DUGiDocsMétodo para descubrir patrones espacio-temporales de comportamiento de usuarios eléctricos utilizando herramientas de aprendizaje automático
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This research presents a novel approach to define geographic areas with
typical electricity consumption profiles from smart meter records, using
machine learning and spatial analysis methods.
Distribution system operators must guarantee the quality and reliability of the electric
service. To achieve this objective, electricity distribution utilities need to know in detail
and with an adequate periodicity the consumption profiles of their customers. Modern
telemetering devices, such as smart meters, open the door to an immense amount of
data and new analysis, due to a higher frequency and precision of consumer electrical
consumption. However, conventional methods cannot deal with the voluminous and
fast gathered data by smart meters. The objective of this research is to apply machine
learning techniques combined with spatial analysis to generate more efficient and
accurate load profiles in the areas of study.
The study analyzes a voluminous database of measurements gathered during 4 years
by 1000 georeferenced smart meters located in the city of Guayaquil in Ecuador.
In the study an unsupervised learning methodology to group and classify the time
series of energy measurements, using the dynamic time warping technique to discover
typical load profiles according to their characteristic weekly consumption, is applied.
Next, we perform a restricted space-time analysis to define geographic areas with
constant and predictable behavior
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