Generation and validation of spatial distribution of hourly wind speed time-series using machine-learning
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Date
2016-09-14
Publication Type
Conference Paper
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yes
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Abstract
Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.
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Publication status
published
Editor
Book title
Journal / series
Volume
749 (1)
Pages / Article No.
12001
Publisher
IOP Publishing
Event
WindEurope Summit 2016
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
wind resource assessment; time-series; confidence intervals
Organisational unit
03901 - Raubal, Martin / Raubal, Martin