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.

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.

Combined Prediction of Wind Power Using Multi-Dimension Embedding Phase Space

2013 ◽  
Vol 321-324 ◽  
pp. 838-841
Author(s):  
Qiang Wang ◽  
Yang Yang
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Wind Power ◽  
Prediction Error ◽  
Wind Farm ◽  
Forecast Accuracy ◽  
Power Prediction ◽  
Combined Model ◽  
Wind Power Prediction ◽  
Network Method

In order to diminish the effect of reconstructed parameters to prediction of chaotic, a combined model for wind power prediction based on multi-dimension embedding is proposed. The combined model makes use of neural network method to achieve combination of several neural networks models based on phase space reconstruction, which can synthesize information and fuse prediction deviation in different embedding dimension, resulting in forecast accuracy improved. Simulation is performed to the real power time series Fujin wind farm. The results show that the combined prediction model is effective, and the prediction error of neural network combination is less than 7%.

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.

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.

Application and Contrast Analysis of BP and RBF Neural Network in Short-Term Wind Power Prediction

2014 ◽  
Vol 492 ◽  
pp. 544-549 ◽  
Author(s):  
Wen Hua Li ◽  
Qian Xiao ◽  
Jin Long Liu ◽  
Hui Qiao Liu
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Farm ◽  
Rbf Neural Network ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Contrast Analysis ◽  
Bp Model ◽  
Better Than

Wind power prediction is very important to maintain the power balance and economic operation of power system. The BP and RBF neural network were respectively used to predict one wind turbines’ output power, in 4 hours, on a wind farm in Shandong Province. The results show that the BP model, with 6-13-1 net structure and considering the meteorological factors, exhibits the best prediction accuracy (MAPE is 3.59%, NRMSE is 1.58%). The most important factor in the meteorological information for power prediction is temperature, followed by air pressure, relative humidity finally. BP model is slightly better than RBF model, but the latter is much better in the learning speed and stability. Dynamic-BP neural network, combined with the dynamical weight adjustment method, is better than BP neural network in solving the weight problem. These methods are feasible to the wind power prediction.

Multi-Scenario Based Generation Scheduling of Power System with Wind Farms

2013 ◽  
Vol 385-386 ◽  
pp. 1040-1044
Author(s):  
Lei Dong ◽  
Jian Kang Yang ◽  
Tian Jiao Pu ◽  
Hai Ming Zhou
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Prediction Error ◽  
Wind Farm ◽  
Wind Farms ◽  
Adjustment Costs ◽  
Environmental Benefit ◽  
Power Prediction ◽  
Power Fluctuation

Wind power penetration to power systems is increasing rapidly in the recent years due to its environmental benefit, while wind power fluctuation also brings some problems to power system operation which impacts the generation of conventional power units. For this reason, probabilistic optimal dispatching model based on multi-scenasio is developed in this paper. With the discretization of wind power fluctuations range, the scenario probability can be get by discretizing wind power prediction error distribution curves, at the same time considering the relevance of the prediction error of the adjacent periods in the time scale. By means of leading probability adjustment costs into objective function, the optimization result can consider the cost due to the fluctuation of wind power. The rationality and effectiveness of the proposed method is verified by testing and demonstrating IEEE-39 bus system with a wind farm.

Research on Wind Power Prediction by Combining Mesoscale Numerical Model with Neural Network Model

2012 ◽  
Vol 512-515 ◽  
pp. 771-777
Author(s):  
Feng Ming Yu ◽  
Xi Cang Li ◽  
Jin Hua Song ◽  
Chun Xiang Gao ◽  
Chun Long Jiang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Correlation Coefficient ◽  
Bp Neural Network ◽  
Wind Farm ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Relative Root

Effective wind power prediction on wind farm can not only guarantee safe operation of wind farm, but also increase wind power storage and utilization efficiency. This research combines mesoscale numerical weather prediction model with BP neural network model for the use of wind power prediction. WRF model is used to recalculate the meteorological elements of trial wind farm from Jun. 2008 to Jun. 2009, and the accuracy check result shows that the correlation coefficient between predicted value and corresponding measured value of wind speed reaches 0.72. Predictions accuracy of wind direction, air temperature, humidity and air pressure are also precise, which meets the requirement of building BP neural network prediction model. The BP neural network prediction models of output power of 40 wind turbines are established on trial wind farm one by one, to analyze the influence of data normalization method and neuron number at the hidden layer on prediction accuracy. The prediction test every 10 minutes, with the actual effect of 24 hours, is done for 26 days, and prediction accuracy test is conducted by using independent samples. The result shows that relative root mean square error of the output power of the single wind turbine from 24.8% to 32.6%, and the correlation coefficient between predicted value and measured value is from 0.45 to 0.68; relative root mean square error of the whole wind farm is 21.5%, and the correlation coefficient between predicted value and measured value is 0.74.

Sign in / Sign up

Export Citation Format

Share Document