Abstract
The increasing deployment of smart meters to collect load profile data in real-time is generating new opportunities for analysing electric power distribution system. Storing, managing and analysing large volumes of collected data, however, is challenging. Measured data is high-dimensional in nature and may contain hidden information and complex patterns that need to be interpreted. In this chapter, a method that combines dimensionality reduction (DR) technique with Particle Swarm Optimization (PSO) algorithm for clustering load profile electricity data is presented. The DR techniques allows to obtain a low-dimensional data model that can be used to project representative electricity load (REL) data onto an easily interpretable 3D space. The PSO algorithm and a validation index algorithm is then applied to obtain an optimal number of clusters. The presented framework methodology is applied to clustering historical REL data. The REL data allows to evaluate the ability of linear and nonlinear DR techniques to extract the relevant information that may be useful to visualize and improve the clustering data process.
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Cuevas, E., Barocio Espejo, E., Conde Enríquez, A. (2019). Clustering Representative Electricity Load Data Using a Particle Swarm Optimization Algorithm. In: Metaheuristics Algorithms in Power Systems. Studies in Computational Intelligence, vol 822. Springer, Cham. https://doi.org/10.1007/978-3-030-11593-7_8
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DOI: https://doi.org/10.1007/978-3-030-11593-7_8
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