Accurate integrated energy load forecasting is a crucial prerequisite for energy scheduling and strategy formulation in integrated community energy systems. However, the complex interrelationships among multiple loads within the integrated community energy system often hinder the improvement of load forecasting accuracy. To address the issue of high volatility in forecasting caused by the deep coupling of load relationships, a short-term load forecasting method for integrated community energy systems based on Spatio-Temporal Graph Convolutional Neural Network (STGCN) is proposed. Firstly, an integrated energy node clustering method is proposed, considering load fluctuation characteristics to address the error superposition problem caused by an excessive number of load nodes. Similar load nodes are gathered to reduce random errors in cooling and heating loads. Secondly, we design a dynamic adjacency matrix construction method based on load bias correlation to address situations where multiple load correlations influence each other. Bias correlation is utilized for the dynamic update of the matrix, ensuring accurate load correlations. Furthermore, we construct an STGCN-based integrated energy load forecasting model to mitigate short-term load forecasting fluctuations and identify different periodic patterns for distinct loads. The model incorporates multi-scale convolution kernels to capture integrated energy local features, enhancing feature representation and improving load forecasting accuracy. The proposed method is tested and verified using a real integrated community energy system dataset, showing higher prediction accuracy. Specifically, when compared to the current mainstream MTL-LSTM model, the proposed method predicts an 18.1 % increase in the MAPE index.

中文翻译：

---

基于STGCN的综合社区能源系统短期负荷预测方法

准确的综合能源负荷预测是综合社区能源系统能源调度和策略制定的重要前提。然而，综合社区能源系统中多个负荷之间复杂的相互关系往往阻碍负荷预测精度的提高。针对负荷关系深度耦合导致的预测波动较大的问题，提出一种基于时空图卷积神经网络（STGCN）的综合社区能源系统短期负荷预测方法。首先，考虑负荷波动特性，提出综合能源节点聚类方法，解决负荷节点数量过多带来的误差叠加问题。聚集相似的负载节点以减少冷热负载的随机误差。其次，我们设计了一种基于负载偏差相关性的动态邻接矩阵构造方法，以解决多个负载相关性相互影响的情况。利用偏差相关性进行矩阵的动态更新，保证负载相关性的准确。此外，我们构建了一个基于 STGCN 的综合能源负荷预测模型，以减轻短期负荷预测波动并识别不同负荷的不同周期模式。该模型采用多尺度卷积核来捕获综合能源局部特征，增强特征表示并提高负荷预测精度。使用真实的综合社区能源系统数据集对所提出的方法进行了测试和验证，显示出更高的预测精度。具体来说，与当前主流的MTL-LSTM模型相比，该方法预测MAPE指数提高了18.1%。