Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

Abstract

Data from smart grids are challenging to analyze due to their very large size, high dimensionality, skewness, sparsity, and number of seasonal fluctuations, including daily and weekly effects. With the data arriving in a sequential form the underlying distribution is subject to changes over the time intervals. Time series data streams have their own specifics in terms of the data processing and data analysis because, usually, it is not possible to process the whole data in memory as the large data volumes are generated fast so the processing and the analysis should be done incrementally using sliding windows. Despite the proposal of many clustering techniques applicable for grouping the observations of a single data stream, only a few of them are focused on splitting the whole data streams into the clusters. In this article we aim to explore individual characteristics of electricity usage and recommend the most suitable tariff to the customer so they can benefit from lower prices. This work investigates various algorithms (and their improvements) what allows us to formulate the clusters, in real time, based on smart meter data.

MultiStream EvolveCluster

“…Multi-stream approaches can broadly be divided into two categories. The first category covers the cases where multiple streams themselves are clustered based on their characteristics [14][15][16][17]. A typical application for these is, e.g., electricity consumption, where user profiles (streams) are clustered together to identify consumer behavior.…”

From Individual Device Usage to Household Energy Consumption Profiling

“…Multiple countries have different costs for the same amount of supplied energy based on the consumer type and the time. In [1], the authors showcase this concept in Poland, which has different costs in different time frames of the same day. Efficient management of electricity consumption is of paramount importance from the perspective of cost reduction.…”

Akan veri kümeleme probleminde ağaç veri yapılarının performans karşılaştırması

“…Akan veri kümeleme yaklaşımları, dinamik bir yapıda sistemle etkileşim halinde olan veri üzerinde oldukça değerli bilgiler sunabilmektedir [6]. Aykırı veri tespiti [7], elektrik tüketim tahmini [8,9], istenmeyen veri tespiti [10], metin kümeleme [11], nesnelerin interneti uygulamaları [12], medikal uygulamalar [13] ve finansal uygulamalar [14] akan veri kümelemenin uygulandığı çeşitli alanlar olarak karşımıza çıkmaktadır.…”