Using Predictive Models of Mean Monthly Flows for Operative Outflows Control from Large Open Reservoirs

Conventional storage reservoir operations are mostly based on rules or rule curves. Both are built up on generalization of past water inflows represented by historical time series. Flow series are changing in the time of expected climate change. It is important to find a new intelligent operation methods of outflows from large open water reservoirs that would be allow to respond on undetermined conditions during climate change. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. The objective function is descripted as the sum of squares deviations between required and actual controlled water outflow from open reservoir where objective function is minimized. Principles of the several predictive models of average monthly flows are introduced in this paper. The algorithm is applied to the operation storage control of selected reservoir. The achieved results for predictions different length are compared and generalized.