Deriving reservoir operating rules via fuzzy regression and ANFIS
نویسندگان
چکیده
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 to provide the input-output data set to be used by OLSR, FR, and ANFIS models. The coefficients of an FR model are found by solving a linear programming (LP) problem. A trained fuzzy inference system (ANFIS) is also used to derive the reservoir operating rules as fuzzy if-then rules. The OLSR, FR, and ANFIS based rules are then simulated and compared. The methods are applied to a long-term planning problem and to a medium-term implicit stochastic optimization model. FR is useful to derive operating rules for the long-term model, where partial information is available. ANFIS is beneficial in the medium term implicit stochastic model as it is able to extract important features of the system from the generated input-output set.
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