Spatio-temporal estimation of wind speed and wind power using machine
learning: predictions, uncertainty and technical potential
- URL: http://arxiv.org/abs/2108.00859v1
- Date: Thu, 29 Jul 2021 09:52:36 GMT
- Title: Spatio-temporal estimation of wind speed and wind power using machine
learning: predictions, uncertainty and technical potential
- Authors: Federico Amato, Fabian Guignard, Alina Walch, Nahid Mohajeri,
Jean-Louis Scartezzini, Mikhail Kanevski
- Abstract summary: The wind power estimate presented here represents an important input for planners to support the design of energy systems with increased wind power generation.
The methodology is applied to the study of hourly wind power potential on a grid of $250times 250$ m$2$ for turbines of 100 meters hub height in Switzerland.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The growth of wind generation capacities in the past decades has shown that
wind energy can contribute to the energy transition in many parts of the world.
Being highly variable and complex to model, the quantification of the
spatio-temporal variation of wind power and the related uncertainty is highly
relevant for energy planners. Machine Learning has become a popular tool to
perform wind-speed and power predictions. However, the existing approaches have
several limitations. These include (i) insufficient consideration of
spatio-temporal correlations in wind-speed data, (ii) a lack of existing
methodologies to quantify the uncertainty of wind speed prediction and its
propagation to the wind-power estimation, and (iii) a focus on less than hourly
frequencies. To overcome these limitations, we introduce a framework to
reconstruct a spatio-temporal field on a regular grid from irregularly
distributed wind-speed measurements. After decomposing data into temporally
referenced basis functions and their corresponding spatially distributed
coefficients, the latter are spatially modelled using Extreme Learning
Machines. Estimates of both model and prediction uncertainties, and of their
propagation after the transformation of wind speed into wind power, are then
provided without any assumptions on distribution patterns of the data. The
methodology is applied to the study of hourly wind power potential on a grid of
$250\times 250$ m$^2$ for turbines of 100 meters hub height in Switzerland,
generating the first dataset of its type for the country. The potential wind
power generation is combined with the available area for wind turbine
installations to yield an estimate of the technical potential for wind power in
Switzerland. The wind power estimate presented here represents an important
input for planners to support the design of future energy systems with
increased wind power generation.
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