基于特性分析的LSTM神经网络短期电力需求预测方法研究

Research on short-term power demand forecasting method based on characteristic analysis of LSTM neural network

在进行短期电力需求预测研究工作中,由于短期电力需求受众多复杂的非线性因素影响,出现了特征考虑不充分和无法对时序相关性建模的问题。本文提出了一种基于特性分析的长短时记忆神经网络短期电力需求预测方法,根据电力需求特性分析挖掘影响因素,再采用线性回归和长短时记忆神经网络结合的网络模型对电力需求建模。通过特征工作挖掘了各个影响因素与最终结果基准量和敏感量的相关性,解决了特征表述能力不够强的问题。在神经网络建模时,采用遗忘门、输入门和输出门的结构设计,增强了模型对时序相关性的学习能力。本文选取某省2015年-2020年电力数据了验证提出的方法,得到了精度较高的预测结果,证明了本文提出的方法对能够提升建模效果,提高短期电力需求预测精度。

In the short-term power demand forecasting research work,due to the short-term power demand being affected by many complex non-linear factors,the problems of insufficient feature consideration and the inability to model the time series correlation have appeared.This paper proposes a short-term power demand forecasting method based on characteristic analysis of long and short-term memory neural network.According to the characteristics of power demand,the influencing factors are analyzed and mined,and then the network model combining linear regression and long short-term memory neural network is used to model the power demand.Through the feature engineering,the correlation between each influencing factor and the final result benchmark and sensitive quantity is mined,and the problem that the feature expression ability is not strong enough is solved.In the neural network modeling,the structure design of forgetting gate,input gate and output gate is adopted to enhance the learning ability of the model to the sequence correlation.In this paper,we choose the 2015-2020 power data of a province to verify the proposed method,and obtain higher precision forecasting results,which proves that the proposed method can improve the modeling results and improve the accuracy of short-term power demand forecasting.