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Pattern recognition algorithms for electricity load curve analysis of buildings

机译：建筑物电力负荷曲线分析的模式识别算法

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Buildings consume 40％ of the total primary energy and 30％ of the annual electricity, contributing significantly to greenhouse gas emissions. Naturally, therefore, building energy efficiency and notions like the nearly zero energy buildings are continuously gaining importance and popularity as means to reduce carbon emissions and the strong dependence on fossil fuels. A step towards this direction is the incorporation of smart grid technologies, mainly through the widespread of automatic meter reading and smart meters. This enables automatic collection of in depth information of the customer's behavior along with the building's performance and, thus, introduces new opportunities for energy saving and efficient management. However, the recorded amassing ream of data requires efficient processing and interpretation, so as to provide for meaningful information. In order to tackle this problem, this paper proposes a comprehensive methodology for the investigation of the electricity behavior of buildings, using clustering techniques. Utilizing a university campus as a case study, the proposed methodology is applied to the load curves of different buildings leading to the determination of an optimum clustering procedure. The methodology may be generalized for any type of building.
机译：建筑物消耗了一次能源总量的40％和年度电力的30％，极大地导致了温室气体的排放。因此，自然而然地，建筑节能和诸如近乎零能耗的建筑之类的观念就日益受到重视和普及，作为减少碳排放和强烈依赖化石燃料的手段。朝这个方向迈出的一步是智能电网技术的整合，主要是通过自动抄表和智能电表的广泛普及。这样可以自动收集有关客户行为以及建筑物性能的深入信息，从而为节能和高效管理带来了新的机遇。但是，记录的大量数据需要有效的处理和解释，以便提供有意义的信息。为了解决这个问题，本文提出了一种使用聚类技术研究建筑物电力行为的综合方法。以大学校园为例，将所提出的方法应用于不同建筑物的负荷曲线，从而确定最佳的聚类程序。该方法可以针对任何类型的建筑物进行概括。

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