The global water and energy demand is expected to grow significantly, making them vital resources that must be managed wisely. The main objective of this paper is to investigate the nonlinear relationship between the water consumption and electricity generation of hydropower plants with dams using the Adaptive NeuroFuzzy Inference System model. The annual average electricity generation and basin-based meteorological parameters of 80 hydroelectric power plants with dams were used as the input parameters. Their water consumption was used as the output parameter in the model. The dataset is divided randomly into training and testing datasets with ratios of 65%-35%, 70%-30%, 80%-20%, and 85%-15%. The 85%-15% splitting gave the best results with 13.54 % MAPE of all datasets. The water consumption of hydropower plants was forecasted between 2023 and 2053 using the GFDL RCP 4.5 and 8.5 climate scenarios. The results show that water intensity (m 3 /GWh) in 2023 will increase from 39,419 m 3 /GWh and 38,663 m 3 /GWh to 65,414 m 3 /GWh and 63,768 m 3 /GWh in 2053, respectively.
Citationcoşkun dilcan, çiğdem & köksal, merih. (2022). FORECASTING THE WATER CONSUMPTION OF HYDROELECTRICITY POWER PLANTS IN THE CONTEXT OF THE WATER-ENERGY NEXUS BASED ON AN ARTIFICIAL INTELLIGENCE APPROACH.
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