Titlebook: Smart Meter Data Analytics; Electricity Consumer Yi Wang,Qixin Chen,Chongqing Kang Book 2020 Science Press and Springer Nature Singapore Pt

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Electricity Theft Detection,nd more difficult to detect. Thus, a data analytics method for detecting various types of electricity thefts is required. However, the existing methods either require a labeled dataset or additional system information which is difficult to obtain in reality or have poor detection accuracy. In this c

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Partial Usage Pattern Extraction,ommunication and storage of big data from smart meters at a reduced cost which has been discussed in Chap. .. The other one is the effective extraction of useful information from this massive dataset. In this chapter, the K-SVD sparse representation technique, which includes two phases (dictionary l

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Socio-demographic Information Identification, automatically extracts features from massive load profiles. A support vector machine (SVM) then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the e

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Clustering of Consumption Behavior Dynamics, customers’ electricity consumption behaviors via load profiling. Instead of focusing on the shape of the load curves, this chapter proposes a novel approach for the clustering of electricity consumption behavior dynamics, where “dynamics” refer to transitions and relations between consumption behav

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Probabilistic Residential Load Forecasting,forecasting possible. Compared to aggregated loads, load forecasting for individual consumers is prone to non-stationary and stochastic features. In this chapter, a probabilistic load forecasting method for individual consumers is proposed to handle the variability and uncertainty of future load pro