改进神经网络下的电力负荷短期预测方法

Short-Term Forecasting Method for Power Load Under Improved Neural Networks

随着我国电力供给压力的逐步增大,电力负荷短期预测工作变得尤为重要。其不仅可为电力调度工作提供一定参考,还能正确指导电价拟定。基于此,以神经网络的特点为切入点,分析改进神经网络下的电力负荷短期预测方法,并对RNN、LSTM、GRU进行实验对比分析,旨在证明通过改进神经网络,可有效提高电力负荷短期预测精度,为电力部门决策提供一定参考。

With the gradual increase of power supply pressure in our country,short-term forecasting of power load is extremely important.It can not only provide certain reference for power dispatching work,but also guide the formulation of electricity prices correctly.Based on this,taking the characteristics of neural networks as the starting point,this paper analyzes and improves the short-term power load forecasting method under neural networks,and conducts experimental comparative analysis on RNN,LSTM,and GRU,aiming to prove that improving neural networks can effectively improve the accuracy of short-term power load forecasting and provide certain reference for decision-making in the power sector.