ACPSO-WFLN算法在短期风电功率预测中的应用

针对传统小波神经网络初始参数设定困难、容易陷入局部极值的问题,提出一种基于自适应混沌粒子群算法(ACPSO)优化的小波链神经网络(WFLN)。首先,将小波神经网络与随机矢量函数连接网络相融合,构建小波链神经网络,加强网络并行运算能力;其次,在粒子群算法中引入混沌优化因子与自适应权重系数,改善粒子群的早熟收敛问题,实现全局与局部寻优能力的动态平衡;最后,利用ACPSO算法优化WFLN神经网络,建立短期风电功率预测模型。实验结果表明:ACPSO-WFLN风电功率预测模型较其它网络明显减少隐层神经元数目与迭代步数,具有较高的预测精度。

Application of ACPSO-WFLN algorithm in short-term wind power forecasting

Concerning the problem that the traditional wavelet neural network is difficult to set initial parameters and easily fall into local extremum, this paper proposes a wavelet functional link neural network (WFLN) based on adaptive chaotic particle swarm optimization (ACPSO). Firstly, wavelet neural network and random vector functional link net are combined to construct wavelet neural network and strengthen parallel computing ability. Secondly, chaos optimization factors and adaptive weight coefficient are introduced into particle swarm optimization to improve premature convergence of particle swarm, and a dynamic balance between global and local search capabilities can be realized. Finally, optimize the WFLN neural network using the ACPSO algorithm, a short-term wind power forecasting model is established.The experimental results show that the ACPSO-WFLN wind power prediction model can significantly reduce the number of hidden neurons and the amount of iterations compared with other networks, and the root mean squared error is reduced to 0.0723, which has a higher prediction accuracy.