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首页> 外文期刊>Acta crystallographica. Section F, Structural biology communications >Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches
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Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches

机译:使用聚类算法和半监控机器学习方法构建能源建模(BEM)

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Energy efficiency is a critical element of building energy conservation. Energy Information Administration (EIA) and International Electrotechnical Commission (IEC) estimated that over 6% of electrical energy was lost during transmission and distribution. Sensing and tracking technologies, and data-mining offer new windows to better understanding these losses in real-time. Recent developments in energy optimization computational methods also allow engineers to better characterize energy consumption load profiles. The paper focuses on developing new and robust data-mining techniques to explore large and complex data generated by sensing and tracking technologies. These techniques would potentially offer new avenues to understand and prevent energy losses during transmission. The paper presents two new concepts: First, a set of clustering algorithms that model the supply-demand characterization of four different substations clusters, and second, a semi-supervised machine learning and clustering technique are developed to optimize the losses and automate the process of identifying loss factors contributing to the total loss. This three-step process uses real-time data from buildings and the substations that supply electricity to the buildings to develop the proposed technique. The preliminary findings of this paper help the utility service providers to understand the energy supply-demand requirements. (C) 2016 Elsevier B.V. All rights reserved.
机译:能效是建筑节能的关键因素。能源信息管理局(EIA)和国际电工委员会(IEC)估计,在传输和分配过程中损失了超过6%的电能。传感和跟踪技术,以及数据挖掘提供了新的窗口,以更好地了解这些损失。最新的能量优化计算方法的发展还允许工程师更好地表征能量消耗负载轮廓。本文侧重于开发新的和强大的数据挖掘技术,以探索通过传感和跟踪技术生成的大型和复杂数据。这些技术可能会提供新的途径,以了解传输过程中的能量损失。本文提出了两种新概念:第一,开发了一组集群算法,模拟四种不同变电站集群的供需表征,以及第二监督机器学习和聚类技术,以优化损失和自动化过程识别有助于总损失的损失因素。这三步过程使用建筑物的实时数据和为建筑物提供电力的变电站来开发所提出的技术。本文的初步调查结果有助于公用事业服务提供商了解能源供应需求要求。 (c)2016年Elsevier B.v.保留所有权利。

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