基于SEResNet-BiLSTM网络的综合能源负荷预测方法

Integrated energy system load forecasting based on SEResNet-BiLSTM network

中文关键词: 综合能源系统  多能源负荷预测  残差网络  双向长短期记忆网络  多任务学习

英文关键词: integrated energy system  multi- energy load forecasting  residual network  bidirectional long short-term memory network  multi-task learning

中文摘要:

      准确预测多能源负荷对综合能源系统的优化调度和经济运行至关重要。针对区域综合能源系统随机性强和多能源耦合关系等特点，提出一种基于SEResNet-BiLSTM网络和注意力机制的多任务短期负荷预测模型。首先，采用压缩与激励网络与残差网络相结合的模型（squeeze-and-excitation networks-residual network，SEResNet）作为高维特征提取单元，挖掘多能源之间的耦合关系，实现多能源负荷数据的高维特征提取；然后，利用双向长短期记忆网络捕获数据之间的时序特征，实现对负荷数据的预测；通过硬权重共享的方式实现多任务负荷学习，从而实现多元负荷预测。最后，通过仿真实验验证了所提方法的有效性，所提出的方法在精度方面相比于其他模型有明显的提升。

英文摘要:

      Accurate prediction of multi-energy load is crucial for the optimal scheduling and economic operation of integrated energy systems（IES）. Aiming at the strong randomness of regional IES and the coupling relationship between multi-energy sources, a multi-task short-term load prediction model based on SEResNet-BiLSTM network and attention mechanism is proposed. Firstly, the model of squeezeand-excitation networks-residual network（SEResNet）is used as the high-dimensional feature extraction unit to mine the coupling relationship between multiple energy sources. The high-dimensional feature extraction of multi-energy load data is realized. Then, bidirectional long short-term memory（BiLSTM）network is used to capture the time series characteristics between data to realize the prediction of load data. Multi-task load learning is realized by hard weight sharing to realize multivariate load forecasting. Finally, the effectiveness of proposed method is verified by simulation experiments, and the accuracy of the proposed method is significantly improved compared with other models.