School of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China.
School of Electric Power Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China.
PLoS One. 2024 Mar 22;19(3):e0299632. doi: 10.1371/journal.pone.0299632. eCollection 2024.
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利于提高电力系统的经济效益，保证电网的安全稳定运行。由于电力系统中的负荷具有波动性和随机性，使得电力负荷的准确可靠预测变得困难，提出了一种基于序列到序列的学习框架，以同步学习不同维度的特征信息。在编码器中构建卷积神经网络（CNN）与双向长短期记忆（BiLSTM）网络相结合，提取嵌入在影响电力负荷的外部因素中的相关时间特征。在解码器中构建并行 BiLSTM 网络，分别挖掘不同区域的电力负荷时间信息。引入多头注意力机制融合不同分量的 BiLSTM 隐藏层状态信息，进一步突出关键信息表示。通过全连接层输出不同区域的负荷预测结果。通过对实际电力负荷数据的实例分析，证明了所提出的模型具有较高的预测精度优势。
