scholarly journals Hybridizing ANN-NSGA-II model with genetic programming method for reservoir operation rule curve determination (Case study Zayandehroud dam reservoir)

2021 ◽  
Author(s):  
Ramtin Moeini ◽  
Kamran Nasiri
2021 ◽  
Author(s):  
ramtin moeini ◽  
kamran nasiri

Abstract One of the most important and effective works of water resource planning and management is determining the specific, applicable, regulated operating policies of the Zayandehroud dam reservoir, as a case study, in which it should be user-friendly and straightforward for the operator. For this purpose, different methods have been proposed in which each of them has its limitations. Due to the unique capabilities of the genetic programming (GP) model, here, this method is used to determine the operating rule curves and policies of the dam reservoir. For this purpose, here, two cases are proposed in which, in the first case, each month is individually simulated and modeled. However, in the second case, all months are simulated simultaneously. A second case is proposed here to determine simple and more applicable operation rule curves. In addition, two approaches are suggested for each case in which in the first approach, the influential input variables are selected by presenting the hybrid method. In the proposed hybrid method, the artificial neural network (ANN) model is equipped with non dominated sorting genetic (NSGA-II) algorithm leading to a hybrid method named the ANN-NSGA-II method. However, in the second approach, the influential input variables are selected automatically using the GP method. Here, the hybrid method is proposed and used to overcome the limitations of existing usual method. In other words, it is proposed to reduce the number of influential input variables of data-driven methods and select the effective ones. The obtained results of all proposed cases and approaches are presented and compared with the standard operation policy (SOP) method, stochastic dynamic programming (SDP), ANN model and, NLP method. Comparison of the results shows the acceptable performance of the proposed cases and approaches. In other words, the best- obtained values of (stability index) SI index and water deficit (objective function value) are 49.3% and 32, respectively.


2016 ◽  
Vol 7 (2) ◽  
pp. 58-67
Author(s):  
Gregory Titus ◽  
Frederik Josep Putuhena

An operation rule curve of a dam provides specific on the target elevation of thereservoir. This can vary throughout the year. This is an indication to the reservoir operator ofactivities to conduct for various situations involving reservoir and the hydrologic conditions.This rule curve shall apply to Murum dam solely for power generation purpose. Thedecisions on when and how much to release will impact the ability to pass a flood as well ashydropower capacity. The main objective of this research is to select the most suitablemethod and to develop the operation rule curves for Murum dam primarily for hydropowergeneration. A trial-and-error method has been selected for this study. Reservoir operationmodeling and simulation have been commenced using historical rainfall data, after runoffwas generated by rainfall runoff modellings. Three (3) simulations are conducted fordeveloping the operation rule curves. They are rule curve according to Sarawak EnergyBerhad’s requirement, ideal rule curve and mean rule curve. All three sets of rule curves aresuccessfully developed using turbine discharges of the dam as the parameters and to achievethe target firm energy generation of 635MW.


2012 ◽  
Vol 2012 ◽  
pp. 1-32 ◽  
Author(s):  
Konstantinos Salpasaranis ◽  
Vasilios Stylianakis

The introduction of a hybrid genetic programming method (hGP) in fitting and forecasting of the broadband penetration data is proposed. The hGP uses some well-known diffusion models, such as those of Gompertz, Logistic, and Bass, in the initial population of the solutions in order to accelerate the algorithm. The produced solutions models of the hGP are used in fitting and forecasting the adoption of broadband penetration. We investigate the fitting performance of the hGP, and we use the hGP to forecast the broadband penetration in OECD (Organisation for Economic Co-operation and Development) countries. The results of the optimized diffusion models are compared to those of the hGP-generated models. The comparison indicates that the hGP manages to generate solutions with high-performance statistical indicators. The hGP cooperates with the existing diffusion models, thus allowing multiple approaches to forecasting. The modified algorithm is implemented in the Python programming language, which is fast in execution time, compact, and user friendly.


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