Abstract
The successful realization of a wind energy project, i.e. a wind farm, wind plant or, in its simplest form, a single wind turbine generator, WTG, strongly depends on accurate assessment of wind energy resources at the target location. While larger wind energy projects include extensive wind resource measurements, this is typically not feasible for small-scale wind applications. This paper presents a simple linear regression model for evaluating wind energy resources at unmonitored sites, based on the correlational analysis of a limited number of sites where wind energy measurements are available. The methodology is illustrated on two regions in Scotland, where wind speed and wind direction measurements are publicly available from 26 UK Met Office stations. The presented approach requires only the coordinates of the end-user's WTG location to estimate the available wind energy and incorporates a method to identify the best-fit WTGs for the calculated wind energy resource. For the selection of WTG, a database of 200 small-scale wind turbines available to UK end-users was constructed, reflecting the current state of the market.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE Manchester PowerTech, Powertech 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509042371 |
| DOIs | |
| Publication status | Published - 13 Jul 2017 |
| Externally published | Yes |
| Event | 2017 IEEE Manchester PowerTech, Powertech 2017 - Manchester, United Kingdom Duration: 18 Jun 2017 → 22 Jun 2017 |
Publication series
| Name | 2017 IEEE Manchester PowerTech, Powertech 2017 |
|---|
Conference
| Conference | 2017 IEEE Manchester PowerTech, Powertech 2017 |
|---|---|
| Country/Territory | United Kingdom |
| City | Manchester |
| Period | 18/06/17 → 22/06/17 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Correlational analysis
- spatio-temporal analysis
- wind energy
- wind generation turbine
- wind resource
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