Forecasting power consumptions of integrated electrical, heat or gas network systems is essential in order to operate more efficiently the whole energy network. Multi-energy systems are increasingly seen as a key component of future energy systems, and a valuable source of flexibility, which can significantly contribute to a cleaner and more sustainable whole energy system. Therefore, there is a stringent need for developing novel and performant models for forecasting multi-energy demand of integrated energy systems, which to account for the different types of interacting energy vectors and of the coupling between them. Previous efforts in demand forecasting focused mainly on the single electrical power consumption or, more recently, on the single heat or gas power consumptions. In order to address this gap, in this paper six novel prediction models based on Convolutional Neural Networks (CNNs) are developed, for either individual or joint prediction of multi-energy power consumptions: the single input/single output CNN model with determining the optimum number of epochs (CNN_1), the multiple input/single output CNN model (CNN_2), the single input/ single output CNN model with training/validation/testing datasets (CNN_3), the joint prediction CNN model (CNN_4), the multiple-building input/output CNN model (CNN_5) and the federated learning CNN model (CNN_6). All six novel CNN models are applied in a comprehensive manner on a novel integrated electrical, heat and gas network system, which only recently has started to be used for forecasting. The forecast horizon is short-term (next half an hour) and all the predictions results are evaluated in terms of the Signal to Noise Ratio (SNR) and the Normalized Root Mean Square Error (NRMSE), while the Mean Absolute Percentage Error (MAPE) is used for comparison purposes with other existent results from literature.

翻译：预测综合电、热或气网系统的电力消耗对于更高效地运行整个能源网络至关重要。多能源系统日益被视为未来能源系统的关键组成部分，以及一种宝贵的灵活性资源，可显著助力构建更清洁、更可持续的整个能源系统。因此，迫切需要开发新颖且性能卓越的模型，用于预测综合能源系统的多能源需求，这些模型需考虑不同类型相互作用的能源载体及其耦合关系。以往的需求预测工作主要集中在单一电力消耗预测上，或近期更侧重于单一热力或燃气消耗预测。为填补这一空白，本文基于卷积神经网络（CNN）开发了六种新颖的预测模型，用于单独或联合预测多能源电力消耗：确定最优训练轮数的单输入/单输出CNN模型（CNN_1）、多输入/单输出CNN模型（CNN_2）、使用训练/验证/测试数据集的单输入/单输出CNN模型（CNN_3）、联合预测CNN模型（CNN_4）、多建筑输入/输出CNN模型（CNN_5）以及联邦学习CNN模型（CNN_6）。所有六种新颖的CNN模型均被全面应用于一个近期才开始用于预测的新型综合电、热、气网络系统。预测时域为短期（未来半小时），所有预测结果均通过信噪比（SNR）和归一化均方根误差（NRMSE）进行评估，同时使用平均绝对百分比误差（MAPE）与文献中其他现有结果进行比较。