基于最大熵混沌时间序列的支持向量机短期风速预测模型研究

SHORT TERM WIND SPEED PREDICTION MODEL BASED ON SUPPORT VECTOR MACHINE USING MAXIMUM ENTROPY OF CHAOTIC TIME SERIES

为了提高短期风速预测的精度,减小风力发电接入对电力系统的安全和稳定运行带来的影响,提出基于最大熵混沌时间序列的支持向量机短期风速预测模型。该模型将最大熵原理引入到混沌时间序列样本选择过程中,针对风速混沌时间序列建模,并采用贝叶斯框架下的最小二乘支持向量机对风速进行短期预测。仿真实验结果表明,该预测模型能有效提高短期风速预测的精度。

Wind speed and wind power forecasting are key measures to strengthen the management of integration of wind power. In order to improve the accuracy of short-term wind forecasting and reduce the impact of wind power access to safe and stable operation of the power system, a short term wind speed prediction model based on support vector machine using maximum entropy of chaotic time series is proposed. In this model, the principle of maximum entropy is introduced to the process of chaotic time series sample selection, and short-term wind speed prediction was conducted by using least squares support vector machine under the Bayesian framework based on the modeling of chaotic time series of wind speed. Simulation results showed that the proposed model can improve the accuracy of short term wind speed prediction effectively.