Application of Recurrence Plot to Wind Power Predictability Study under Different Sampling Intervals

2014 ◽  
Vol 672-674 ◽  
pp. 195-198
Author(s):  
Mao Yang ◽  
Yue Qi
Keyword(s):  
Wind Power ◽  
Recurrence Rate ◽  
Large Scale ◽  
Wind Farm ◽  
Prediction Method ◽  
Recurrence Plot ◽  
Sampling Interval ◽  
Power Prediction ◽  
Power Fluctuations ◽  
Sampling Intervals

The accurate prediction of wind power is an important way to improve the safety and economy of wind power system operation in large scale. On one side, the accuracy of wind power prediction depends on the characterized degree of accuracy for wind power fluctuations under the used prediction method. On the other side, it depends on the inherent degree of predictability in the characteristics of wind power fluctuations. This text will focus on the predictability of wind power, and use recurrence plot and recurrence rate to analyze it in the qualitative and quantitative view, and analyze change rule of recurrence plot and recurrence rate under different time scales. The real case of a wind farm has been analyzed to demonstrate that predictability of wind power will be poorer when the sampling interval becomes large.

Research of Aggregated Wind Power Prediction Method for a Region

2013 ◽  
Vol 860-863 ◽  
pp. 262-266
Author(s):  
Jin Yao Zhu ◽  
Jing Ru Yan ◽  
Xue Shen ◽  
Ran Li
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Prediction Method ◽  
Support Vector ◽  
Power Prediction ◽  
Power Characteristics ◽  
Wind Power Prediction ◽  
Grey Correlation Theory

Wind power is intermittent and volatility. Some new problems would arise to power system operation when Large-scale wind farm is connected with power systems. One of the most important effect is the influence on the grid dispatch. An aggregated wind power prediction method for a region is presented. By means of analyzing power characteristics and correlation, then the greater correlation is selected as model input. Based on grey correlation theory, a least squares support vector machine prediction model is established. Finally, this method is executed on a real case and integrated wind power prediction method can effectively improve the prediction accuracy and simplify the prediction step are proved.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

scholarly journals M2GSNet: Multi-Modal Multi-Task Graph Spatiotemporal Network for Ultra-Short-Term Wind Farm Cluster Power Prediction

2020 ◽  
Vol 10 (21) ◽  
pp. 7915
Author(s):  
Hang Fan ◽  
Xuemin Zhang ◽  
Shengwei Mei ◽  
Kunjin Chen ◽  
Xinyang Chen
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Farms ◽  
Task Graph ◽  
Probabilistic Prediction ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

Ultra-short-term wind power prediction is of great importance for the integration of renewable energy. It is the foundation of probabilistic prediction and even a slight increase in the prediction accuracy can exert significant improvement for the safe and economic operation of power systems. However, due to the complex spatiotemporal relationship and the intrinsic characteristic of nonlinear, randomness and intermittence, the prediction of regional wind farm clusters and each wind farm’s power is still a challenge. In this paper, a framework based on graph neural network and numerical weather prediction (NWP) is proposed for the ultra-short-term wind power prediction. First, the adjacent matrix of wind farms, which are regarded as the vertexes of a graph, is defined based on geographical distance. Second, two graph neural networks are designed to extract the spatiotemporal feature of historical wind power and NWP information separately. Then, these features are fused based on multi-modal learning. Third, to enhance the efficiency of prediction method, a multi-task learning method is adopted to extract the common feature of the regional wind farm cluster and it can output the prediction of each wind farm at the same time. The cases of a wind farm cluster located in Northeast China verified that the accuracy of a regional wind farm cluster power prediction is improved, and the time consumption increases slowly when the number of wind farms grows. The results indicate that this method has great potential to be used in large-scale wind farm clusters.

Short-Term Wind Power Forecasting Based on SVM with Backstepping Wind Speed of Power Curve

2012 ◽  
Vol 224 ◽  
pp. 401-405
Author(s):  
Xi Yun Yang ◽  
Peng Wei ◽  
Huan Liu ◽  
Bao Jun Sun
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Plants ◽  
Wind Farm ◽  
Prediction Method ◽  
Power Curve ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Wind Power Prediction

Accurate wind farm power prediction can relieve the disadvantageous impact of wind power plants on power systems and reduce the difficulty of the scheduling of power dispatching department. Improving accuracy of short-term wind speed prediction is the key of wind power prediction. The authors have studied the short-term wind power forecasting of power plants and proposed a model prediction method based on SVM with backstepping wind speed of power curve. In this method, the sequence of wind speed that is calculated according to the average power of the wind farm operating units and the scene of the power curve is the input of the SVM model. The results show that this method can meet the real-time needs of the prediction system, but also has better prediction accuracy, is a very valuable short-term wind power prediction method.

scholarly journals ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 701
Author(s):  
Honghai Niu ◽  
Yu Yang ◽  
Lingchao Zeng ◽  
Yiguo Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Prediction Interval ◽  
High Reliability ◽  
Prediction Method ◽  
Prediction Intervals ◽  
Probabilistic Prediction ◽  
Data Simulation ◽  
Weighting Coefficients

Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified.

scholarly journals Ultra-Short-Term Wind Power Prediction Based on Bidirectional Gated Recurrent Unit and Transfer Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Wenjin Chen ◽  
Weiwen Qi ◽  
Yu Li ◽  
Jun Zhang ◽  
Feng Zhu ◽  
...  
Keyword(s):  
Wind Power ◽  
Transfer Learning ◽  
Wind Farm ◽  
Meteorological Data ◽  
Prediction Method ◽  
Wind Farms ◽  
Power Prediction ◽  
Short Term ◽  
Training Time ◽  
Gated Recurrent Unit

Wind power forecasting (WPF) is imperative to the control and dispatch of the power grid. Firstly, an ultra-short-term prediction method based on multilayer bidirectional gated recurrent unit (Bi-GRU) and fully connected (FC) layer is proposed. The layers of Bi-GRU extract the temporal feature information of wind power and meteorological data, and the FC layer predicts wind power by changing dimensions to match the output vector. Furthermore, a transfer learning (TL) strategy is utilized to establish the prediction model of a target wind farm with fewer data and less training time based on the source wind farm. The proposed method is validated on two wind farms located in China and the results prove its superior prediction performance compared with other approaches.

Wind Power Prediction Based on Cloud Model and GMDH Two-Stage Optimization Approach

2014 ◽  
Vol 670-671 ◽  
pp. 1545-1549
Author(s):  
Zhong Kang Wei ◽  
Dun Nan Liu ◽  
Yu Jie Xu ◽  
Jing Xing ◽  
Yan Ling Du
Keyword(s):  
Wind Power ◽  
Atmospheric Pressure ◽  
Large Scale ◽  
Wind Farm ◽  
Cloud Model ◽  
Forecast Model ◽  
Optimization Approach ◽  
Security Risk ◽  
Power Prediction ◽  
Two Stage

Currently, large scale of wind power grid-tied has brought great security risk on the grid. Accurate forecast of wind power is of vital importance. In this paper, a forecast method based on cloud model and GMDH two-stage optimization approach is proposed. Firstly, association rules between wind speed and different atmospheric pressure are mined, cloud model inference method is applied to get the data of wind power speed at next time. Then, many forecast methods were applied to acquire power forecast data at next time, furthermore, combination forecast model based on GMDH is acquired. Through analyzing historical data, different atmospheric pressure and generation power of some wind farm in Jibei, it is verified that this model, having high practical value, can improve the accuracy of power prediction.

A Wind Power Prediction Method Based on RBF Neural Network

2015 ◽  
Vol 713-715 ◽  
pp. 1107-1110 ◽  
Author(s):  
Yue Ren Wang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Large Scale ◽  
Historical Data ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Power Prediction ◽  
Output Data ◽  
Wind Power Prediction ◽  
System Input

With the interconnection of the large-scale wind power, wind power forecasting is particularly important to the dispatcher of power grid. Based on the historical data, this paper proposes a prediction method based on RBF (radial basis function) neural network. This method is based on the model taking the influence of the system input (wind speed, wind direction, historical power output data) on the predicting error into consideration to get the optimal input values. Examples with field data obtained from Northwest of China show the effectiveness and higher precisionof the proposed method.

scholarly journals Ultra-Short-term Power Prediction of the Wind Farm Based on Multivariate Data Combination

2021 ◽  
Vol 898 (1) ◽  
pp. 012001
Author(s):  
Yong Jian ◽  
Zhong Li ◽  
Biao Li ◽  
Xuyuan Cao ◽  
Jiayuan Zhu
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Measurement Data ◽  
Power Prediction ◽  
Short Term ◽  
Actual Measurement ◽  
Grid Connection ◽  
Wind Power Prediction ◽  
Weighted Values

Abstract Accurate wind power prediction is an important way to promote large-scale wind power grid connection. First, to address the abnormal wind farm actual measurement data caused by wind abandonment and power limitation, the DBSCAN method is used to pre-process the wind farm actual measurement data and eliminate the abnormal data. Then, a short-term wind power prediction model with a combination of GA-LSSVM and ARIMA weights is established, and the Lagrange multiplier algorithm is used to obtain the weighted values of each single model in the combined model to further obtain the wind power prediction results. Finally, the effectiveness of the proposed method is verified by arithmetic examples, and the results show that the proposed model and method can effectively improve the prediction accuracy of short-term wind power.

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