DHNs simulation data

This simulation dataset provides the topology structures of four different District Heating Networks (DHNs), along with randomly selected consumer clusters. It features high-resolution, with 1-minute time step, simulation results for 5.5 months, including water temperatures, mass flow rates, consumer substation heating demands, and heat source power productions. This data serves as the foundation for training, validating and testing diverse neural networks models to learn clusters underlying physics. Publicly available training and data pipeline code on Github [https://github.com/drod-96/smart_clusters_v1] facilitates data extraction for our machine learning models and post-treatment.

搜集汇总

背景与挑战

背景概述

该数据集包含四个区域供热网络的拓扑结构和消费者集群的高分辨率模拟数据(1分钟时间步长，5.5个月)，涵盖水温、流量、热需求等多维参数，专为神经网络模型训练设计，并配有公开的GitHub数据处理代码。