In hydroelectric systems, water inflow is important to coordinate a cascade and define the energy price. This paper presents a method for managing inflow forecasting studies with a specific module for advanced assessment. The main goal is to provide a structure that facilitates the analysis of water inflow prediction models. A case study has been applied to five mathematical models based on linear regression, artificial neural networks, and hydrologic simulation. These models present daily and monthly inflow forecasts for a set of hydroelectric plants and monitoring stations. The benefits of the proposed method are analyzed in four situations: water inflow prediction, performance evaluation of a specific model, research tool for inflow forecasting, and comparison tool for distinct models. The results show that implementation of the proposed method provides a useful tool for managing inflow forecasting studies and analyzing models. Therefore, it can assist researchers and engineering professionals alike by improving the quality of water inflow predictions.
Management of inflow forecasting studies
I. G. Hidalgo, P. S. F. Barbosa, A. L. Francato, I. Luna, P. B. Correia, P. S. M. Pedro; Management of inflow forecasting studies. Water Practice and Technology 1 June 2015; 10 (2): 402–408. doi: https://doi.org/10.2166/wpt.2015.050
Download citation file: