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.

Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network

2014 ◽  
Vol 536-537 ◽  
pp. 470-475
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Prediction ◽  
Elman Neural Network ◽  
Wind Power Prediction ◽  
Forecasting Method ◽  
Safety And Stability

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

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 A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error

2021 ◽  
Vol 9 ◽  
Author(s):  
Haifeng Luo ◽  
Xun Dou ◽  
Rong Sun ◽  
Shengjun Wu
Keyword(s):  
Power Systems ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Method ◽  
Input Sequence ◽  
Wind Farms ◽  
Power Prediction ◽  
Cumulative Error ◽  
Wind Power Prediction ◽  
Accuracy And Stability

Wind power generation is likely to hinder the safe and stable operations of power systems for its irregularity, intermittency, and non-smoothness. Since wind power is continuously connected to power systems, the step length required for predicting wind power is increasingly extended, thereby causing an increasing cumulative error. Correcting the cumulative error to predict wind power in multi-step is an urgent problem that needs to be solved. In this study, a multi-step wind power prediction method was proposed by exploiting improved TCN to correct the cumulative error. First, multi-scale convolution (MSC) and self-attentiveness (SA) were adopted to optimize the problem that a single-scale convolution kernel of TCN is difficult to extract temporal and spatial features at different scales of the input sequence. The MSC-SA-TCN model was built to recognize and extract different features exhibited by the input sequence to improve the accuracy and stability of the single-step prediction of wind power. On that basis, the multi-channel time convolutional network with multiple input and multiple output codec technologies was adopted to build the nonlinear mapping between the output and input of the TCN multi-step prediction. The method improved the problem that a single TCN is difficult to tap the different nonlinear relationships between the multi-step prediction output and the fixed input. The MMED-TCN multi-step wind power prediction model was developed to separate linearity and nonlinearity between input and output to reduce the multi-step prediction error. An experimental comparative analysis was conducted based on the measured data from two wind farms in Shuangzitai, Liaoning, and Keqi, Inner Mongolia. As revealed from the results, the MAE and RMSE of the MMED-TCN-based multi-step prediction model achieved the cumulative mean values of 0.0737 and 0.1018. The MAE and RMSE metrics outperformed those of the VMD-AMS-TCN and MSC-SA-TCN models. It can be seen that the wind power prediction method proposed in this study could improve the feature extraction ability of TCN for input sequences and the ability of mining the mapping relationship between multiple inputs and multiple outputs. The method is superior in terms of the accuracy and stability of wind power prediction.

scholarly journals Research on Wind Power Prediction Algorithm Based on Fusion Model

2021 ◽  
Vol 898 (1) ◽  
pp. 012004
Author(s):  
Angru Li ◽  
Dechao Ma ◽  
Qi Liu ◽  
Kun Ji ◽  
Shaoliang Ling ◽  
...  
Keyword(s):  
Power Generation ◽  
Prediction Model ◽  
Wind Power ◽  
Wind Farm ◽  
Prediction Algorithm ◽  
Gradient Boosting ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
The Impact

Abstract Wind power generation is currently one of the most promising power generation technologies. It is particularly important to improve the prediction accuracy of wind power output, which can effectively reduce the impact on the grid when wind power is connected to the grid. Based on the fractal model, this paper integrates it with the wind power prediction model, and combines the custom K nearest neighbor algorithm to evaluate the prediction effect using multi-dimensional indicators. Finally, taking the data of a wind farm in northwest china as an example, compared it with the prediction model of random forest, support vector machine and gradient boosting decision tree prediction model to verify the effectiveness of the prediction algorithm in this paper.

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.

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.

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.

Wind power prediction method based on hybrid kernel function support vector machine

2017 ◽  
Vol 42 (3) ◽  
pp. 252-264 ◽  
Author(s):  
Zhongda Tian ◽  
Shujiang Li ◽  
Yanhong Wang ◽  
Xiangdong Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Kernel Function ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

The prediction accuracy of wind power affects the operation cost of the power grid, which is a direct result of the supply and demand balance of the grid. Therefore, how to improve the prediction accuracy of wind power is very important. Considering the prediction accuracy of current prediction methods is not high, a wind power prediction method based on a hybrid kernel function support vector machine is proposed. On the basis of the exhibited characteristics of different kernel functions, the hybrid kernel function is a linear combination of the radial basis function and the polynomial kernel function. The hybrid kernel function is selected as the kernel function of support vector machine. The global kernel function is used to fit the correlation of the distant sample data, while the partial kernel function is used to fit the correlation of the data in neighboring fields. The generalization performance of the support vector machine model is improved. At the same time, an improved particle swarm optimization algorithm is introduced to determine the optimal parameters of the hybrid kernel function and support vector machine prediction model. Finally, the built prediction model is used to predict the wind power. The simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

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