计算机与数字工程2024，Vol.52Issue(1)：219-222,4.DOI:10.3969/j.issn.1672-9722.2024.01.036
基于深度学习的电网短期负荷预测
Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Grid
赵从杰 1潘文林2
作者信息
- 1. 云南民族大学电气信息工程学院 昆明 650500
- 2. 云南民族大学数学与计算机科学学院 昆明 650500
- 折叠
摘要
Abstract
Aiming at the problem of insufficient short-term load forecasting accuracy of microgrid,this paper proposes a load forecasting method based on bidirectional long short-term memory(BI-LSTM)deep learning.The parameters affecting the forma-tion of household and commercial load distributions are used as input variables,and the total household and commercial load distri-bution of the grid is taken as the target.The input variables are used to train the BI-STM network.By identifying the consumption patterns of the grid,the grid load on-going is forecasted.Correlation coefficient(R),mean square error(MSE)and root mean square error(RMSE)and other performance evaluation indicators are used to analyze the prediction results.The results show that the BI-LSTM method has a higher correlation coefficient.关键词
电网/深度学习/短期负荷预测Key words
power grid/deep learning/short-term load forecasting分类
信息技术与安全科学引用本文复制引用
赵从杰,潘文林..基于深度学习的电网短期负荷预测[J].计算机与数字工程,2024,52(1):219-222,4.
