes to ensure computational fairness. The second challenge involves the potential leakage of private information through gradient differences. This paper introduces a federated learning system that leverages Software Guard extensions (SGX) and blockchain technology to enhance the fairness and security of federated learning for analyzing chest imaging data. The modeling of imaging factors for chest radiography data can effectively regulate the data quality of semi-trusted nodes. A blockchain smart contract is employed for data quality-aware proof, and the quality-aware block undergoes a two-stage rewriting process across distinct nodes. A notary membership model achieves notarized cluster consensus for imaging informatics data. The multi-channel layer model quantifies the distribution of computational workload across participating nodes within the medical consortium. Upon successful verification of the enclave command in the aggregation cycle, traceable and packaged signatures are generated to support computational fairness. To ensure robust information security, SGX remote authentication is used between downstream and upstream nodes. The federated learning system, built upon SGX and blockchain, has been implemented using the Fabric federated chain platform. The ResNet model underwent training through cross-sectional federated learning to effectively classify novel coronavirus lung medical image data. Following this, multiple experimental control and security analyses were conducted, revealing that the time loss incurred due to crossing channels is approximately 21.31%, while the time overhead of SGX remains within 5.2%, as allowed by the number of nodes. © 2023 ieee.

关键词： Blockchain

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Toward Scientific Workflows in a Serverless World 18

Toward Scientific Workflows in a Serverless World

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ieee 18th international conference on e-science (e-science)

作者： Khochare, Aakash Simmhan, Yogesh Mehta, Sameep Agarwal, Arvind Indian Inst Sci Bangalore 560012 Karnataka India IBM India Res Lab Bangalore Karnataka India

ISBN: (数字)9781665461245

ISBN: (纸本)9781665461245

Serverless computing and FaaS have gained popularity due to their ease of design, deployment, scaling and billing on clouds. However, when used to compose and orchestrate scientific workflows, they pose limitations due to cold starts, message indirection, vendor lock-in and lack of provenance support. Here, we propose a design for a Serverless Scientific Workflow Orchestrator that overcomes these challenges using techniques like function fusion, pilot invocations and data fabrics.

关键词： Serverless computing Fabrics

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evolving Large Scale HPC Monitoring & Analysis to Track Modern Dynamic environments

Evolving Large Scale HPC Monitoring & Analysis to Track Mode...

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2024 international conference on Cluster computing

作者： Shoga, Kathleen Brandt, Jim Schwaller, Benjamin Tucker, Thomas Lawrence Livermore Natl Lab Livermore CA 94550 USA Sandia Natl Labs Albuquerque NM USA Open Grid Comp Austin TX USA

ISBN: (纸本)9798350383461;9798350383454

High Performance computing (HPC) architectures are evolving at an ever-accelerating pace in order to meet the growing diversity of demands placed on them by changing workloads (e.g., high-fidelity multi-physics simulations and Artificial Intelligence (AI)). As network, compute, and memory technologies expand to accomplish this, power densities and energy needs have also increased. Our goal is to efficiently manage the disparate workflows and workloads required to meet mission needs across our shared HPC resources while remaining within shifting power, energy, and cooling envelopes. To accomplish this we require holistic insight into how workflow components utilize our HPC resources and their time-varying impact on power, energy, and cooling. Approaches to this vary across the U.S. Department of energy (DOe) laboratories. At Lawrence Livermore National Laboratory (LLNL) and Sandia National Laboratories (SNL) we have independently taken similar approaches to creating HPC monitoring and analysis systems. LLNL created "Sonar" and SNL created "AppSysFusion", both of which serve as extensible (i.e., pluggable frameworks that enable new functionalities to be easily deployed) HPC monitoring analysis and visualization frameworks. This paper describes the capabilities, pluggable component architecture, and design choices, both historic and current, for the components and the tooling that enables collaborative development across institutions.

关键词： high performance computing monitoring and operational data analytics

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A Defect Identification Method for Synchronous Condenser Based on Gramian Angular Fields enhancement and Optimization Graph Convolutional Neural Network 6

A Defect Identification Method for Synchronous Condenser Bas...

引用

6th ieee international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Li, Wenbo Zhang, Shuai Yang, Yong Jin, Yongtao Zhao, Lin Lu, Hongkun Wei, Bo E. Energy Technology Co. Ltd. Electric Power Research Institute State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China E. Energy Technology Co. Ltd. Hangzhou China Electric Power Research Institute State Grid Zhejiang Electric Power Co. Ltd. Zhejiang Hangzhou China

ISBN: (纸本)9798350374315

To achieve intelligent and reliable recognition of the operating conditions of synchronous condenser equipment, a typical defect recognition method of synchronous condenser based on gramian angular fields enhancement and optimization graph convolutional neural network is proposed. Firstly, the one-dimensional voiceprint signal of the camera is mapped into a two-dimensional image by using the K-field method, and then the original data sets under different working conditions are constructed. Then, the Gram angular field map under different working conditions is divided into several superpixel blocks by using super pixel segmentation method, which are used as nodes and graph structure data are constructed according to color and texture characteristics. Finally, the constructed graph structure is input into the graph convolution neural network model with attention mechanism for training and learning, so as to realize accurate recognition of the state of synchronous condenser equipment under multi-typical working conditions. A simulation test system including typical defects of the synchronous condenser equipment under normal, unbalanced and low oil pressure conditions is established. The test results show the proposed method can effectively characterize the three typical working states of the synchronous condenser, and the defect recognition accuracy is significantly improved compared with the conventional method, and it has good stability. The recognition rate can still be more than 93 % in a noise environment of 5 dB, which is about 5.3% higher than that of the conventional method. © 2024 ieee.

关键词： Convolutional neural networks

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An open science exploration of global 1-km simulations of the earth's atmosphere 18

An open science exploration of global 1-km simulations of th...

引用

ieee 18th international conference on e-science (e-science)

作者： Anantharaj, Valentine Hatfield, Samuel Polichtchouk, Inna Wedi, Nils O'Neill, Morgan e. Papatheodore, Thomas Dueben, Peter Oak Ridge Natl Lab Oak Ridge TN 37830 USA European Ctr Medium Range Weather Forecasts Reading Berks England Stanford Univ Stanford CA 94305 USA

We are making available a multi-petabyte data set for open science via a data hackathon. The participants bring their science to the computing and data facility, alleviating the need to transfer and manage the large v... 详细信息

ISBN: (数字)9781665461245

ISBN: (纸本)9781665461245

关键词： open science big data hackathon climate computing

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Research on High throughput Consensus Algorithm of energy Blockchain based on Recurrent Neural Network Node Integrity Prediction 8

Research on High throughput Consensus Algorithm of Energy Bl...

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Sun, Tao Li, Da Wang, Dong State Grid Digital Technology Holding Co. Ltd Beijing China State Grid Digital Technology Holding Co. Ltd Blockchain Technology Laboratory of State Grid Corporation of China Beijing China

ISBN: (纸本)9798350302455

With the continuous improvement of the digitalization level in the field of energy and electric power, the combination of blockchain technology and business in the field of energy and electric power is getting deeper and deeper. This paper designs a blockchain consensus algorithm combined with artificial intelligence, aiming at the efficiency, security and other problems existing in the existing blockchain consensus algorithm. First, on the basis of ensuring the reliability of the blockchain system, combined with the integrity performance of the consensus node of the blockchain, combined with the recurrent neural network(RNN) to predict the credibility value of the node, screened out the reliable consensus node to participate in the consensus of the energy blockchain, and punished the malicious node. This algorithm can effectively enhance the consensus efficiency of the energy blockchain and improve the level of data privacy protection. We will further support the construction of new power systems. © 2023 ieee.

关键词： Blockchain

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A cyber-physical power system testbed for PMU based grid monitoring application

A cyber-physical power system testbed for PMU based grid mon...

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2023 ieee international conference on Power electronics, Smart grid, and Renewable energy, PeSGRe 2023

作者： Gayathri, K. Jena, Manas Kumar Indian Institute of Technology Palakkad Department of Electrical Engineering Palakkad India

ISBN: (纸本)9798350310573

Wide area power system monitoring is a developing technology that uses advanced communication networks, and data analytics to improve grid monitoring and increase overall reliability and efficiency. This paper presents a cost-effective grid monitoring testbed (GMT), a hardware testbed for wide-area power system monitoring applications. The phasor data for testing is generated using the emulated phasor measurement units (ePMU) inside the DSA Tools software. ieee 9 bus system has been used as the test system to demonstrate the effectiveness of the suggested strategy. The data from the phasor measurement unit (PMU) is sent to the SeL-3350 real time automation controller (RTAC) using the ieee-C37.118 protocol. The algorithm under test is implemented within the RTAC controller. The controller output is then streamed to the SeL-synchrowave real-time power system monitoring software. On the hardware configuration, the algorithm for the center of inertia (COI) frequency reference, inertia distribution index (IDI), and angle difference with the COI angle as the reference is implemented. © 2023 ieee.

关键词： Center of inertia inertia distribution index power system monitoring real-time automation controller reference angle

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Giant Trevally Optimization for enhanced Vehicle-to-grid and grid-to-Vehicle Operations in eV Charging Stations 3

Giant Trevally Optimization for Enhanced Vehicle-to-Grid and...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Prasad, e. Shiva Gosavi, Vinodpuri Rampuri Venkatesan, V. Asma, M.D. Kavitha, P. Prasad, P. Venkata Department of Electrical and Electronics Engineering VNR VJIET Telangana Hyderabad500090 India Maharashtra Nashik422213 India Department of Automobile Engineering GKM College of Engineering and Technology Tamil Nadu Chennai600063 India Department of Computer Science and Engineering CMR Technical Campus KandlaKoya Medchal Telangana Hyderabad501401 India Department of Electrical and Electronics Engineering R.M.K. Engineering College Tamil Nadu Kavaraipettai601206 India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9798331532420

Implementation of the Vehicle-to-grid (V2G) and grid-to-Vehicle (G2V) capabilities in electric vehicle charging stations (eVCS) is crucial for supporting grid balance and improving the energy consumption. However, the conventional techniques of optimizing this problem are unlikely to accurately transfer energy to between the grid and eVs, causing poor load regulation and high expenses in operation. In efforts to solve these challenges, this paper introduces the Giant Trevally Optimization (GTO) algorithm that ushers in efficiency in the V2G and G2V processes of the eV charging stations. Thus, the fundamental goal of the proposed method is to minimize charging time, strengthen the power stability and optimize the efficiency of the operation in the eV charging processes. The GTO algorithm is applied to control energy exchange in V2G and G2V transactions seeking to achieve efficient charging /discharging timetable in a manner that can prevent high costs as well as instabilities in the electric grid. By then, the proposed GTO approach in implemented on MATLAB platform and evaluate their performance with various existing methods include Whale Optimization Algorithm (WOA), Grey Wolf Optimization Algorithm (GWO), Fuzzy Logic-Based Optimization (FLO) and Teaching-Learning-Based Optimization (TLBO). The Gesturing Trade Off method is proposed in this work and it has an operating cost of $2628.12 conclusively showing that the proposed method is cost effective. Compared to the current optimization techniques, the proposed GTO method yields a settling time of 0.52s, a rising time of 0.25s, and marginal overshoot of only 0.8% meaning that the adaptation of the proposed GTO method can improve the efficiency and response time of adaptive V2G and G2V controllers of eVCS. © 2024 ieee.

关键词： Charging stations

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