—Accurate multi-energy load forecasting (MELF) is the key to realize the balance between supply and demand in regional integrated energy systems (RIES). To this end, a hybrid MELF method for RIES considering temporal dynamic and coupling characteristics (MELF_TDCC) is proposed. The novelty of MELF_TDCC lies in the following three aspects: 1) considering the high-dimensional temporal dynamic characteristic, an encoder-decoder model based on long-short term memory network (LSTMED) is proposed, which can extract the high dimensional potential feature, and reflect the temporal dynamic characteristics of historical load sequence effectively; 2) considering the cross-coupling characteristic, a coupling feature matrix of multi-energy load is constructed, which reflects the cross-influence of electricity, cooling and heating loads; 3) with the feature fusion layer of the hybrid model being built by gradient boosting decision tree (GBDT), the extended feature matrix for each class of load is constructed considering the intra-class inherent characteristics and inter-class coupling characteristic of loads, and the GBDT model is trained on the extended feature matrix, which provides multi-dimensional perspective for researching load essential characteristics. MELF_TDCC is verified on the ultra-short-term and short-term MELF scenarios based on an actual dataset. The simulation result shows that the proposed MELF_TDCC outperforms the current advanced methods.

中文翻译：

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考虑时间动态和耦合特性的区域综合能源系统多能源负荷预测

——准确的多能源负荷预测（MELF）是实现区域综合能源系统（RIES）供需平衡的关键。为此，提出了一种考虑时间动态和耦合特性的 RIES 混合 MELF 方法（MELF_TDCC）。MELF_TDCC的新颖之处在于以下三个方面：1）考虑到高维时间动态特性，提出了一种基于长短期记忆网络（LSTMED）的编码器-解码器模型，可以提取高维潜在特征，有效反映历史负荷序列的时间动态特征；2）考虑交叉耦合特性，构建多能负荷耦合特征矩阵，反映电力、冷热负荷交叉影响；3）通过梯度提升决策树（GBDT）构建混合模型的特征融合层，考虑负载的类内固有特性和类间耦合特性，构建每类负载的扩展特征矩阵，以及GBDT模型在扩展特征矩阵上进行训练，为研究负载本质特征提供了多维视角。MELF_TDCC 在基于实际数据集的超短期和短期 MELF 场景中进行验证。仿真结果表明，所提出的 MELF_TDCC 优于当前的先进方法。考虑负载的类内固有特性和类间耦合特性，构建各类负载的扩展特征矩阵，并在扩展特征矩阵上训练GBDT模型，为研究负载本质特征提供多维视角. MELF_TDCC 在基于实际数据集的超短期和短期 MELF 场景中进行验证。仿真结果表明，所提出的 MELF_TDCC 优于当前的先进方法。考虑负载的类内固有特性和类间耦合特性，构建各类负载的扩展特征矩阵，并在扩展特征矩阵上训练GBDT模型，为研究负载本质特征提供多维视角. MELF_TDCC 在基于实际数据集的超短期和短期 MELF 场景中进行验证。仿真结果表明，所提出的 MELF_TDCC 优于当前的先进方法。