Short-term Wind Power Prediction Model Based on GSA Optimized GRU Neural Network

2020 ◽  
Author(s):  
Qingyun Xie ◽  
Lianqing Song ◽  
Yongkang He ◽  
Pengju Dang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Wind Power Prediction

Application of the Combination Prediction Model in Forecasting the Short-Term Wind Power

2015 ◽  
Vol 733 ◽  
pp. 893-897
Author(s):  
Peng Yu Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Pso Algorithm ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Combination Forecasting ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

scholarly journals Short-term Wind Power Prediction Model Based on Encoder-Decoder LSTM

2018 ◽  
Vol 186 ◽  
pp. 012020
Author(s):  
Kuan Lu ◽  
Wen Xue Sun ◽  
Xin Wang ◽  
Xiang Rong Meng ◽  
Yong Zhai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Wind Power Prediction

scholarly journals Ultra-short-term Wind Power Prediction Model Based on VMD Decomposition and LSTM

2021 ◽  
Vol 838 (1) ◽  
pp. 012002
Author(s):  
Xingdou Liu ◽  
Li Zhang ◽  
Zhirui Zhang ◽  
Tong Zhao ◽  
Liang Zou
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Wind Power Prediction

Wind Power Prediction Model Based on Wavelet Neural Network under Missing Data

2015 ◽  
Vol 737 ◽  
pp. 76-80
Author(s):  
Jing Lu ◽  
Yan Qing Zhao ◽  
Yu Hong Zhao ◽  
Jun Yi Zhao ◽  
Chao Ying Yang
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Prediction Model ◽  
Wind Power ◽  
Wind Farm ◽  
Wavelet Neural Network ◽  
Power Prediction ◽  
Model Based ◽  
Wind Power Prediction ◽  
Mean Square Errors

Wind power prediction is a key problem in optimizing power dispatching. This paper builds a wind power prediction model based on wavelet neural network which substitutes wavelet basis function for the transfer function of hidden layer. A missing data interpolation strategy is also given to improve the applicability of the model. With the wind farm data from southeast coast, the model works and the wind power in the next 30 hours is predicted. In the sense of the mean square errors this paper compared the prediction results of the model and BP neural network model, the results shows the models have a better accuracy.

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