E3S Web Conf.
Volume 64, 20182018 3rd International Conference on Power and Renewable Energy
|Number of page(s)||6|
|Section||Power System and Energy|
|Published online||27 November 2018|
Characterization and Classification of Daily Electricity Consumption Profiles: Shape Factors and k-Means Clustering Technique
1 Grupo de Investigación en Energías, Universidad Politécnica Salesiana, Cuenca, Ecuador
2 Institute for Energy Engineering, Universitat Politècnica de València, Valencia, Spain
This paper exposes a method to classify the electric consumption profiles of different types of consumers, based on patterns given. The direct characteristics method is used in this paper, this method is also known as shape factors deduction (SFs) to easily define consumption profiles by using the load patterns resulting from measurements in the time domain, considering weekdays and time ranges. After the characterization of load profiles, k-means clustering technique is applied to SFs. The SFs are segmented in such a way that, in each group similar SFs are gathered. The characterization and classification of electric profiles has important applications, such as the application of specific tariffs according the consumer type, determination of optimal location of generation resources in electrical distribution systems, detection of anomalies in transmission and distribution of electricity or classify geographical areas according to electricity consumption and perform an optimum balance of feeders in electrical substations.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.