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Smart Meter Data Analytics: Systems, Algorithms, and Benchmarking

机译:智能电表数据分析:系统,算法和基准测试

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Smart electricity meters have been replacing conventional meters worldwide, enabling automated collection of fine-grained (e.g.,every 15 minutes or hourly) consumption data. A variety of smart meter analytics algorithms and applications have been proposed, mainly in the smart grid literature. However, the focus has been on what can be done with the data rather than how to do it efficiently. In this article, we examine smart meter analytics from a software performance perspective. First, we design a performance benchmark that includes common smart meter analytics tasks. These include offline feature extraction and model building as well as a framework for online anomaly detection that we propose. Second, since obtaining real smart meter data is difficult due to privacy issues, we present an algorithm for generating large realistic datasets from a small seed of real data. Third, we implement the proposed benchmark using five representative platforms: a traditional numeric computing platform (Matlab), a relational DBMS with a built-in machine learning toolkit (PostgreSQL/MADlib), a main-memory column store ('' System C ''), and two distributed data processing platforms (Hive and Spark/Spark Streaming). We compare the five platforms in terms of application development effort and performance on a multicore machine as well as a cluster of 16 commodity servers.
机译:智能电表已在全球范围内取代了常规电表,从而能够自动收集细粒度(例如,每15分钟或每小时)的能耗数据。主要在智能电网文献中,已经提出了多种智能电表分析算法和应用。但是,重点一直放在如何处理数据上,而不是如何有效地进行处理上。在本文中,我们将从软件性能的角度检查智能电表分析。首先,我们设计一个性能基准,其中包括常见的智能电表分析任务。其中包括离线特征提取和模型构建,以及我们建议的在线异常检测框架。其次,由于由于隐私问题很难获得真实的智能电表数据,因此我们提出了一种算法,可从一小部分真实数据中生成大型现实数据集。第三,我们使用五个代表性平台实施建议的基准测试:传统数值计算平台(Matlab),带有内置机器学习工具包的关系DBMS(PostgreSQL / MADlib),主内存列存储(“ System C' ')和两个分布式数据处理平台(Hive和Spark / Spark Streaming)。我们在多核计算机以及由16个商用服务器组成的集群中,就应用程序开发工作量和性能方面对五个平台进行了比较。

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