@Article{EE.2020.011619,
AUTHOR = {Xing Deng, Haijian Shao},
TITLE = {Deep Learning Approach with Optimizatized Hidden-Layers Topology for Short-Term Wind Power Forecasting},
JOURNAL = {Energy Engineering},
VOLUME = {117},
YEAR = {2020},
NUMBER = {5},
PAGES = {279--287},
URL = {http://www.techscience.com/energy/v117n5/40113},
ISSN = {1546-0118},
ABSTRACT = {Recurrent neural networks (RNNs) as one of the representative deep
learning methods, has restricted its generalization ability because of its indigestion
hidden-layer information presentation. In order to properly handle of hidden-layer
information, directly reduce the risk of over-fitting caused by too many neuron
nodes, as well as realize the goal of streamlining the number of hidden layer neurons, and then improve the generalization ability of RNNs, the hidden-layer information of RNNs is precisely analyzed by using the unsupervised clustering
methods, such as Kmeans, Kmeans++ and Iterative self-organizing data analysis
(Isodata), to divide the similarity of raw data points, and maps the hidden-layer
information into the feature space where data separation is easily implemented.
Experiments based on dataset from the National Renewable Energy Laboratory
(NREL) is proposed to demonstrate the performance of the proposed approaches,
the average forecasting errors of which is respectively increased by 2.1%, 7.6%,
10.26% with respect to 6-steps, 12-steps and 18-steps in four seasons over the
ones that achieved using the traditional deep learning approaches.},
DOI = {10.32604/EE.2020.011619}
}