Shapelet based classification of customer consumption patterns

Butunoi, Bogdan-Petru; Frîncu, Marc

doi:10.1109/isgteurope.2017.8260281

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…In this case, the author has to deal with classes that are imbalanced and incrementally updated, and with the online application of the model. Another study, which approximates to ours, analyzes the effectiveness of the shapelet algorithm in classifying various weekend consumption patterns extracted from real-life data [16]. The author suggests the potential of shapelets to determine which customers use more electricity at weekends, a period in which consumption usually reduces, which saves during peak hours, or who responds to demand response events.…”

Section: Related Workmentioning

confidence: 92%

“…When considering the energy-specific domain in the application of those techniques, shapelets is one that was not extensively applied. From the literature review, shapelets were used for non-intrusive load monitoring (NILM) [15], discovering customer weekend load patterns [16], classification of district heating substations [17], evaluation of voltage stability [18][19][20], and clustering power curves [21] with a modified version of shapelets to work as an unsupervised technique. Although with a few applications, it is evident that shapelets have not yet extensively assessed time series with a power load at the national level, as well as at primary and secondary substations.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with [16], we used data from the system level (instead of 33 buildings at the low-voltage level) with the same number of quarter-hourly training time series (namely 300 compared to 272) and a higher number of testing time series (namely, 2566 compared to 636). Although they have slightly different goals, both studies reach the same degree of accuracy (89%).…”

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confidence: 99%

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Shapelets to Classify Energy Demand Time Series

2022

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Data are an important asset that the electric power industry have available today to support management decisions, excel in operational efficiency, and be more competitive. The advent of smart grids has increased power grid sensorization and so, too, the data availability. However, the inability to recognize the value of data beyond the siloed application in which data are collected is seen as a barrier. Power load time series are one of the most important types of data collected by utilities, because of the inherent information in them (e.g., power load time series comprehend human behavior, economic momentum, and other trends). The area of time series analysis in the energy domain is attracting considerable interest because of growing available data as more sensorization is deployed in power grids. This study considers the shapelet technique to create interpretable classifiers for four use cases. The study systematically applied the shapelet technique to data from different hierarchical power levels (national, primary power substations, and secondary power substations). The study has experimentally shown shapelets as a technique that embraces the interpretability and accuracy of the learning models, the ability to extract interpretable patterns and knowledge, and the ability to recognize and monetize the value of the data, important subjects to reinforce the importance of data-driven services within the energy sector.

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Section: Related Workmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Shapelets to Classify Energy Demand Time Series

2022

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“…Besides the shape-based and structure-based methods, some research tried to classify electricity consumers by analysing their electricity consumption time series with machine learning (ML) techniques, e.g., [16]- [18]. While ML techniques can generally handle extremely complex systems and can infer from incomplete data, their application in power system operation, as a safety-critical system, casts a doubt.…”

Section: Introductionmentioning

confidence: 99%

IRMAC: Interpretable Refined Motifs and Binary Classification for Rooftops PV Owners

Rui¹,

Pourmousavi²,

Soong³

et al. 2021

Preprint

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In this paper, we seek to identify residential rooftop solar PV owners using imported energy data. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose Interpretable Refined Motifs And binary Classification (IRMAC) method, which includes a shape-based dimensionality reduction technique we call Refined Motif (RM), and a classification technique with linear complexity to identify solar owners. Furthermore, with the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners as well as identifying electrical heating system users. The performances of the proposed method is studied and compared with various of state-of-the-art methods, where the proposed method outperformed the alternatives.

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Shapelet based classification of customer consumption patterns

Cited by 2 publications

References 18 publications

Shapelets to Classify Energy Demand Time Series

Shapelets to Classify Energy Demand Time Series

IRMAC: Interpretable Refined Motifs and Binary Classification for Rooftops PV Owners

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