Deep learning to predict the generation of a wind farm

One of today's greatest technological challenges is adding renewable energies to an electric grid, with the goal being to achieve sustainable and environmentally friendly electricity generation that is also affordable. In order for the incorporation of renewables to be successful, however, predictive tools are required which can be used to determine sufficiently far in advance how much renewable energy will be available to be injected into the grid so that the remaining generation sources, including those based on fossil fuels, can be adjusted in order to fill the demand. This would limit the environmental impact and the dependence on this type of fuel in a foreseeable shortfall scenario. This paper seeks to advance in the creation of these predictive generation models for wind farms using deep learning. We present a predictive model based on a deep, multi-layered neural network that based on a forecast for atmospheric conditions is capable of estimating the generation produced by a wind farm 24 h in adva...

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