Markov Random Field for Wind Farm Planning

Hale Cetinay-Iyicil, Taygun Kekec, Fernando Kuipers, David Tax

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

1 Citation (Scopus)
169 Downloads (Pure)


Many countries aim to integrate a substantial amount of wind energy in the near future. This requires meticulous planning, which is challenging due to the uncertainty in wind profiles. In this paper, we propose a novel framework to discover and investigate those geographic areas that are well suited for building wind farms. We combine the key indicators of wind farm investment using fuzzy sets, and employ multiple-criteria decision analysis to obtain a coarse wind farm suitability value. We further demonstrate how this suitability value can be refined by a Markov Random Field (MRF) that takes the dependencies between adjacent areas into account. As a proof of concept, we take wind farm planning in Turkey, and demonstrate that our MRF modeling can accurately find promising areas
Original languageEnglish
Title of host publication2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE)
Place of PublicationPiscataway
Number of pages6
ISBN (Electronic)978-1-5386-1776-2
ISBN (Print)978-1-5386-1775-5
Publication statusPublished - 2017
Event2017 The 5th IEEE International Conference on Smart Energy Grid Engineering (SEGE): SEGE 2017 - Oshawa, Canada
Duration: 14 Aug 201717 Aug 2017


Conference2017 The 5th IEEE International Conference on Smart Energy Grid Engineering (SEGE)


  • quality of wind
  • criteria for wind farms
  • spatial relations in wind farm investments
  • planning for wind power integration


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