基于深度学习的电力负荷预测研究

Research on Electricity Load Forecasting Based on Deep Learning

随着能源需求的不断增长和能源结构的不断调整,电力负荷预测成为电力系统运行和规划中的关键问题。传统的电力负荷预测方法往往受限于特征提取和模型建模的局限性,无法充分挖掘数据中的信息,因此导致预测精度不高。为了克服这些问题,采用基于深度学习的方法,旨在提高电力负荷预测的准确性和稳定性。通过实验验证,发现基于深度学习的电力负荷预测模型在不同时间尺度和预测周期下表现出较高的预测精度和稳定性。与传统方法相比,深度学习模型能够更好地捕捉数据中的非线性关系和时序特征,从而提高了预测效果。

With the continuous growth of energy demand and adjustment of energy structure,power load forecasting has become a key issue in power system operation and planning.Traditional power load forecasting methods are often limited by the limitations of feature extraction and model modelling,which cannot fully explore the information in the data,thus leading to poor forecasting accuracy.In order to overcome these problems,a deep learning-based approach is adopted with the aim of improving the accuracy and stability of power load forecasting.Through experimental validation,it is found that the deep learning-based power load prediction model exhibits high prediction accuracy and stability at different time scales and prediction periods.Compared with traditional methods,the deep learning model is able to better capture the nonlinear relationships and time-series features in the data,thus improving the prediction effect.