基于CNN-BiLSTM-Attention的超短期电力负荷预测

超短期电力负荷预测对电力系统的快速响应和实时调度至关重要，准确预测负荷能保障电力系统的安全并提高用电效率。为获得准确可靠的负荷预测结果，针对电网负荷数据非线性和时序性等特征，提出了一种基于CNN-BiLSTM-Attention(AC-BiLSTM)的新型超短期电力负荷预测方法。该方法首先将卷积神经网络(CNN)和双向长短期记忆(BiLSTM)网络相结合充分提取负荷数据本身的时空特征。然后引入注意力(Attention)机制自动为BiLSTM隐藏层状态分配相应的权重，以区分不同时间负荷序列的重要性，能够有效减少历史信息的丢失并突出关键历史时间点的信息。最后通过全连接层输出最终负荷预测结果。以某地区真实负荷数据为例进行了实验分析。通过两种实验场景对比，验证了该方法具有较高的预测精度，可以为电力系统规划和稳定运行提供可靠的依据。

Ultra-short-term power load forecasting based on CNN-BiLSTM-Attention

Ultra-short-term power load forecasting is crucial for rapid response and real-time dispatch in a power system. Accurate load forecasting ensures the safety of the power system and improves electricity efficiency. To obtain accurate and reliable load forecasting results, a new ultra-short-term power load forecasting method based on CNN-BiLSTM-Attention (AC-BiLSTM) is proposed for the characteristics of nonlinear and time-series nature of grid load data. First, a convolutional neural network (CNN) and bidirectional long and short-term memory (BiLSTM) networks are used to extract the spatio-temporal features of the load data. The attention mechanism automatically assigns corresponding weights to BiLSTM to distinguish the importance of different time load sequences. These can effectively reduce the loss of historical information and highlight the information of key historical time points. Finally, the final load prediction results are output through the fully connected layer. Taking the real load data of a certain area as an example, the comparison between two experimental scenarios proves that the proposed method has high prediction accuracy and can provide a reliable basis for power system planning and stable operation. This work is supported by the Science and Technology Planning Project of Henan Province (No. 212102210226).