scholarly journals Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5400
Author(s):  
Pei Zhang ◽  
Chunping Li ◽  
Chunhua Peng ◽  
Jiangang Tian
Keyword(s):  
Wind Power ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Support Vector ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Prediction ◽  
Long Short Term Memory ◽  
The Impact

To improve the accuracy of ultra-short-term wind power prediction, this paper proposed a model using modified long short-term memory (LSTM) to predict ultra-short-term wind power. Because the forget gate of standard LSTM cannot reflect the correction effect of prediction errors on model prediction in ultra-short-term, this paper develops the error following forget gate (EFFG)-based LSTM model for ultra-short-term wind power prediction. The proposed EFFG-based LSTM model updates the output of the forget gate using the difference between the predicted value and the actual value, thereby reducing the impact of the prediction error at the previous moment on the prediction accuracy of wind power at this time, and improving the rolling prediction accuracy of wind power. A case study is performed using historical wind power data and numerical prediction meteorological data of an actual wind farm. Study results indicate that the root mean square error of the wind power prediction model based on EFFG-based LSTM is less than 3%, while the accuracy rate and qualified rate are more than 90%. The EFFG-based LSTM model provides better performance than the support vector machine (SVM) and standard LSTM model.

A novel wavenets long short term memory paradigm for wind power prediction

2020 ◽  
Vol 269 ◽  
pp. 115098 ◽  
Author(s):  
Farah Shahid ◽  
Aneela Zameer ◽  
Ammara Mehmood ◽  
Muhammad Asif Zahoor Raja
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Prediction ◽  
Long Short Term Memory

A long short-term memory based wind power prediction method

2021 ◽  
Author(s):  
Yufeng Huang ◽  
Min Ding ◽  
Zhijian Fang ◽  
Qingyi Wang ◽  
Zhili Tan ◽  
...  
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Prediction Method ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Prediction ◽  
Long Short Term Memory

scholarly journals Support Vector Machine and Long Short-term Memory using Multivariate Models for Wind Power Forecasting

2020 ◽  
Vol 9 (4) ◽  
pp. 2364-2369
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Renewable energy has recently gained considerable attention. In particular, interest in wind energy is rapidly increasing globally. However, the characteristics of instability and volatility in wind energy systems also have a significant on power systems. To address these issues, numerous studies have been carried out to predict wind speed and power. Methods used to forecast wind energy are divided into three categories: physical, data-driven (statistical and artificial intelligence methods), and hybrid methods. In this study, among artificial intelligence methods, we compare short-term wind power using a support vector machine (SVM) and long short-term memory (LSTM). The method using an SVM is a short-term wind power forecast that considers the wind speed and direction on Jeju Island, whereas the method using LSTM does not consider the wind speed and direction. As the experiment results indicate, the SVM method achieves an excellent performance when considering the wind speed and direction.

scholarly journals Ultra-Short-Term Multistep Prediction of Wind Power Based on Representative Unit Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Mao Yang ◽  
Lei Liu ◽  
Yang Cui ◽  
Xin Su
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Power Grid ◽  
Support Vector ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction ◽  
Wide Range

With the continuous expansion of wind power grid scale, wind power prediction is an important means to reduce the adverse impact of large-scale grid integration on power grid: the higher prediction accuracy, the better safety, and economy of grid operation. The existing research shows that the quality of input sample data directly affects the accuracy of wind power prediction. By the analysis of measured power data in wind farms, this paper proposes an ultra-short-term multistep prediction model of wind power based on representative unit method, which can fully excavate data information and select reasonable data samples. It uses the similarity measure of time series in data mining, spectral clustering, and correlation coefficient to select the representative units. The least squares support vector machine (LSSVM) model is used as a prediction model for outputs of the representative units. The power of the whole wind farm is obtained by statistical upscaling method. And the number of representative units has a certain impact on prediction accuracy. The case study shows that this method can effectively improve the prediction accuracy, and it can be used as pretreatment method of data. It has a wide range of adaptability.

scholarly journals Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3227 ◽  
Author(s):  
Xiaoyu Shi ◽  
Xuewen Lei ◽  
Qiang Huang ◽  
Shengzhi Huang ◽  
Kun Ren ◽  
...  
Keyword(s):  
Wind Power ◽  
Wind Farm ◽  
Short Term Memory ◽  
Accurate Prediction ◽  
Variational Model ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Model Decomposition ◽  
Long Short Term Memory

A more accurate hourly prediction of day-ahead wind power can effectively reduce the uncertainty of wind power integration and improve the competitiveness of wind power in power auction markets. However, due to the inherent stochastic and intermittent nature of wind energy, it is very difficult to sharply improve the multi-step wind power forecasting (WPF) accuracy. According to theory of direct and recursive multi-step prediction, this study firstly proposes the models of R (recursive)-VMD (variational model decomposition)-LSTM (long short-term memory) and D (direct)-VMD-LSTM for the hourly forecast of day-ahead wind power by using a combination of a novel and in-depth neural network forecasting model called LSTM and the variational model decomposition (VMD) technique. The data from these model tests were obtained from two real-world wind power series from a wind farm located in Henan, China. The experimental results show that LSTM can achieve more precise predictions than traditional neural networks, and that VMD has a good self-adaptive ability to remove the stochastic volatility and retain more adequate data information than empirical mode decomposition (EMD). Secondly, the R-VMD-LSTM and D-VMD-LSTM are comparatively studied to analyze the accuracy of each step. The results verify the effectiveness of the combination of the two models: The R-VMD-LSTM model provides a more accurate prediction at the beginning of a day, while the D-VMD-LSTM model provides a more accurate prediction at the end of a day.

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