Comparing Electricity Consumer Categories Based on Load Pattern Clustering with Their Natural Types

Jiang, Zigui; Lin, Rongheng; Yang, Fangchun; Liu, Zhihan; Zhang, Qi-Qi

doi:10.1007/978-3-319-65482-9_51

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…Massive Online Analysis (MOA) is an open source framework for large data streams analysis, project that is the complement of WEKA for Big Data analysis. [3], [40], [44], [49], [64], [67], [84], [94], [129], [153], [160], [204], [212], [214], [245]), fuzzy c-means clustering (7) ( [49], [64], [173], [204], [245], [265], [266]), Hierarchical Clustering (HAC) (7) ( [44], [56], [64], [94], [204], [212], [232]), Support Vector Machine (SVM) (6) [3], [112], [150], [204], [239], [250], Self-Organising Map (SOM) (4) [2], [64], [167], [212], Multi Layer Perceptron (MLP) ANN (3) [40], [150], [232], t-means clustering [183], k-Nearest Neighbour (kNN) [112], [204], Random Forest…”

Section: Sms Resultsmentioning

confidence: 99%

“…SG failures deals with fault status detection, fault type classification, power distribution reliability. [3], [26], [40], [75], [84], [94], [112], [129], [160], [167], [173], [204], [212], [214], [232], [233], [239], [245]), power consumption pattern recognition ( [7], [44], [49], [64], [67], [131], [150], [153], [183], [265], [266]), power load forecasting ( [56], [250]), events/tasks extraction ( [50])…”

Section: B Rq2mentioning

confidence: 99%

See 1 more Smart Citation

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Section: B Rq2mentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…These consumers locate in 73 locations of the same district and are labeled with their consumer types, including sixteen different types which are restaurant, school, office, supermarket, etc. However, these consumer types refer to their building types and are not equal to the consumer categories that we require [40]. They cannot be used as labels for assessing the accuracy of clustering and classification.…”

Section: Datasetmentioning

confidence: 99%

“…The values will increase dramatically if we use consumer types as labels for measuring Accuracy-WIL in classification phase. Nevertheless, our previous work [40] proves that such consumer types are not appropriate for being labels because consumers in different types may have the similar electricity consumption behaviors which lead them to be assigned into the same consumer categories.…”

Section: New Consumer Classificationmentioning

confidence: 99%

A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data

2018

Self Cite

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Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose a hybrid machine learning model including both unsupervised clustering and supervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. Unsupervised clustering algorithm is used to extract the typical electricity consumption behaviors and perform fuzzy consumer categorization, followed by a proposed novel algorithm to identify distinct consumer categories and their consumption characteristics. Supervised classification algorithm is used to classify new consumers and evaluate the validity of the identified categories. The proposed model is applied to a real dataset of U.S. non-residential consumers collected by smart meters over one year. The results indicate that large or special institutions usually have their distinct consumption characteristics while others such as some medium and small institutions or similar building types may have the same characteristics. Moreover, the comparison results with other methods show the improved performance of the proposed model in terms of category identification and classifying accuracy.

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