Markov Random Field for wind farm planning
| Autor: | David M. J. Tax, Hale Cetinay, Taygun Kekec, Fernando A. Kuipers |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
Markov random field Wind power Operations research business.industry Fuzzy set Farm planning quality of wind Investment (macroeconomics) planning for wind power integration Proof of concept spatial relations in wind farm investments Key (cryptography) criteria for wind farms business Environmental planning Decision analysis |
| Zdroj: | 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE) |
| DOI: | 10.1109/sege.2017.8052796 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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