eprintid: 10200260 rev_number: 8 eprint_status: archive userid: 699 dir: disk0/10/20/02/60 datestamp: 2024-12-17 14:40:27 lastmod: 2024-12-17 14:45:37 status_changed: 2024-12-17 14:40:27 type: proceedings_section metadata_visibility: show sword_depositor: 699 creators_name: Assouline, Dan creators_name: Mohajeri, Nahid creators_name: Mauree, Dasaraden creators_name: Scartezzini, Jean-Louis title: Machine learning and geographic information systems for large-scale wind energy potential estimation in rural areas ispublished: pub divisions: UCL divisions: B04 divisions: C04 divisions: F34 note: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence, https://creativecommons.org/licenses/by/3.0/. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. abstract: Clean, safe, affordable and available in the long-term, wind is one of the most promising sources of renewable energy. Its optimized and profitable use, however, requires an estimation of the potential in locations of interest, given its very volatile behavior in various settings. In the present study, we propose a methodology using a combination of Machine Learning (Random Forests), Geographic Information Systems and wind parametric models to estimate the large-scale theoretical wind speed potential in rural areas over the entire Switzerland. The monthly wind speed over rural areas is estimated based on wind speed measurements and several meteorological, topographic, and wind-specific features available accross the country. Wind speed values and their associated uncertainty are computed at the scale of 200 x 200 [m2] pixels covering the territory, at a typical height for rural commercial wind turbine installation, that is, z=100m. The developed methodology, is, however, applicable to any large region, given the availability of data of interest. The results show that in the case of Switzerland, wind turbines could approximately represent an non-negligible installed power capacity of, for each pixel and for each turbine installation, on average 80 kW in Swiss rural areas, and up to 1600 kW in most suitable pixels. date: 2019 date_type: published publisher: IOP Publishing official_url: http://dx.doi.org/10.1088/1742-6596/1343/1/012036 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1907909 doi: 10.1088/1742-6596/1343/1/012036 lyricists_name: Mohajeri, Nahid lyricists_id: NMOHA02 actors_name: Mohajeri, Nahid actors_id: NMOHA02 actors_role: owner full_text_status: public pres_type: paper series: Journal of Physics Conference Series publication: CLIMATE RESILIENT CITIES - ENERGY EFFICIENCY & RENEWABLES IN THE DIGITAL ERA (CISBAT 2019) volume: 1343 number: 1 place_of_pub: Bristol, UK pagerange: 012036 pages: 6 event_title: CISBAT 2019: Climate Resilient Cities – Energy Efficiency & Renewables in the Digital Era event_location: SWITZERLAND, Ecole Polytechnique Fed Lausanne, Solar Energy & Building Phys Lab, Lausanne event_dates: 4 Sep 2019 - 6 Sep 2019 issn: 1742-6588 book_title: Journal of Physics: Conference Series citation: Assouline, Dan; Mohajeri, Nahid; Mauree, Dasaraden; Scartezzini, Jean-Louis; (2019) Machine learning and geographic information systems for large-scale wind energy potential estimation in rural areas. In: Journal of Physics: Conference Series. (pp. 012036). IOP Publishing: Bristol, UK. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10200260/1/Assouline_2019_J._Phys.__Conf._Ser._1343_012036.pdf