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Short Term wind Power forecasting Using Autoregressive Integrated Moving Average Approach

Journal of Energy and Power Engineering 2013年第11期7卷 2089-2095页

作者： Almoataz Youssef Abdelaziz Mohamed Abdel Rahman Mohammed Mostafa El-Khayat Electrical Power and Machines Department Faculty of Engineering Ain Shams University New and Renewable Energy Authority

wind energy is one of the most promising electricity generating sources as a clean and free alternate compared with the conventional power plants and due to the volatility feature in the wind speeds it will reflect some problems in power systems reliability particularly if the system is deeply penetrated by wind farms. Therefore, wind power forecasting issue become and is still an important scope that will help in ED （economic dispatch）, UC （unit commitment） purposes to get more reliable and economic systems. This paper introduces short term wind power forecasting model, based on ARIMA （autoregressive integrated moving average） which will be applied to hourly wind data from Zaafarana 5 project in Egypt. The proposed model successfully outperforms the persistence model with significant improvement up to 6 h ahead.

关键词： wind forecasting time series analysis ARIMA Box-Jenkins model.

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Short-Term wind Power Prediction Based on ICEEMDAN-SE-LSTM Neural Network Model with Classifying Seasonal

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Energy Engineering 2023年第12期120卷 2761-2782页

作者： Shumin Sun Peng Yu Jiawei Xing Yan Cheng Song Yang Qian Ai State Grid Shandong Electric Power Research Institute Jinan250002China Key Laboratory of Control of Power Transmission and Conversion Shanghai Jiao Tong UniversityShanghai200240China

wind power prediction is very important for the economic dispatching of power systems containing wind *** this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and(long short-term memory)LSTM neural network is proposed and ***,the original data is prepossessed including removing outliers and filling in the ***,the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast *** addition,this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different *** this basis,sample entropy is used to calculate the complexity of each component and reconstruct them into trend components,oscillation components,and random ***,these three components are input into the LSTM neural network,*** with the predicted values of the three components,the overall power prediction results are *** simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57%to 9.46%than other traditional models,which indicates the reliability and effectiveness of the proposed method for power prediction.

关键词： wind forecasting ICEEMDAN long short-term memory seasonal classification sample entropy

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