Profiling of Household Residents’ Electricity Consumption Behavior Using Clustering Analysis

Nordahl, Christian; Boeva, Veselka; Grahn, Håkan; Netz, Marie Persson

doi:10.1007/978-3-030-22750-0_78

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…If instead a strict measure was used, such as ED, those similar behaviors would likely not be identified as similar. However, if there was a concern that the behaviors should be performed at the exact time and place each day to be identified as similar, ED would be a better choice of measure Nordahl et al (2019).…”

Section: Dissimilarity Measuresmentioning

confidence: 99%

EvolveCluster: an evolutionary clustering algorithm for streaming data

et al. 2021

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Data has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.

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Section: Dissimilarity Measuresmentioning

confidence: 99%

EvolveCluster: an evolutionary clustering algorithm for streaming data

et al. 2021

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“…The system is supposed to build and maintain an electricity consumption behavior model for each monitored household. Initially, a model of normal electricity consumption behavior is created for each particular household by using historical data [12]. In order to monitor such a model over time it is necessary to build a new model on each new portion of electricity consumption data and then compare the current model with the new household electricity consumption behavior model.…”

Section: Case Descriptionmentioning

confidence: 99%

Modeling Evolving User Behavior via Sequential Clustering

Boeva

Nordahl

2020

Machine Learning and Knowledge Discovery in Databases

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In this paper we address the problem of modeling the evolution of clusters over time by applying sequential clustering. We propose a sequential partitioning algorithm that can be applied for grouping distinct snapshots of streaming data so that a clustering model is built on each data snapshot. The algorithm is initialized by a clustering solution built on available historical data. Then a new clustering solution is generated on each data snapshot by applying a partitioning algorithm seeded with the centroids of the clustering model obtained at the previous time interval. At each step the algorithm also conducts model adapting operations in order to reflect the evolution in the clustering structure. In that way, it enables to deal with both incremental and dynamic aspects of modeling evolving behavior problems. In addition, the proposed approach is able to trace back evolution through the detection of clusters' transitions, such as splits and merges. We have illustrated and initially evaluated our ideas on household electricity consumption data. The results have shown that the proposed sequential clustering algorithm is robust to modeling evolving behavior by being enable to mine changes and update the model, respectively.

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“…Furthermore, Refs. [ 33 , 34 ] applied an improved k-means algorithm with particle swarm optimization (PSO) to open residential buildings datasets to divide their electricity consumption in an entire region into different levels. The authors of [ 35 ] developed a methodology in which one-dimensional time series smart meter data were reshaped to two-dimensional arrays called load profile images.…”

Section: Introductionmentioning

confidence: 99%

Contributions to Power Grid System Analysis Based on Clustering Techniques

Răboaca

Dumitrescu

Manea

et al. 2023

Sensors

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The topic addressed in this article is part of the current concerns of modernizing power systems by promoting and implementing the concept of smart grid(s). The concepts of smart metering, a smart home, and an electric car are developing simultaneously with the idea of a smart city by developing high-performance electrical equipment and systems, telecommunications technologies, and computing and infrastructure based on artificial intelligence algorithms. The article presents contributions regarding the modeling of consumer classification and load profiling in electrical power networks and the efficiency of clustering techniques in their profiling as well as the simulation of the load of medium-voltage/low-voltage network distribution transformers to electricity meters.

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Profiling of Household Residents’ Electricity Consumption Behavior Using Clustering Analysis

Cited by 9 publications

References 10 publications

EvolveCluster: an evolutionary clustering algorithm for streaming data

EvolveCluster: an evolutionary clustering algorithm for streaming data

Modeling Evolving User Behavior via Sequential Clustering

Contributions to Power Grid System Analysis Based on Clustering Techniques

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