Short-term wind speed prediction based on the wavelet transformation and Adaboost neural network

2017 6th International Conference on Power Science and Engineering

Hai, Zhou^1,2 ; Xiang, Zhu^1 ; Haijian, Shao^3 ; Ji, Wu^1

China Electric Power Research Institute, No.8 NARI Road, Nanjing, China^1

State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems, China Electric Power Research Institute, No.8 NARI Road, Nanjing, China^2

Southeast University, Nanjing, China^3

关键词: Experimental evaluation;    Forecasting accuracy;    Forecasting methods;    Frequency characteristic;    Multi-scale Decomposition;    Short-term wind speed predictions;    Wavelet transformations;    Wind speed forecasting;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/136/1/012008/pdf
DOI  :  10.1088/1755-1315/136/1/012008

来源: IOP

【 摘 要 】

The operation of the power grid will be affected inevitably with the increasing scale of wind farm due to the inherent randomness and uncertainty, so the accurate wind speed forecasting is critical for the stability of the grid operation. Typically, the traditional forecasting method does not take into account the frequency characteristics of wind speed, which cannot reflect the nature of the wind speed signal changes result from the low generality ability of the model structure. AdaBoost neural network in combination with the multi-resolution and multi-scale decomposition of wind speed is proposed to design the model structure in order to improve the forecasting accuracy and generality ability. The experimental evaluation using the data from a real wind farm in Jiangsu province is given to demonstrate the proposed strategy can improve the robust and accuracy of the forecasted variable.