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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Partial Usage Pattern Extraction,he extracted patterns. Comprehensive comparisons with the results of .-means clustering, the discrete wavelet transform (DWT), principal component analysis (PCA), and piecewise aggregate approximation (PAA) are conducted on real datasets in Ireland. The results show that our proposed technique outpe

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Clustering of Consumption Behavior Dynamics,y carried out to obtain the typical dynamics of consumer behavior, with the difference between any two consumption patterns measured by the Kullback–Liebler (K–L) distance, and to classify the customers into several clusters. To tackle the challenges of big data, the CFSFDP technique is integrated i

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Aggregated Load Forecasting with Sub-profiles,ing the number of clusters. Finally, an optimal weighted ensemble approach is employed to combine these forecasts and provide the final forecasting result. Case studies are conducted on two open datasets and verify the effectiveness and superiority of the proposed method.

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Book 2020umer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly

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