Soiling determination for parabolic trough collectors based on operational data analysis and machine learning.

Advanced cleaning strategies for parabolic trough collectors at concentrated solar power plants maximize the yield and minimize the costs for cleaning activities. However, they require information about the current soiling level of each collector. In this work, a novel, data-driven method for soiling estimation with machine learning for parabolic trough collectors is developed using gloss values as a surrogate for soiling values. Operational data and meteorological data from the solar field Andasol-3 with changing time horizons are used together with various Machine Learning techniques to estimate the soiling of every collector in the field. The best results were achieved with a Decision Tree model, with a coefficient of determination of R 2 = 0. 77 from the maximum value of 1 and a mean squared error of M S E = 6. 14 for the determination of specific soiling values. A second metric to evaluate the quality of soiling predictions from the models classifies whether soiling is above or below a cleaning threshold was also investigated. Model results are compared to soiling measurements that indicate the need for cleanings. Cleaning recommendations are derived and compared with the current fixed-time cleaning schedule of Andasol-3. All models show an improvement over the cleaning schedule currently in use. The use of a Decision Tree model increases the detected necessary cleanings by 12.2 %, while the number of unnecessary cleanings are reduced by 14.3 %. This has the potential to reduce operational costs and increase the solar field yield. The dataset used in this work is made publicly available https://doi.org/10.5281/zenodo.7061913 , along with the code to reproduce all results, which can be found at https://doi.org/10.5281/zenodo.7554806. • Data-driven soiling model for parabolic troughs with process and meteo data. • Comparison of various machine learning models and dataset sizes. • Evaluation of model for soiling prediction and cleaning schedule optimization. • Investigation of the most important model inputs via permutation feature importance. • Code and dataset available at (https://doi.org/10.5281/zenodo.7554806 , https://doi.org/10.5281/zenodo.7061913). [ABSTRACT FROM AUTHOR]