ANNO: A Time Series Annotation Tool to Evaluate Event Detection Algorithms

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1199)


The research field of energy analytics is concerned with the collection and processing of data related to electrical power generation and consumption. Electricity consumption data can reveal information pertaining to the nature of underlying appliances, their mode of operation, and many other aspects. Sudden load changes, so-called events, constitute the principal source of information in such time series data, thus their reliable detection and interpretation is a prerequisite for accurate energy analytics. The development of event detection algorithms is, however, hampered due to the unavailability of comprehensive data sets that feature energy consumption time series with corresponding event annotations. We hence present ANNO, a tool to provide annotations to time series consumption data in a supervised fashion and use them for the development of energy analytics algorithms, in this work.


Load signature analysis Supervised data set annotation 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of InformaticsTechnische Universität ClausthalClausthal-ZellerfeldGermany

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