Analyzing the Behavior of Electricity Consumption Using Hadoop
Authors: - Dr. Mohammed Abdul Waheed, Samreen Sultana`
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In a competitive retail market, large volumes of smart meter data provide opportunities for load serving entities to
enhance their knowledge of customers’ electricity consumption behaviors via load profiling. Instead of focusing on the shape of
the load curves, this paper proposes a novel approach for clustering of electricity consumption behavior dynamics,
where“dynamics” refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent
periods. First, for each individual customer, symbolic aggregate approximation is performed to reduce the scale of the data set,
and time-based Markov model is applied to model the dynamic of electricity consumption, transforming the large data set of
load curves to several state transition matrixes. Second, a clustering technique by fast search and find of density peaks
(CFSFDP) is primarily carried out to obtain the typical dynamics of consumption behavior, with the difference between any two
consumption patterns measured by the Kullback–Liebler distance, and to classify the customers into several clusters. To tackle
the challenges of big data, the CFSFDP technique is integrated into a divide-andconquer approach toward big data applications.
A numerical case verifies the effectiveness of the proposed models and approaches.