Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function

Author:

Liu Xin,Zhou Jun,Qian Huimin

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference52 articles.

1. Short-term wind power combined forecasting based on error forecast correction;Liang;Energy Convers. Manage.,2016

2. Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional gated recurrent unit network and support vector regression;Liu;Renew. Energy,2019

3. A review on the forecasting of wind speed and generated power;Lei;Renewable& Sustainable Energy Reviews,2009

4. Current methods and advances in forecasting of wind power generation;Foley;Renew. Energy,2012

5. G. Giebel, R. Brownsword, G. Kariniotakis, M. Denhard, C. Draxl, The state-of-the-art in short-term prediction of wind power: A literature overview, 2011, (https://academic.microsoft.com/paper/2593375484).

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