Intelligent control for modelling of real‐time reservoir operation

Abstract

Abstract:This paper presents a new approach to improving real-time reservoir operation. The approach combines two major procedures: the genetic algorithm (GA) and the adaptive network-based fuzzy inference system (ANFIS). The GA is used to search the optimal reservoir operating histogram based on a given inflow series, which can be recognized as the base of input-output training patterns in the next step. The ANFIS is then built to create the fuzzy inference system, to construct the suitable structure and parameters, and to estimate the optimal water release according to the reservoir depth and inflow situation. The practicability and effectiveness of the approach proposed is tested on the operation of the Shihmen reservoir in Taiwan. The current M-5 operating rule curves of the Shihmen reservoir are also evaluated. The simulation results demonstrate that this new approach, in comparison with the M-5 rule curves, has superior performance with regard to the prediction of total water deficit and generalized shortage index (GSI).

Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series

“…Some researchers have applied ANFIS in hydrological modelling. Chang & Chang (2001) studied the intelligent control of a real-time reservoir operation model and found that, given sufficient information to construct the fuzzy rules, the ANFIS helps to ensure more efficient reservoir operation than the classical models based on rule curve.…”

Comparison of Decision Tree Based Rainfall Prediction Model with Data Driven Model Considering Climatic Variables

“…For a year period the size of the database might be [360,6]. Extracting information from this huge historical database might lead to poor knowledge recovery from the database [9]. In this study to improve the knowledge recovery process, as a part of data preprocessing important and well correlated reservoir variables were selected based on correlation a statistical technique.…”

Comparison of fuzzy inference systems for streamflow prediction

“…The Gaussian MF is a common choice (e.g. Chang et al, 2001;Xiong et al, 2001;Şen, 2004;Altunkaynak et al, 2005a,b;Özger & Şen, 2007), and is expressed as:…”

“…proposed a fuzzy unit hydrograph to account for the number of uncertainties raised from both model assumptions and data acquisition in representing the rainfall-runoff transformation. There are other FL modelling applications in: rainfall-runoff processes (Abebe et al, 2000;Hundecha et al, 2001;Jacquin & Shamseldin, 2006); river flow routing (See & Openshaw, 2000;Chang & Chang, 2001); groundwater modelling (Hong et al, 2002); water-level prediction in reservoirs ; and time series modelling (Nayak et al, 2004). Deka & Chandramouli (2005) proposed a new approach combining FL and ANNs, which is referred to as fuzzy neural networks (FNN), for river flow prediction.…”