首页|基于FNN-LSTM-Attention的短期电力负荷预测研究

基于FNN-LSTM-Attention的短期电力负荷预测研究

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为了充分挖掘数据潜在规律,解决电力负荷复杂性、非线性等预测难点,提出一种基于FNN-LSTM-Attention的混合预测模型.通过前馈神经网络(FNN)在时间维度上提取数据特征,得到不同特征,利用长短期记忆(LSTM)提取日期、温度等因素对负荷的影响,通过Self-Attention层进一步挖掘数据特征,输出预测值.以新疆某地区实际负荷数据为实例,对不同模型的预测误差进行分析与对比,结果显示,所提出的混合预测模型的预测误差较小,证明了所提模型的有效性.
Research on Short-term Power Load Forecasting Based on FNN-LSTM-Attention
In order to fully explore the potential patterns of data and overcome the forecasting difficulties such as complexity and nonlinearity of power load,this paper proposes a hybrid forecasting model based on FNN-LSTM-Attention.Data features are extracted in the time dimension through feedforward neural network(FNN),different features are obtained,and long short-term memory(LSTM)is used to extract the impact of factors such as date and temperature on load.The Self-Attention layer is used to further explore data features and output predicted values.Taken actual load data from a certain region in Xinjiang as an example,the forecasting errors of different models are analyzed and compared.The results show that the proposed hybrid fore-casting model has smaller forecasting errors,proving the effectiveness of the model.

deep learningpower load forecastinglong short-term memory networkSelf-Attention mechanism

薛文斌、穆晨宇、杜建城、穆羡瑛、田永明、邹德凡

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国网新疆电力有限公司乌鲁木齐供电公司,新疆,乌鲁木齐 830001

华北电力大学,电气与电子工程学院,北京 102206

深度学习 电力负荷预测 长短期记忆网络 自注意力机制

2024

微型电脑应用
上海市微型电脑应用学会

微型电脑应用

CSTPCD
影响因子:0.359
ISSN:1007-757X
年,卷(期):2024.40(12)