OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering

Lin, Rongheng; Ye, Zezhou; Zhao, Yingying

doi:10.3390/en12142668

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…Load clustering is an important task in demand-side management, which aims to divide electricity users into different groups through feature similarity to enhance the targeting of demand response [3][4][5][6]. Obviously, clustering algorithm is one of the keys to achieve effective partitioning.…”

Section: Introductionmentioning

confidence: 99%

A shape‐based clustering algorithm and its application to load data

Xian

Dong

et al. 2023

Cognitive Comp and Systems

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The popularity of smart metres has brought a huge amount of demand‐side data, which provides important information for the demand response of the power sector, to guide practitioners to understand the customers' electricity usage behaviours and patterns. Clustering analysis of customers' daily load data is an important tool for mining users' consumption habits and achieve non‐fixed market segmentation. Since the load data is time series, it is inappropriate to perform clustering directly without extracting targeted features. Therefore, according to the shape features of the daily load curve, a shape‐based clustering algorithm called BDKM is proposed. The algorithm first uses the B‐splines regression to fit the time series data to extract morphological features, and then the objects are segmented based on the dynamic time warping distance by clustering. Finally, the real world daily customers' load data is used to prove the effectiveness of the proposed algorithm based on B‐splines regression.

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Section: Introductionmentioning

confidence: 99%

A shape‐based clustering algorithm and its application to load data

Xian

Dong

et al. 2023

Cognitive Comp and Systems

View full text Add to dashboard Cite

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“…For example, in video segmentation tasks, segmentation can be conducted frame by frame, and results of successive frames have temporal continuity because adjacent frames share similarities [3] [4]. Such instances are also ubiquitous in dynamic community detection [5] [6] [7], user preference mining [8], and consumer behavior analysis [9], etc.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Robust Clustering Over Time for Temporal Data

Zhao,

Yan,

Shi

2021

Preprint

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In many clustering scenes, data samples' attribute values change over time. For such data, we are often interested in obtaining a partition for each time step and tracking the dynamic change of partitions. Normally, a smooth change is assumed for data to have a temporal smooth nature. Existing algorithms consider the temporal smoothness as an a priori preference and bias the search towards the preferred direction. This a priori manner leads to a risk of converging to an unexpected region because it is not always the case that a reasonable preference can be elicited given the little prior knowledge about the data. To address this issue, this paper proposes a new clustering framework called evolutionary robust clustering over time. One significant innovation of the proposed framework is processing the temporal smoothness in an a posteriori manner, which avoids unexpected convergence that occurs in existing algorithms. Furthermore, the proposed framework automatically tunes the weight of smoothness without data's affinity matrix and predefined parameters, which holds better applicability and scalability. The effectiveness and efficiency of the proposed framework are confirmed by comparing with state-of-the-art algorithms on both synthetic and real datasets.

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“…The seasonal variations due to temperature, working patterns, and human behavior, cause variations in the energy consumption patterns. These patterns could be used in monitoring systems to detect abnormal consumption [3,4]. The detected anomalies can be reported to managers of facilities to take the necessary corrections.…”

Section: Introductionmentioning

confidence: 99%

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

et al. 2020

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Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.

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OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering

Cited by 7 publications

References 30 publications

A shape‐based clustering algorithm and its application to load data

A shape‐based clustering algorithm and its application to load data

Evolutionary Robust Clustering Over Time for Temporal Data

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

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