
This dataset comprises the experimental measurements utilized for the "simulation-to-reality" (sim2Real) step of the Attention-Gated U-Net (AttU-Net) firstly trained on the simulated data from "Concentrating solar power (CSP) plants AI-training dataset for flux density measurements." (10.5281/zenodo.15173386). After proving the success of that training on the peer-review journal publication "Concentrating solar power (CSP) plant data-driven digital twin: A novel method for flux density prediction" (https://doi.org/10.1016/j.rineng.2025.107096), the authors conduct this second training with the aim of reaching experimental accuracies in flux density predictions for solar power towers. The normalized grayscale images were obtained throughout a 3-month experimental campaign which took place in the Solar Tower Jülich (STJ) facility between May, 2025 and July, 2025. The location of this plant is Jülich, Germany (50.915° N, 6.388° E) at 110 metres over sea level and it is equipped with 2016 heliostats of 8 squared-metres. The utilized Lambertian white surface is the 4x4 squared-metres radiation shield of HEHTRES receiver, located at 40.8 metres centred at the western tower of that facility (check layout images). For repeatibility, this dataset is structured as follows: Training images. This folder contains the 572 "input" and "groundtruth" pairs - divided in two subfolders - utilized for training the AttU-Net. Therefore, their resolutions are downsampled to 256x256 pixels. Input: it contains 572 simulated flux maps under the same input conditions registered at the corresponding experiment. Each image is labelled by using the experiment time (nomenclature: "yymmdd_hhmmss_down.png"). Groundtruth: it contains the 572 real images of the radiation shield under irradiation. Each image is labelled by using the experiment time (nomenclature: "yymmdd_hhmmss_down.png"). Input conditions. This folder contain a subfolder for each of the timesteps recorded in experiments. Inside of each subfolder (nomenclature: "yymmdd-hhmmss") a JSON file named the same way (+"yymmdd-hhmmss.json") is found. It contains the following self-explanatory entries: "datetime". Date and time of the experimental measurement [yymmdd-hhmmss]. "radiometer_values". Recorded signals of the 4 radiometers equipped (1 to 4 in clockwise direction beginning from the lower left corner), [kW/m²]. "dni_value". Average DNI recorded by two pyrheliometers (located at each end of the heliostat field), [W/m²]. "reflectivity_value". Average reflectivity from the heliostat field. "solar_elevation". Sun position at experiment time, [deg]. "solar_azimuth". Sun position at experiment time, [deg]. "active_heliostats". Heliostats focused toward the receiver. Layout figure. Validation dataset. GIF from validation (inference) conditions. Full dataset movement. GIF from training dataset conditions.
Deep Learning, Concentrating Solar Power, Flux Density Prediction, Convolutional Neural Networks
Deep Learning, Concentrating Solar Power, Flux Density Prediction, Convolutional Neural Networks
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