Markov Random Field for Wind Farm Planning
Hale Cetinay-Iyicil (TU Delft - Network Architectures and Services)
Taygun Kekeç (TU Delft - Pattern Recognition and Bioinformatics)
Fernando Kuipers (TU Delft - Embedded Systems)
David M. J. Tax (TU Delft - Pattern Recognition and Bioinformatics)
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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