Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting

Laurinec, Peter; Lucká, Mária

doi:10.1007/s10618-018-0598-2

Cited by 25 publications

(19 citation statements)

References 42 publications

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“…Laurinec and Lucká [6,7] presented an interpretable approach for the grouping of multiple data streams in a smart grid, used to improve the forecasting accuracy of aggregated electricity consumption and grid analysis, called ClipStream. In their approach, consumer time-series streams are compressed and represented by interpretable features separated from clipped representation (online phase).…”

Section: Of 25mentioning

confidence: 99%

Simulation Study on the Electricity Data Streams Time Series Clustering

et al. 2020

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Currently, thanks to the rapid development of wireless sensor networks and network traffic monitoring, the data stream is gradually becoming one of the most popular data generating processes. The data stream is different from traditional static data. Cluster analysis is an important technology for data mining, which is why many researchers pay attention to grouping streaming data. In the literature, there are many data stream clustering techniques, unfortunately, very few of them try to solve the problem of clustering data streams coming from multiple sources. In this article, we present an algorithm with a tree structure for grouping data streams (in the form of a time series) that have similar properties and behaviors. We have evaluated our algorithm over real multivariate data streams generated by smart meter sensors—the Irish Commission for Energy Regulation data set. There were several measures used to analyze the various characteristics of a tree-like clustering structure (computer science perspective) and also measures that are important from a business standpoint. The proposed method was able to cluster the flows of data and has identified the customers with similar behavior during the analyzed period.

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Section: Of 25mentioning

confidence: 99%

Simulation Study on the Electricity Data Streams Time Series Clustering

et al. 2020

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“…A more detailed analysis of the literature on grouping of multiple data streams (or time series stream), which is the subject of this article, is desired. For example, the recent methods are constructed in a way to ensure the division of streams over time [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. All of them monitor the proximity of data streams using a record flow and introduce some strategies to obtain partitioning of streams into a set of clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In [ 32 ] authors have developed a powerful online version of the fuzzy C-means algorithm (FCM-DS), allowing to quickly calculate the approximate distance between the streams, thanks to the scalable online transformation of the original data. In [ 35 ] authors have presented an algorithm called ClipStream where time-series are compressed and represented by interpretable features separated from clipped representation. Next, based on such data transformation the K-medoids method with the Partition Around Medoids (PAM) algorithm cluster the data streams.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The ClipStream algorithm is composed of two main phases [ 35 ], i.e., online data abstraction (representation) and an offline clustering. The first data representation phase includes a fast and incremental method of calculating feature vector from each

block named FeaClip and automatic detection of outliers.…”

Section: Time Series Data Streams Clustering Algorithmsmentioning

confidence: 99%

“…According to the authors [ 35 ] the representation phase has the linear time complexity

with respect to the length of the time window. Outlier detection phase is linear

.…”

Section: Time Series Data Streams Clustering Algorithmsmentioning

confidence: 99%

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

Gajowniczek

Bator

Ząbkowski

2020

Entropy

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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.

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Deep Incremental Learning for Big Data Stream Analytics

Alex

Nayahi²

2019

Lecture Notes on Data Engineering and Communications Technologies

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Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting

Cited by 25 publications

References 42 publications

Simulation Study on the Electricity Data Streams Time Series Clustering

Simulation Study on the Electricity Data Streams Time Series Clustering

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

Deep Incremental Learning for Big Data Stream Analytics

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