机械与电子2026，Vol.44Issue(2)：17-23,7.
融合日类型注意力的电力负荷短期预测方法研究
Research on Short-term Power Load Forecasting Method Integrating Day-type Attention
摘要
Abstract
To fully exploit the daily and weekly periodicity characteristics of power load,a short-term load forecasting method integrating a day-type attention mechanism is proposed.Firstly,the applicability of a multiple linear regression model was verified using linear regression prediction results of single-day power load,and the historical data were grouped accordingly.Secondly,an attention mechanism was intro-duced to highlight the contributions of different historical days to the target day,and a neural network-based forecasting model with integrated day-type attention was constructed.Finally,the proposed model was compared with multiple linear regression,and the Bidirectional Long Short-term Memory(BiLSTM)networks were applied to case studies.The results indicate that the proposed model outperforms the com-parative models in terms of Mean Absolute Percentage Error(MAPE),Normalized Root Mean Square Er-ror(NRMSE),and root mean square percentage error(RMSPE),demonstrating its effectiveness in predic-tion accuracy and stability.关键词
电力负荷预测/短期预测/注意力机制/深度学习/神经网络Key words
power load forecast/short-term forecast/attention mechanism/deep learning/neural net-work分类
信息技术与安全科学引用本文复制引用
张海静,冯延坤,肖楚鹏,许静,卢瑞,王奎..融合日类型注意力的电力负荷短期预测方法研究[J].机械与电子,2026,44(2):17-23,7.基金项目
国家电网有限公司科技项目(520633230001) （520633230001）
