JSON dataset för simulerad byggnadsvärmekontroll för system-av-system interoperabilitet

Interoperability in systems-of-systems is a difficult problem due to the abundance of data standards and formats. Current approaches to interoperability rely on hand-made adapters or methods using ontological metadata. This dataset was created to facilitate research on data-driven interoperability solutions. The data comes from a simulation of a building heating system, and the messages sent within control systems-of-systems. For more information see attached data documentation. The data comes in two semicolon-separated (;) csv files, training.csv and test.csv. The train/test split is not random; training data comes from the first 80% of simulated timesteps, and the test data is the last 20%. There is no specific validation dataset, the validation data should instead be randomly selected from the training data. The simulation runs for as many time steps as there are outside temperature values available. The original SMHI data only samples once every hour, which we linearly interpolate to get one temperature sample every ten seconds. The data saved at each time step consists of 34 JSON messages (four per room and two temperature readings from the outside), 9 temperature values (one per room and outside), 8 setpoint values, and 8 actuator outputs. The data associated with each of those 34 JSON-messages is stored as a single row in the tables. This means that much data is duplicated, a choice made to make it easier to use the data. The simulation data is not meant to be opened and analyzed in spreadsheet software, it is meant for training machine learning models. It is recommended to open the data with the pandas library for Python, available at https://pypi.org/project/pandas/.系统之系统（Systems-of-Systems）的互操作性是一项极具挑战性的难题，其根源在于数据标准与格式种类繁多、纷繁复杂。当前主流的互操作性实现方案，要么依赖手工定制的适配器，要么采用基于本体元数据的方法。 本数据集旨在推动数据驱动型互操作性解决方案的相关研究工作。 数据来源于建筑供暖系统的仿真模拟，以及控制系统之系统内传输的控制报文。如需获取更多详细信息，请参阅随附的数据文档。 本数据集包含两个以分号（;）分隔的CSV文件，分别为training.csv与test.csv。训练集与测试集的划分并非随机：训练数据取自仿真时间步的前80%，测试数据则对应剩余的后20%。本数据集未设置独立的验证集，验证数据需从训练集中随机选取。 仿真的总时间步数与可用的室外温度数据点数完全一致。原始SMHI数据仅支持每小时采样一次，我们通过线性插值将其转换为每10秒一次的温度采样值。 每个时间步存储的数据包含34条JSON报文（每个房间对应4条报文，外加2条室外温度读数报文）、9项温度数值（每个房间1项，外加1项室外温度数值）、8项设定值以及8项执行器输出。这34条JSON报文各自对应表格中的一行数据，因此存在大量数据冗余——该设计选择旨在降低数据集的使用难度。 本仿真数据集并非为电子表格软件的分析场景而设计，而是专门面向机器学习模型的训练任务。推荐使用Python的pandas库读取该数据集，其官方下载地址为https://pypi.org/project/pandas/。