Abstract
Edinburgh Postnatal Depression Scale (EDPD) is continuously seeking new and efficient ways to access and process the high volume of data regarding low-voltage customers, taking advantage of the continued technological investments being made in Advanced Metering Infrastructure. Clustering, as a technique, has been widely used to turn that information into typical load/generation profiles. In this context, this study presents a big data cloud-oriented approach, using Azure Databricks, developed to cluster customer profiles into typical load/generation.
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