Load estimation based on self-organizing maps and bayesian networks for microgrid design in rural zones

V. Caquilpan, Doris Sáez, Roberto Hernández, Jacqueline Llanos, T. Roje, Alfredo Nunez

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)
68 Downloads (Pure)

Abstract

Microgrids are suitable electrical solutions for providing energy in rural zones. However, it is challenging to propose in advance a good design of the microgrid because the electrical load is difficult to estimate due to its highly dependence of the residential consumption. In this paper, a novel estimation methodology for the residential load profiles is proposed. Socio-demographic data and electrical power consumption are used to generate significant knowledge about the load behavior. Socio-demographic data are used as input for a neural network called Self-Organizing Maps (SOM). The SOM proposes a way to group dwelling according to their different features. Moreover, a probabilistic model based on Bayesian networks incorporates daily variations of the electrical load, simulating the behavior of the electrical appliances. The methodology, as a whole, is applied to a case study in a rural community located in Chile. The methodology is easily adaptable to other rural communities.
Original languageEnglish
Title of host publication2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America). Quito, Ecuador
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-5386-3312-0
Publication statusPublished - 2017
Event2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America - JW Marriott Hotel, Quito, Ecuador
Duration: 20 Sep 201722 Sep 2017
http://ieee-isgt-latam.org/files/2017/08/Technical-Program_IEEE_ISGT_LA_2017.pdf

Conference

Conference2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America
Abbreviated title2017 ISGT Latin America
CountryEcuador
CityQuito
Period20/09/1722/09/17
Internet address

Keywords

  • Microgrids
  • residential load profiles
  • rural communities

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