Global and regional models for identification of cooling technology in thermal power generation for water demand estimations in water-energy nexus studies

Abstract

Water-energy nexus studies aim to connect the process of power generation with the corresponding water demand. The lack of information on the currently installed cooling systems at individual power plants is a challenge for the assessment of the water use on the power plant-level, which complicates decision-making in water management, especially in water-stressed regions. In this study, we investigate the spatial and temporal trends in cooling technology installations globally. Based on that, we propose a machine learning model for cooling technology identification on a regional and global level, which uses a combination of feature selection and classification algorithms. The global model demonstrates an average test set accuracy of 85.42%, which corresponds to only a minor underestimation of the actual global water footprint of 1.78% when the cooling technology and water footprint of individual power plant units is unknown. Apart from that, a special emphasis was placed on regions characterized by high and extremely high water stress, where mistakes in water policy planning and water management may lead to an unsustainable water use or even to an overexploitation of water resources. In these regions, the calculated test set accuracy was 80.83%, which is significantly larger than the average accuracy of a majority class model. The results and the method proposed in this study enable cooling system identification in individual power units using information available from other sources, such as the water stress score or seasonal freshwater availability in the region.