基于改进的遗传算法优化BP神经网络的电力负荷预测

BP neural network based on improved genetic algorithm optimization of power load forecasting

本文通过对BP神经网络在负荷预测方面收敛的速率过于慢、易于陷入局部最优解等问题。提出用改进的遗传算法去优化BP神经网络的阈值、初始权值以及加入的平滑因子。提高BP神经网络的自适应学习率和附加动量,提高搜索的效率。该模型具有较好的收敛速率和全局空间搜索能力。为了验证改进的(Im-GA)-BP模型预测的合理性,通过对某区域的电力负荷预测分析。此方法的预测精度比一般的BP算法效率高。

In this paper,the convergence rate of BP neural network in load forecasting is too slow and it is easy to fall into the local optimal solution.An improved genetic algorithm is proposed to optimize the threshold,initial weight and added smoothing factor of BP neural network.At the same time,improve the BP neural network adaptive learning rate and additional Improve the efficiency of search.The model has good convergence rate and global spatial search ability.In order to verify the rationality of the improved(IM-GA)-BP model,the power load prediction analysis of a certain area was carried out.The prediction accuracy of this method is higher than that of the general BP algorithm.