@Article{cmes.2022.019245,
AUTHOR = {Dandan Xu, Haijian Shao, Xing Deng, Xia Wang},
TITLE = {The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {131},
YEAR = {2022},
NUMBER = {2},
PAGES = {567--597},
URL = {http://www.techscience.com/CMES/v131n2/47007},
ISSN = {1526-1506},
ABSTRACT = {As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind
and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach
with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for
forecasting wind power and PV production. The physical model, statistical learning method, and machine learning
approaches based on historical data are all evaluated for the forecasting of wind power and PV production.
Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation.
With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its
causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits
and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis
methods.},
DOI = {10.32604/cmes.2022.019245}
}