This paper deals with modelling and identification of a river system using physical insights about the process, experience of operating the system and information about the system dynamics shown by measured data. These components together form a linear model structure in the state space form. The inputs of the prospective model are physical variables, which are not directly measured. However, the model inputs can be found by a nonlinear transformation of measured variables. Unknown parameters of the model are estimated from measured data. The modelling work focuses on the principle of parsimony, which means the best model approach is the simplest one that fit the purpose of the application.

The goal of the model is to control the water level of the river, where the water flow is mainly determined by the demand for energy generation produced by the hydropower stations along the river. The energy requirement increases in the morning and decreases in the evening. These flow variations, caused by the energy demand, have to be compensated by controlling the power plants downstream, in such a way that the water level between the power stations is guaranteed. Simulation of the control system by using an adaptive model predictive controller shows that the water levels vary less and can be maintained at a higher level than during manual control. This means that more electric power can be produced with the same amount of water flow.