Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy

被引:25
作者
Fayaed, Sabah S. [1 ]
El-Shafie, Ahmed [1 ]
Jaafar, Othman [1 ]
机构
[1] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Dept Civil & Struct Engn, Bangi 43600, Malaysia
关键词
Artificial neural network (ANN); Simulation technique; Optimization technique; Stochastic dynamic programming (SDP); Reservoir operation policy; Sg. Langat dam; WATER-RESOURCES MANAGEMENT; RESERVOIR OPERATION; SYSTEMS; VULNERABILITY; RELIABILITY; PERFORMANCE; HYDROPOWER; CRITERIA;
D O I
10.1007/s11269-013-0373-5
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Complexicity in reservoir operation poses serious challenges to water resources planners and managers. These challenges of water reservoir operation are illustrated using a simulation to aid the development of an optimal operation policy for dam and reservoir. To achieve this, a Comprehensive Stochastic Dynamic Programming with Artificial Neural Network (SDP-ANN) model were developed and tested at Sg. Langat Reservoir in Malaysia. The nonlinearity of the natural physical processes was a major problem in determining the simulation of the reservoir parameters (elevation, surface-area, storage). To overcome water shortages resulting from uncertainty, the SDP-ANN model was used to evaluate the input variable and the performance outcome of the Model were compared with the Stochastic Dynamic Programming integrated with auto-regression (SDP-AR) model. The objective function of the models was set to minimize the sum of squared deviation from the desired targeted supply. Comparison result on the performance between SDP-AR model policy with SDP-ANN model found that the SDP-ANN model is a reliable and resilience model with a lesser supply deficit. The study concludes that the SDP-ANN model performs better than the SDP-AR model in deriving an optimal operating policy for the reservoir.
引用
收藏
页码:3679 / 3696
页数:18
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