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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). Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
123.
To investigate the impact of carbon emission reduction paths on energy demand and CO2 emissions in China, in this study, quantitative carbon emission reduction paths in the period 2014–2020 are established by decomposing the target for emissions reduction. An optimization model of energy demand, into which reduction paths are incorporated, is then constructed from a goal-oriented perspective. The results suggest that energy consumption varies under different emission reduction paths. Coal demand is found to be much more sensitive to the choice of emission reduction path than other forms of energy; in particular, it responds strongly to the decreasing reduction path. We conclude that the decreasing reduction path is a better means than the increasing reduction path of achieving China’s emission reduction target for 2020 with the least amount of energy and the least amount of CO2 emissions. 相似文献