This paper examines how co-locating multiple VMs on a single physical server with shared storage impacts I/O performance, specifically focusing on latency of I/O operations, and the overall throughput. We introduce a ...
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Precise fault distance estimation is critical for effective transmission system protection, especially in modern power systems with integrated distributed generators (DGs). As the global demand for energy continues to...
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In the era of big data, efficiently processing and retrieving insights from unstructured data presents a critical challenge. This paper introduces a scalable leader-worker distributed data pipeline designed to handle ...
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Enhanced Astacus optimization (EAOA) algorithm is applied for solving the real power loss diminution problem in grid-connected renewable energy system. Astacus optimization algorithm is scientifically defined through ...
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Bringing intelligent decision-making to industrial facilities involves coordinating a complex ecosystem of applications with different performance requirements that often depend on each other. A promising approach to ...
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The complex structure of interplanetary magnetic fields and their variability, due to solar activity, make it necessary to compute the Cosmic Ray (CR) modulation with numerical simulations. COde for a Speedy Monte Car...
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Traditional decision support systems (DSS) show obvious limitations in dealing with increasingly complex and dynamic decision-making scenarios. By integrating graph neural networks (GNNs) and expert systems (ESs), thi...
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With the increasing demand for computility, managing distributed computility resources is crucial for improving service quality and performance of the cross-region computility infrastructure. In order to ensure timely...
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Accurate load forecasting is crucial for the optimized operation and planning of smart power grids. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric veh...
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ISBN:
(纸本)9791188428137
Accurate load forecasting is crucial for the optimized operation and planning of smart power grids. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric vehicles create significant uncertainties and complexities in load patterns. Traditional centralized forecasting models struggle with data privacy concerns, communication overheads, and lack of model adaptiveness. This paper proposes a privacy-preserving federated learning-based framework for short-term load forecasting in smart grids. Local machine learning models are trained on distributed private datasets across different stations of the grid and only the model parameters are communicated to a central server to create an aggregated global model for load forecasting, without exchanging any raw private data. The proposed approach harnesses edge resources efficiently through decentralized on-device training while providing enhanced accuracy and personalization over centralized models. Several experiments conducted on electricity consumption data validate the effectiveness of proposed approach in handling complex spatiotemporal load changes and generating station-specific adaptive forecasts. By adopting a decentralized approach, proposed methodology seeks to enhance grid resilience by preserving data privacy, mitigating security risks, and optimizing the efficiency of smart microgrid operations. The proposed solution can enable optimized capacity planning and retail pricing for sustainable grids of the future. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.
The increasing integration of networked distributed Energy Resources (DERs) into microgrids is expanding the attack surface of modern power systems, particularly in decentralized environments. This paper explores the ...
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