Feature-Based Clustering for Electricity Use Time Series Data

Autor:	Mikko Kolehmainen, Teemu Räsänen
Rok vydání:	2009
Předmět:	Electric power distribution business.industry Computer science Feature extraction Machine learning computer.software_genre Missing data Load management Data stream clustering Data mining Artificial intelligence Time series business Cluster analysis computer Curse of dimensionality
Zdroj:	Adaptive and Natural Computing Algorithms ISBN: 9783642049200 ICANNGA
DOI:	10.1007/978-3-642-04921-7_41
Popis:	Time series clustering has been shown effective in providing useful information in various applications. This paper presents an efficient computational method for time series clustering and its application focusing creation of more accurate electricity use load curves for small customers. Presented approach was based on extraction of statistical features and their use in feature-based clustering of customer specific hourly measured electricity use data. The feature-based clustering was able to cluster time series using just a set of derive statistical features. The main advantages of this method were; ability to reduce the dimensionality of original time series, it is less sensitive to missing values and it can handle different lengths of time series. The performance of the approach was evaluated using real hourly measured data for 1035 customers during 84 days testing time period. After all, clustering resulted into more accurate load curves for this set of customers than present load curves used earlier. This kind of approach helps energy companies to take advantage of new hourly information for example in electricity distribution network planning, load management, customer service and billing.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2e5202bfc2c026bdf4297d48b81bc85d https://doi.org/10.1007/978-3-642-04921-7_41 Zobrazit plný text záznamu