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.
Highlights
The advances in smart metering solutions have enabled that gathering information about customer power consumption in real time is feasible and it can be successfully used for data exploration to bring actionable recommendations
The literature provides some methods of the data streams clustering, majority of them are not appropriate for the whole time series data streams clustering
Even though electricity consumer objectives are usually based on monetary benefits, electricity providers benefit from the knowledge of consumer’ profiles, to create individualized means aimed at consumers with compatible use profiles and socio-economic behavior
Summary
The advances in smart metering solutions have enabled that gathering information about customer power consumption in real time is feasible and it can be successfully used for data exploration to bring actionable recommendations. The analysis requires a lot of effort to discover the segmentation of entities based on their electricity consumption data, the benefits, as the result of the data insights, would be very appealing to the electricity providers [1]. By supplying providers with demand response predictions on aggregated level, due to segmentation (other terms such as clustering and grouping are used interchangeably), and revealing the real economic structure of the entities (e.g., individual users, households, small business) the goal is to fit into the integrated planning system, where the appropriate real-time actions could be proposed to meet the system demands effectively [2]. The changes in smart meter streaming data may be the result of many factors, including those related to weather conditions, to week days or those related to price incentives [5]
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