基于提升小波和改进PSO-Elman神经网络的短期负荷预测

为了提高电力负荷预测的精度,提出基于提升小波和改进PSO-Elman神经网络的短期负荷预测模型。首先,针对负荷的波动性和趋势性,将提升小波算法用于分解原始负荷数据并提取其主要特征,然后,在蚁群算法改进粒子群算法(GPSO)中,采用混沌理论,对部分适应度值较差的粒子进行混沌扰动,提出CGPSO算法,改善细致搜索的准确性,并提高全局搜索能力,将CGPSO算法用于Elman神经网络初始参数优化,最后建立负荷预测模型。本文采用我国北方某地区的实际数据进行仿真,实验结果表明,该方法的预测精度相比于传统ENN方法提高了2.3626%。

Short-term load forecasting based on lifting wavelet and improved PSO-Elman neural network

In order to improve the accuracy of power load forecasting, a short-term load forecasting model based on lifting wavelet and improved PSO-Elman neural network is proposed. Firstly, aiming at the fluctuation and trend of load, the lifting wavelet algorithm is used to decompose the original load data and extract its main features. Then, in the improved particle swarm optimization (GPSO) algorithm of ant colony algorithm, chaos theory is used to disturb some particles with poor fitness. The CGPSO algorithm is proposed to improve meticulousness. The accuracy of the search and the ability of global search are improved. The CGPSO algorithm is used to optimize the initial parameters of Elman neural network. Finally, the load forecasting model is established. In this paper, the actual data of a certain area in northern China are used to simulate. The experimental results show that the prediction accuracy of this method is 2.362 6% higher than that of the traditional ENN method.