Segmentation of Portuguese Electricity Daily Load Demand using Self-Organizing Maps

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

From Portuguese historical data, a Kohonen's Self-Organizing Map (SOM) is used to classify the days of the year according to their load curve profile. The SOM Kohonen's algorithm is an unsupervised neural network clustering technique often used for pattern classification tasks that preserves the topology of data. Through the visualization of the data by projecting it into a bidimensional grid it is possible to identify the 'natural' clusters. The main objective of the classification is to separate the load demand by clusters for forecasting purposes. The classification/clustering via SOM will be the first phase of a so called Artificial Neural Networks hybrid model, where two or more ANN models are combined in order to draw more accurate predictions. The historical data used is the Portuguese continental load demand from 2009 to 2017 registered in periods of 15 min, comprising a total of 96 load registries per day. This data will be classified in different clusters according to its load curve profile.

Original languageEnglish
Title of host publication2021 IEEE Electrical Power and Energy Conference, EPEC 2021
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages220-224
Number of pages5
ISBN (Electronic)9781665429283
DOIs
Publication statusPublished - 2021
Event2021 IEEE Electrical Power and Energy Conference, EPEC 2021 - Virtual, Online, Canada
Duration: 22 Oct 202131 Oct 2021

Conference

Conference2021 IEEE Electrical Power and Energy Conference, EPEC 2021
Country/TerritoryCanada
CityVirtual, Online
Period22/10/2131/10/21

Keywords

  • artificial neural networks
  • electric demand
  • self-organizing Maps
  • short-term load forecasting
  • time series clustering

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