制冷与空调(四川)2024,Vol.38Issue(6) :776-781,790.

基于LSTM+Attention模型的区域用电负荷增长预测方法

A Regional Electricity Load Growth Prediction Method Based on LSTM+Attention Model

罗晓冬 辜小琢 方煜 杜萍 陈丽娟 王滢桦 卢海明
制冷与空调(四川)2024,Vol.38Issue(6) :776-781,790.

基于LSTM+Attention模型的区域用电负荷增长预测方法

A Regional Electricity Load Growth Prediction Method Based on LSTM+Attention Model

罗晓冬 1辜小琢 1方煜 1杜萍 1陈丽娟 1王滢桦 1卢海明1
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作者信息

  • 1. 广东电网有限责任公司汕头供电局 汕头 515041
  • 折叠

摘要

在制冷、空调系统中,用电量受外界温度、建筑保温及室内人员活动等多种因素影响,形成复杂的用电依赖网络.若仅关注用电负荷增长值而忽视这些依赖关系,将显著增大预测负荷的损失.因此,提出基于LSTM+Attention模型的区域用电负荷增长预测方法.拟合分析区域的历史用电负荷数据,结合用电依赖性残差值的计算,分析用电负荷增长的周期性特征,引入LSTM+Attention模型识别用电负荷的影响因子特征,通过缩放线性回归方程,得到预测区域用电负荷增长值结果.实验结果表明:所提方法应用后得出的预测结果,表现出的预测负荷损失较小,预测准确度较高,满足了区域供电的电力调度决策需求.

Abstract

In refrigeration and air conditioning systems,electricity consumption is influenced by various factors such as external temperature,building insulation,and indoor personnel activities,forming a complex electricity dependency network. If we only focus on the growth value of electricity load and ignore these dependency relationships,it will significantly increase the loss of predicted load. Therefore,a regional electricity load growth prediction method based on LSTM+Attention model is proposed. Fit the historical electricity load data of the analysis area,combined with the calculation of electricity dependency residual value,analyze the periodic characteristics of electricity load growth,introduce LSTM+Attention model to identify the influencing factor characteristics of electricity load,and obtain the predicted regional electricity load growth value by scaling the linear regression equation. The experimental results show that the prediction results obtained after the application of the proposed method exhibit small load loss and high prediction accuracy,meeting the power dispatch decision-making needs of regional power supply.

关键词

区域用电/用电负荷/用电负荷增长/负荷增长预测/LSTM+Attention模型/预测方法

Key words

Regional electricity consumption/Electricity load/Electricity load growth/Load growth forecast/LSTM+Attention model/Prediction methods

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出版年

2024
制冷与空调(四川)
四川省制冷学会 西南交通大学

制冷与空调(四川)

CSTPCD
影响因子:0.475
ISSN:1671-6612
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