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Research on the IL-Bagging-DHKELM short-term wind power prediction Algorithm Based on Error AP Clustering Analysis

Computers, Materials & Continua 2024年第6期79卷 5017-5030页

作者： Jing Gao Mingxuan Ji Hongjiang Wang Zhongxiao Du School of Electric power Shenyang Institute of EngineeringShenyang100083China

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is *** the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is *** algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM ***,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training ***,the correlation between wind power prediction errors and Numerical Weather prediction(NWP)data is introduced as incremental updates to the initial wind power prediction *** the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power *** examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration *** accuracy and precision of wind power generation prediction are effectively improved through the method.

关键词： short-term wind power prediction deep hybrid kernel extreme learning machine incremental learning error clustering

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A physical approach of the short-term wind power prediction based on CFD pre-calculated flow fields

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Journal of Hydrodynamics 2013年第1期25卷 56-61页

作者： LI Li LIU Yong-qian YANG Yong-ping HAN Shuang WANG Yi-mei State Key Laboratory of Alternate Electrical power System with Renewable Energy Sources North China Electric Power University School of Energy and power Engineering Shandong University

A physical approach of the wind power prediction based on the CFD pre-calculated flow fields is proposed in this paper. The flow fields are obtained based on a steady CFD model with the discrete inflow wind conditions as the boundary conditions, and a database is established containing the important parameters including the inflow wind conditions, the flow fields and the corresponding wind power for each wind turbine. The power is predicted via the database by taking the Numerical Weather prediction （NWP） wind as the input data. In order to evaluate the approach, the short-term wind power prediction for an actual wind farm is conducted as an example during the period of the year 2010. Compared with the measured power, the predicted results enjoy a high accuracy with the annual Root Mean Square Error （RMSE） of 15.2% and the annual MAE of 10.80%. A good performance is shown in predicting the wind power＇s changing trend. This approach is independent of the historical data and can be widely used for all kinds of wind farms including the newly-built wind farms. At the same time, it does not take much computation time while it captures the local air flows more precisely by the CFD model. So it is especially practical for engineering projects.

关键词： short-term wind power prediction physical approach CFD model flow field database

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short-term wind power prediction Based on RF-VMD-ELM

Short-Term Wind Power Prediction Based on RF-VMD-ELM

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第31届中国过程控制会议（CPCC2020）

作者： DONG Jiayuan TANG Zhenhao CAO Shengxian BU Bing HAN Hongzhi School of Automation Engineering Northest Electric Power University Guodian Hefeng wind power Development Co. Ltd.

Due to the time variability of wind speed and the influence of meteorological factors, the output power of wind turbines fluctuates, which poses challenges to the safe operation of power systems. Therefore, how to accurately predict short-term wind power has become the primary issue. For short-term wind power modeling, an extreme learning machine prediction model is proposed based on random forest feature selection and variational mode decomposition feature *** the first part, the wind power is used as the reference variable, then the random forest algorithm is utilized to filter and to select the input variables to be exploited in the feature extraction part. Next, the input variables are subjected to variational mode decomposition and the main frequency information is extracted as the final input variable of the prediction model. In the second part, an extreme learning machine is utilized to establish a prediction model with the use of input variables processed above, then the prediction performance of the model is evaluated. It can be realized form the experimental results that the proposed short-term wind power forecasting model has good forecasting accuracy compared with other models.

关键词： short-term wind power prediction variational mode decomposition random forest extreme learning machine

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