Forecasting Energy Demand by Clustering Smart Metering Time Series

被引:2
|
作者
Bock, Christian [1 ,2 ]
机构
[1] Heinrich Heine Univ, Inst Comp Sci, D-40225 Dusseldorf, Germany
[2] BTU EVU Beratung GmbH, D-40545 Dusseldorf, Germany
来源
INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: THEORY AND FOUNDATIONS, IPMU 2018, PT I | 2018年 / 853卷
关键词
Big data; Data mining; Knowledge discovery; Clustering; Time series; Smart metering; Load profiles; LOAD PROFILES; CLASSIFICATION; SEGMENTATION; CONSUMPTION;
D O I
10.1007/978-3-319-91473-2_37
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Current demands on the energy market, such as legal policies towards green energy usage and economic pressure due to growing competition, require energy companies to increase their understanding of consumer behavior and streamline business processes. One way to help achieve these goals is by making use of the increasing availability of smart metering time series. In this paper we extend an approach based on fuzzy clustering using smart meter data to yield load profiles which can be used to forecast the energy demand of customers. In addition, our approach is built with existing business processes in mind. This helps not only to accurately satisfy real world requirements, but also to ease adoption by the industry. We also assess the quality of our approach using real world smart metering datasets.
引用
收藏
页码:431 / 442
页数:12
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