麻雀搜索算法优化BP神经网络的短期风功率预测  

BP neural networks optimized by sparrow search algorithm for short-term wind power prediction

作　　者：刘湲 王芳[1] LIU Yuan;WANG Fang(School of Electrical Engineering,Shanghai Dianji University,Shanghai 201306,China)

机构地区：[1]上海电机学院电气学院,上海201306

出　　处：《上海电机学院学报》2022年第3期132-136,共5页Journal of Shanghai Dianji University

摘　　要：传统BP神经网络存在收敛速度慢、易陷入局部最小值,以及对初始权值和阈值选择敏感等缺点。为了保证高效、准确的短期风功率预测,构建了一种基于麻雀搜索算法(SSA)优化BP(SSABP)神经网络的短期风功率预测模型。用该预测模型对我国沿海某风电场的历史数据进行仿真测试,并与其他模型的仿真测试结果进行比较。仿真结果表明:SSA-BP神经网络预测模型的精度较高。The traditional BP neural networks have disadvantages such as slow convergence speed,prone to local minima,and sensitivity to initial weights and thresholds.In order to ensure efficient and accurate short-term wind power prediction,a short-term wind power prediction model based on a BP neural network optimized by sparrow search algorithm(SSA)(SSA-BP)is constructed.The prediction model is used to simulate and test the historical data of a coastal wind farm in China,and compared with the simulation test results of other models.The simulation results show that the prediction model based on the SSA-BP neural network has higher accuracy.

关 键 词：麻雀搜索算法(SSA) BP神经网络 短期风功率预测 

分 类 号：TK8[动力工程及工程热物理—流体机械及工程]