@Article{cmes.2020.08768,
AUTHOR = {Xing Deng, Haijian Shao, Chunlong Hu, Dengbiao Jiang, Yingtao Jiang},
TITLE = {Wind Power Forecasting Methods Based on Deep Learning: A Survey},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {122},
YEAR = {2020},
NUMBER = {1},
PAGES = {273--301},
URL = {http://www.techscience.com/CMES/v122n1/38246},
ISSN = {1526-1506},
ABSTRACT = {Accurate wind power forecasting in wind farm can effectively reduce the
enormous impact on grid operation safety when high permeability intermittent power
supply is connected to the power grid. Aiming to provide reference strategies for relevant
researchers as well as practical applications, this paper attempts to provide the literature
investigation and methods analysis of deep learning, enforcement learning and transfer
learning in wind speed and wind power forecasting modeling. Usually, wind speed and
wind power forecasting around a wind farm requires the calculation of the next moment
of the definite state, which is usually achieved based on the state of the atmosphere
that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles.
As an effective method of high-dimensional feature extraction, deep neural network can
theoretically deal with arbitrary nonlinear transformation through proper structural design,
such as adding noise to outputs, evolutionary learning used to optimize hidden layer
weights, optimize the objective function so as to save information that can improve the
output accuracy while filter out the irrelevant or less affected information for forecasting.
The establishment of high-precision wind speed and wind power forecasting models is
always a challenge due to the randomness, instantaneity and seasonal characteristics.},
DOI = {10.32604/cmes.2020.08768}
}