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Titlebook: Data Analytics for Renewable Energy Integration. Technologies, Systems and Society; 6th ECML PKDD Worksh Wei Lee Woon,Zeyar Aung,Stuart Mad

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发表于 2025-3-21 20:00:07 | 显示全部楼层 |阅读模式
书目名称Data Analytics for Renewable Energy Integration. Technologies, Systems and Society
副标题6th ECML PKDD Worksh
编辑Wei Lee Woon,Zeyar Aung,Stuart Madnick
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Data Analytics for Renewable Energy Integration. Technologies, Systems and Society; 6th ECML PKDD Worksh Wei Lee Woon,Zeyar Aung,Stuart Mad
描述.This book constitutes the revised selected papers from the 6th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2018, held in Dublin, Ireland, in September 2018...The 9 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response, and many others..
出版日期Conference proceedings 2018
关键词artificial intelligence; computer networks; data mining; energy efficiency; energy resources; estimation;
版次1
doihttps://doi.org/10.1007/978-3-030-04303-2
isbn_softcover978-3-030-04302-5
isbn_ebook978-3-030-04303-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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https://doi.org/10.1007/978-3-031-45948-1eraging mathematical optimization. Based on electricity consumption and location of the household, the algorithm finds PV module design parameters using Covariance Matrix Adaptation Evolution Strategy (hereafter CMA-ES). According to these computed design parameters, the algorithm finds the most sim
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Social Work and Advanced Marginalitys and opportunities in power flow optimization. This promises reduced power generation costs through better integration of renewable energy generators into the Smart Grid. Unfortunately, renewable generators are fundamentally variable and uncertain. This uncertainty motivates our study of probabilis
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https://doi.org/10.1007/978-3-030-16222-1of time. With our method, we demonstrate a process of utilizing large-scale satellite images to classify a wave height with a continuous regressive output using a corresponding input for close shore sea. We generated and trained a convolutional neural network model that achieved an average loss of 0
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Class, Individualization and Late Modernityorld’s primary energy consumption. Building Performance Simulation (BPS) can be used to model the relationship between building characteristics and energy consumption and to facilitate optimization efforts. However, BPS is computationally intensive and only a limited set of building configurations c
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Class, Surplus, and the Division of Labour In order to help achieve this balance in the grid, the renewable energy resources such as wind and stream-flow should be forecast at high accuracies on the generation side, and similarly, electricity consumption should be forecast using a high-performance system. In this paper, we deal with short-t
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