Simulation Study on the Electricity Data Streams Time Series Clustering

Gajowniczek, Krzysztof; Bator, Marcin; Ząbkowski, Tomasz; Orłowski, Arkadiusz; Loo, Chu Kiong

doi:10.3390/en13040924

Cited by 2 publications

(8 citation statements)

References 34 publications

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“…As previously, the algorithm can gradually expand or reduce the tree according to changes in the stream that change the node variance. Finally, in [ 45 ] authors have presented an extended version of the TS-Stream algorithm, that overcomes some base algorithm drawbacks. After those modifications the final tree structure reaches its full size immediately and it can have leaves with the number of time series above a certain threshold (otherwise the tree would be very complex and deep).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The algorithm presented by [ 45 ] is an extended (improved) version of the algorithm presented in [ 37 ]. In general, it evokes a model with a structure similar to the decision tree, but built in an unsupervised manner.…”

Section: Time Series Data Streams Clustering Algorithmsmentioning

confidence: 99%

“…The overall time complexity is

. It is important to note that the quadratic term in the algorithm refers to the number of time series, which is typically low (order of tenths) [ 45 ].…”

Section: Time Series Data Streams Clustering Algorithmsmentioning

confidence: 99%

“…The second algorithm, which is ClipStream, was entirely implemented using ClipStream library which is a software strictly connected to the article [ 55 ]. Finally, the extended TS-Stream algorithm was implemented in line with the following work [ 45 ].…”

Section: Research Framework and Settingsmentioning

confidence: 99%

“…To do so we reformulated standard ARI measure as follows:

where

denotes the number of objects that are in both, cluster

form

-th time window and cluster

from the

time window (

is simply the same cluster as

but from consecutive window), with the marginal distributions denoted as

and

. After comparing each batch to each other an upper triangle matrix is created [ 45 ] (for an example please see Table 4).…”

Section: Research Framework and Settingsmentioning

confidence: 99%

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

Gajowniczek

Bator

Ząbkowski

2020

Entropy

Self Cite

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Time Series Data Streams Clustering Algorithmsmentioning

confidence: 99%

“…The overall time complexity is

. It is important to note that the quadratic term in the algorithm refers to the number of time series, which is typically low (order of tenths) [ 45 ].…”

Section: Time Series Data Streams Clustering Algorithmsmentioning

confidence: 99%

Section: Research Framework and Settingsmentioning

confidence: 99%

“…To do so we reformulated standard ARI measure as follows:

where

denotes the number of objects that are in both, cluster

form

-th time window and cluster

from the

time window (

is simply the same cluster as

but from consecutive window), with the marginal distributions denoted as

and

. After comparing each batch to each other an upper triangle matrix is created [ 45 ] (for an example please see Table 4).…”

Section: Research Framework and Settingsmentioning

confidence: 99%

See 3 more Smart Citations

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

Gajowniczek

Bator

Ząbkowski

2020

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

MultiStream EvolveCluster

Nordahl

Boeva

Grahn

2023

Proceedings of the Canadian Conference on Artificial Intelligence

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This paper proposes a novel multi-stream clustering algorithm, MultiStream EvolveCluster (MS-EC), that can be used for continuous and distributed monitoring and analysis of evolving time series phenomena. It can maintain evolving clustering solutions separately for each stream/view and consensus clustering solutions reflecting evolving interrelations among the streams. Each stream behavior can be analyzed by different clustering techniques using a distance measure and data granularity that is specially selected for it. The properties of the MultiStream EvolveCluster algorithm are studied and evaluated with respect to different consensus clustering techniques, distance measures, and cluster evaluation measures in synthetic and real-world smart building datasets. Our evaluation results show a stable algorithm performance in synthetic data scenarios. In the case of real-world data, the algorithm behavior demonstrates sensitivity to the individual streams' data quality and the used consensus clustering technique.

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Simulation Study on the Electricity Data Streams Time Series Clustering

Cited by 2 publications

References 34 publications

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

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

MultiStream EvolveCluster

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