e2-Diagnoser: A System for Monitoring, Forecasting and Diagnosing Energy Usage
Data Mining Workshop（2014）
摘要
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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关键词
building management systems,computerised monitoring,data integration,data mining,energy consumption,energy management systems,intelligent sensors,learning (artificial intelligence),power engineering computing,semantic Web,statistical analysis,user interfaces,GAM,abnormal energy consumption diagnosis,building energy usage prediction,data integration,e2-Diagnoser,energy usage diagnosis,energy usage forecasting,energy usage monitoring,generalized additive model,interactive user interface,real-time data mining system,semantic Web technologies,smart sensor-equipped building energy management,smart-grid networks,statistical learning algorithm,stream processing platform,Building Benchmark,Energy Prediction,Pattern Extraction,Smart Buildings
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