Abstract
This work presents operational data of a large-scale solar thermal collector array. The array belongs to a solar thermal plant located at Fernheizwerk Graz, Austria, which feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe. The collector array deploys flat plate collectors with a total gross collector area of 516 m(2) (361 kW nominal thermal power). Measurement data was collected in situ within the scientific research project MeQuSo using high-precision measurement equipment and implementing extensive data quality assurance measures. Data compromises one full operational year (2017) in a 1-minute sampling rate with a share of missing data of 8.2%. Several files are provided, including data files and Python scripts for data processing and plot generation. The main dataset contains the measured values of various sensors, including volume flow, inlet and outlet temperature of the collector array, outlet temperatures of single collector rows, global tilted and global horizontal irradiance, direct normal irradiance, and weather data (ambient air temperature, wind speed, ambient relative humidity) at the plant location. Beyond the measurement data, the dataset includes additional calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks. The dataset also provides uncertainty information in terms of standard deviation of a normal distribution, based either on sensor specifications or on error propagation of the sensor uncertainties. Uncertainty information is provided for all continuous variables, with some exceptions such as the solar geometry, where uncertainty is negligible. The data files include a JSON file containing metadata (e.g., plant parameters, data channel descriptions, physical units, etc.) in both human and machine-readable format. The dataset is suitable for detailed performance and quality analysis and for modelling of flat plate collector arrays. Specifically, it can be helpful to improve and validate dynamic collector array models, radiation decomposition and transposition algorithms, short-term thermal power forecasting algorithms with machine learning techniques, performance indicators, in situ performance checks, dynamic optimization procedures such as parameter estimation or MPC control, uncertainty analyses of measurement setups, as well as testing and validation of open-source software code. The dataset is released under a CC BY-SA 4.0 license. To the best knowledge of the authors, there is no comparable dataset of a large-scale solar thermal collector array publicly available.