粒子群优化小波包回声状态网的短期电力负荷预测

精确的短期电力负荷预测是电力生产优化调度和安全稳定运行的重要保证,是智能电网建设的重要一环。为提高模型的预测精度,提出了一种基于粒子群优化小波包回声状态神经网络的短期电力负荷预测方法。首先利用多分辨率分析小波包分解理论对负荷数据进行分解和重构,建立小波包回声状态网预测模型；然后,利用粒子群算法对预测模型储备池中的参数进行优化。实验结果表明：针对短期电力负荷动态时间序列数据,与BP、Elman、传统ESN等网络相比,PSO-WPESN网络的预测精度、稳定性和泛化能力都得到明显增强,尤其是能在一定程度上缓解由于输出矩阵过大造成ESN存在病态解的弊端。

Short-term load forecast based on wavelet packet echo state network optimized by particle swarm optimization

Accurate short-term power load forecasting is an important guarantee for power production scheduling and safe and stable operation. It is also an important part in the construction of smart grid. In order to improve the prediction accuracy of the model, a new wavelet packet echo state network optimized by particle swarm optimization is proposed in this paper to predict the short-term power load. Firstly, the load data is decomposed and reconstructed by wavelet packet theory, and wavelet packet echo state network prediction model is established. Then, the prediction model parameters of dynamic neurons reservoir is optimized by particle swarm optimization algorithm. The results show that the forecasting accuracy, stability and generalization ability of PSO-WPESN have been significantly enhanced, compared with BP, Elman, traditional ESN, especially eases ESN disadvantages caused by excessive sick solution.