Markov Random Field for Wind Farm Planning

Conference paper (2017)

Authors

H. Çetinay Iyicil Network Architectures and Services - EEMCS
I.T. Kekec Pattern Recognition and Bioinformatics - EEMCS
F.A. Kuipers Embedded Systems - EEMCS
D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS
Research Group
Network Architectures and Services (EEMCS) (TU Delft)
Copyright: © 2017 H. Çetinay Iyicil , I.T. Kekec, F.A. Kuipers, D.M.J. Tax
DOI
help_outline
https://doi.org/10.1109/SEGE.2017.8052796
Quality of wind Criteria for wind farms Spatial relations in wind farm investments Planning for wind power integration
More Info
expand_more

Published Date

2017

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Network Architectures and Services

Abstract

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

Files

MRF4Wind.pdf
(.pdf | 2 Mb)