Deep Belief Network Based Multi-Dimensional Phase Space for Short-Term Wind Speed Forecasting

2018 ◽  
Author(s):  
Xiaodan Wang ◽  
Yi Yang ◽  
Chuan Li
Keyword(s):  
Phase Space ◽  
Wind Speed ◽  
Deep Belief Network ◽  
Short Term ◽  
Belief Network ◽  
Wind Speed Forecasting ◽  
Dimensional Phase Space

Forecasting a short-term wind speed using a deep belief network combined with a local predictor

2018 ◽  
Vol 14 (2) ◽  
pp. 238-244 ◽  
Author(s):  
Y. Yu ◽  
Z. M. Chen ◽  
M. S. Li ◽  
T. Y. Ji ◽  
Q. H. Wu
Keyword(s):  
Wind Speed ◽  
Deep Belief Network ◽  
Short Term ◽  
Belief Network

Deep belief network based deterministic and probabilistic wind speed forecasting approach

2016 ◽  
Vol 182 ◽  
pp. 80-93 ◽  
Author(s):  
H.Z. Wang ◽  
G.B. Wang ◽  
G.Q. Li ◽  
J.C. Peng ◽  
Y.T. Liu
Keyword(s):  
Wind Speed ◽  
Deep Belief Network ◽  
Belief Network ◽  
Wind Speed Forecasting

scholarly journals Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Zexian Sun ◽  
Hexu Sun ◽  
Jingxuan Zhang
Keyword(s):  
Wind Speed ◽  
Random Forest ◽  
Wind Power ◽  
Weather Prediction ◽  
Deep Belief Network ◽  
Atmospheric Conditions ◽  
Power Prediction ◽  
Short Term ◽  
Belief Network ◽  
Wind Speed Prediction

A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.

scholarly journals Meta Learning-Based Hybrid Ensemble Approach for Short-Term Wind Speed Forecasting

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 172859-172868
Author(s):  
Zhengwei Ma ◽  
Sensen Guo ◽  
Gang Xu ◽  
Saddam Aziz
Keyword(s):  
Wind Speed ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Ensemble Approach ◽  
Meta Learning

Very short-term wind speed forecasting with Bayesian structural break model

2013 ◽  
Vol 50 ◽  
pp. 637-647 ◽  
Author(s):  
Yu Jiang ◽  
Zhe Song ◽  
Andrew Kusiak
Keyword(s):  
Wind Speed ◽  
Structural Break ◽  
Short Term ◽  
Wind Speed Forecasting

Bayesian Optimization Based ANN Model for Short Term Wind Speed Forecasting in Newfoundland, Canada

2020 ◽  
Author(s):  
Habibur Rahaman ◽  
T. M. Rubaith Bashar ◽  
Mohammad Munem ◽  
Md. Hasibul Hasan Hasib ◽  
Hasan Mahmud ◽  
...  
Keyword(s):  
Wind Speed ◽  
Bayesian Optimization ◽  
Ann Model ◽  
Short Term ◽  
Wind Speed Forecasting
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