http://popups.lib.uliege.be/1373-5411/index.php?id=1989 Publications of Auteurs Kumaraswamy Ponnambalam fr 0 http://popups.lib.uliege.be/1373-5411/index.php?id=2649 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. Thu, 29 Aug 2024 16:07:06 +0200 Thu, 10 Oct 2024 16:25:39 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=2649 http://popups.lib.uliege.be/1373-5411/index.php?id=1988 Great Lakes reservoirs system is a complex natural system containing alarge percentage of the fresh water resources of the world. It is located in Canada and U.S.A. serving about 40 Million people and is managed by an International Joint Commission made up of engineers from these two countries. Management of this system is still based on rule curves and much more could be done to improve this situation. The system is complex also due to highly differing scales of variables, nonlinearity, uncertainty, and dimensionality. An implicit stochastic method is applied using successive approximation optimization to obtain the optimal state and control variables of the reservoirs using the 90 years monthly data. However, when simulating the derived policies a re-optimization in each time period is needed due to inequalities and nonlinear relations existing among variables. The optimal values obtained from simulation are used as input-output data in training an Adaptive-Network-based Fuzzy Inference System (ANFIS) model for one of the months that required a non-constant release policy. ANFIS derives the general operating rules of the reservoirs in the form of fuzzy "if-then" rules. The parameters of a Sugeno-type Fuzzy Inference system (FIS) are optimized through an Artificial Neural Network (ANN) using back-propagation learning algorithm and least square method. The model of our system is anticipatory in nature given the fact that we base our current decision from the expectation of a future state.In this paper, we discuss the various aspects related to our implementation and the computational issues. Simulation of operating policies obtained from the ANFIS model, and comparison of its performance with other policies shows the potential capability of the proposed approach to tackle optimal operations of the system. Fri, 19 Jul 2024 09:50:35 +0200 Thu, 10 Oct 2024 16:13:14 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1988