Feature selection and hyper parameters optimization for short-term wind power forecast

2021 ◽  
Author(s):  
Hui Huang ◽  
Rong Jia ◽  
Xiaoyu Shi ◽  
Jun Liang ◽  
Jian Dang
Keyword(s):  
Feature Selection ◽  
Wind Power ◽  
Parameters Optimization ◽  
Short Term ◽  
Wind Power Forecast

Wind power forecast based on improved Long Short Term Memory network

Energy ◽  
2019 ◽  
Vol 189 ◽  
pp. 116300 ◽  
Author(s):  
Li Han ◽  
Huitian Jing ◽  
Rongchang Zhang ◽  
Zhiyu Gao
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Forecast ◽  
Memory Network ◽  
Long Short Term Memory

Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch

2014 ◽  
Vol 5 (1) ◽  
pp. 511-520 ◽  
Author(s):  
Le Xie ◽  
Yingzhong Gu ◽  
Xinxin Zhu ◽  
Marc G. Genton
Keyword(s):  
Power System ◽  
Wind Power ◽  
Short Term ◽  
Look Ahead ◽  
Wind Power Forecast ◽  
Spatio Temporal

scholarly journals The impact of wind field spatial heterogeneity and variability on short-term wind power forecast errors

2019 ◽  
Vol 11 (3) ◽  
pp. 033304 ◽  
Author(s):  
Mao Yang ◽  
Luobin Zhang ◽  
Yang Cui ◽  
Qiongqiong Yang ◽  
Binyang Huang
Keyword(s):  
Spatial Heterogeneity ◽  
Wind Power ◽  
Wind Field ◽  
Forecast Errors ◽  
Short Term ◽  
Wind Power Forecast ◽  
The Impact

scholarly journals Probabilistic Short-Term Wind Power Forecasting Using Sparse Bayesian Learning and NWP

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Kaikai Pan ◽  
Zheng Qian ◽  
Niya Chen
Keyword(s):  
Wind Power ◽  
Bayesian Learning ◽  
Weather Prediction ◽  
Parameters Optimization ◽  
Gaussian Kernel ◽  
Sparse Bayesian Learning ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Power Forecasting

Probabilistic short-term wind power forecasting is greatly significant for the operation of wind power scheduling and the reliability of power system. In this paper, an approach based on Sparse Bayesian Learning (SBL) and Numerical Weather Prediction (NWP) for probabilistic wind power forecasting in the horizon of 1–24 hours was investigated. In the modeling process, first, the wind speed data from NWP results was corrected, and then the SBL was used to build a relationship between the combined data and the power generation to produce probabilistic power forecasts. Furthermore, in each model, the application of SBL was improved by using modified-Gaussian kernel function and parameters optimization through Particle Swarm Optimization (PSO). To validate the proposed approach, two real-world datasets were used for construction and testing. For deterministic evaluation, the simulation results showed that the proposed model achieves a greater improvement in forecasting accuracy compared with other wind power forecast models. For probabilistic evaluation, the results of indicators also demonstrate that the proposed model has an outstanding performance.

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