Deriving Reservoir Operating Rules via Fuzzy Regression and ANFIS

The methods of ordinary least-squares regression (OLSR), fuzzy regression (FR), and adaptive network fuzzy inference system (ANFIS) are compared in inferring operating rules for a reservoir operations problem. Dynamic programming (DP) is used as an optimization tool to provide the input-output data set to be used by OLSR, FR, and ANFIS models. The OLSR. FR. and ANFIS based rules are then simulated and compared. The methods are applied to a long-term planning problem as well as to a medium-term implicit stochastic optimization model. The results indicate that FR is useful to derive operating rules for a long-term planning model, where imperfect and partial information is available. ANFIS is beneficial in medium term optimization as it is able to extract important features of the system from the generated input-output set.