e2-Diagnoser: A System for Monitoring, Forecasting and Diagnosing Energy Usage

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI:10.1109/ICDMW.2014.56

J. Ploennigs, Bei Chen, Paulito Palmes, Raymond Lloyd

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引用次数: 13

Abstract

We propose e2-Diagnoser, a real-time data mining system for the energy management of smart, sensor-equipped buildings. The main features of e2-Diagnoser are: (i) fast extraction of a large portfolio of buildings' benchmarks at multiple places, and (ii) accurate prediction of buildings' energy usage down to sub meter level to detect and diagnose abnormal energy consumptions. Fundamentally, the e2-Diagnoser system is built on a novel statistical learning algorithm using the Generalized Additive Model (GAM) to simultaneously monitor the mean and variation of the energy usage as well as identify the influencing factors such as weather conditions. Its implementation is based on stream processing platform that integrates data from various sources using semantic web technologies and provides an interactive user interface to visualize results. The platform is scalable and can be easily adapted to other applications such as smart-grid networks. Here we describe the architecture, methodology, and show the web-interface to demonstrate the main functions in the e2-Diagnoser.

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e2-Diagnoser:一个监测、预测和诊断能源使用的系统

我们提出e2-Diagnoser，这是一个实时数据挖掘系统，用于智能传感器建筑的能源管理。e2-Diagnoser的主要特点是:(i)快速提取多个地点的大量建筑物基准组合，(ii)准确预测建筑物的能源使用情况，精确到亚米水平，以检测和诊断异常的能源消耗。从根本上说，e2-Diagnoser系统建立在一种新的统计学习算法上，使用广义可加模型(GAM)来同时监测能源使用的平均值和变化，并识别天气条件等影响因素。它的实现基于流处理平台，该平台使用语义web技术集成了来自不同来源的数据，并提供了一个交互式用户界面来可视化结果。该平台是可扩展的，可以很容易地适应其他应用，如智能电网网络。在这里，我们描述了架构、方法，并展示了web界面来演示e2- diagnostic的主要功能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2014 IEEE International Conference on Data Mining Workshop

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