基于SSA-PSO-ANFIS的短期风速预测研究

针对风速具有强非线性的特点,提出一种奇异谱分析和改进粒子群优化自适应模糊推理系统的短期风速预测模型。该方法采用奇异谱分析将原始序列分解为趋势和谐波分量,对各分量分别建立模糊神经网络模型,最后将各分量预测结果叠加得到预测风速值。为提高预测精度,改用改进粒子群算法对自适应模糊推理系统的隶属度函数进行优化。以河北某风电场实测数据进行仿真并与传统的神经网络对比分析,结果表明将风速重构后分别预测再叠加降低了原始问题的复杂度,同时提高了预测精度,在不同时间间隔的风速序列预测中该模型显著降低了多步实时预测中的误差。

SHORT-TERM WIND SPEED PREDICTION BASED ON SSA-PSO-ANFIS

Aiming at the strong nonlinearity of wind speed,a short-term wind speed prediction model based on singular spectrum analysis and improved particle swarm optimization adaptive fuzzy inference system is proposed. The method uses singular spectrum analysis to decompose the original series into trend and harmonic components,then establishes a fuzzy neural network model for each component,and finally superimposes each component prediction result to obtain the next wind speed value. In order to improve the prediction accuracy,the improved particle swarm optimization algorithm is used to optimize the membership function of the adaptive fuzzy inference system. Simulation based on measured data from a wind farm in Hebei Province and comparison with traditional neural networks. The results show that the re-superimposition of the wind speed after re-construction reduces the complexity of the original problem and improves the prediction accuracy. This model significantly reduces errors in multi-step predictions in wind speed sequence predictions at different time intervals.