摘要：Energy consumption forecasting is essential for energy system integration and management. However, existing studies mainly focus on temporal features of energy consumption, which neglects the spatial correlation of variables with time information. Capturing the spatio-temporal relationships helps to improve forecasting accuracy and further promote energy dispatch. To tackle this problem, an explainable Convolutional Neural Network-Long Short Term Memory forecasting model is employed to effectively predict the total energy consumption by capturing the spatial and temporal features of multivariate time series. In the model, the autoencoder is used to achieve the nonlinear dimensionality reduction and transfer the data to a low-dimensional space. Furthermore, a Convolutional Neural Network is used to extract more effective features from the decoded data, and long short-term memory is employed to identify the temporal dependencies between extracted features and total energy consumption. Shapley additive explanation is introduced to interpret the outputs of the black-box model. The superior performance of the proposed method with high accuracy and good adaptability is verified by the comparisons with conventional forecasting models. This method provides an insight into the regional energy consumption analyzing contributions of weather variables to energy consumption, which helps administers in understanding regional energy performance for enhancing energy efficiency. ? 2024 Elsevier Ltd

ISSN号：0360-5442

卷、期、页：卷301

发表日期：2024-08-15

期刊分区（SCI为中科院分区）：一区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Energy

参与作者：范霖,张丽,玉德俊

通讯作者：彭世亮,何宇轩,何倩,王虓

第一作者：苏怀,张劲军

论文类型：期刊论文

论文概要：彭世亮,范霖,张丽,苏怀,何宇轩,何倩,王虓,玉德俊,张劲军，Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network，Energy，2024，卷301

论文题目：Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network