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.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Machine learning and geographic information systems for large-scale wind energy potential estimation in rural areas |
| Event: | CISBAT 2019: Climate Resilient Cities – Energy Efficiency & Renewables in the Digital Era |
| Location: | SWITZERLAND, Ecole Polytechnique Fed Lausanne, Solar Energy & Building Phys Lab, Lausanne |
| Dates: | 4 Sep 2019 - 6 Sep 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1742-6596/1343/1/012036 |
| Publisher version: | http://dx.doi.org/10.1088/1742-6596/1343/1/012036 |
| Language: | English |
| Additional information: | 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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10200260 |
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