Datasets divide information.

Ultra-short-term power load forecasting is beneficial to improve the economic efficiency of power systems and ensure the safe and stable operation of power grids. As the volatility and randomness of loads in power systems, make it difficult to achieve accurate and reliable power load forecasting, a sequence-to-sequence based learning framework is proposed to learn feature information in different dimensions synchronously. Convolutional Neural Networks(CNN) Combined with Bidirectional Long Short Term Memory(BiLSTM) Networks is constructed in the encoder to extract the correlated timing features embedded in external factors affecting power loads. The parallel BiLSTM network is constructed in the decoder to mine the power load timing information in different regions separately. The multi-headed attention mechanism is introduced to fuse the BiLSTM hidden layer state information in different components to further highlight the key information representation. The load forecastion results in different regions are output through the fully connected layer. The model proposed in this paper has the advantage of high forecastion accuracy through the example analysis of real power load data.

超短期电力负荷预测有助于提升电力系统的经济效益，保障电网安全稳定运行。鉴于电力系统中负荷兼具波动性与随机性，难以实现精准可靠的电力负荷预测，本文提出一种基于序列到序列（sequence-to-sequence）的学习框架，以同步学习多维度特征信息。编码器部分构建了卷积神经网络（Convolutional Neural Networks, CNN）与双向长短期记忆（Bidirectional Long Short Term Memory, BiLSTM）网络的组合结构，用于提取影响电力负荷的外部因素中蕴含的关联时序特征。解码器部分搭建并行BiLSTM网络，以分别挖掘不同区域的电力负荷时序信息。引入多头注意力机制，对不同组件中的BiLSTM隐藏层状态信息进行融合，进一步强化关键信息的表征能力。通过全连接层输出不同区域的负荷预测结果。经真实电力负荷数据的实例分析验证，本文提出的模型具备较高的预测精度优势。