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

• Main Authors: Krzysztof Gajowniczek, Marcin Bator, Tomasz Ząbkowski
• Format: Article
• Language: English
• Published: MDPI AG 2020-12-01
• Series: Entropy
• Subjects: clustering; data stream; machine learning; smart metering; time series
• Online Access: https://www.mdpi.com/1099-4300/22/12/1414

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.