计算机科学 ›› 2019, Vol. 46 ›› Issue (6): 49-54.doi: 10.11896/j.issn.1002-137X.2019.06.006
张洋1, 姬波1,2, 卢红星1,2, 娄铮铮1
ZHANG Yang1, JI Bo1,2, LU Hong-xing1,2, LOU Zheng-zheng1
摘要: 传统模型在短期高压负荷电流预测中难以同时解决负荷电流数据的非线性和时间相关性问题。针对此问题,提出一种基于长短期记忆(LSTM)循环神经网络的短期高压负荷电流回归预测方法SHCP-LSTM。该方法引入自循环权重,使细胞彼此循环连接,可以动态改变累积的时间尺度,使其具有长短期记忆功能;使用遗忘门来控制输入和输出,从而使得门控单元具有sigmoid非线性。实验结果验证了该方法的可行性和有效性,与线性逻辑回归算法LR和机器学习算法ANN神经网络、BPNN神经网络预测相比,SHCP-LSTM收敛速度更快,且精确度更高。
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