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Dissertations / Theses on the topic 'Reservoir inflow forecasting'

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Published: 4 June 2021

Last updated: 11 February 2022

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1

Burton, Holly. "Reservoir inflow forecasting using time series and neural network models." Thesis, McGill University, 1998. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29800.

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In practice, the reservoir net inflow is computed based upon the application of the water balance equation to the reservoir system since it is difficult to obtain direct and reliable measurements of this variable. The net inflow process has been thus found to possess a random behaviour because it is related to the stochastic nature of various physical processes involved in the water balance computation (e.g., precipitation, evaporation, etc.). Therefore, the aim of this research is to propose a forecasting method that can accurately and efficiently predict the random reservoir inflow series. T

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2

Burton, Holly. "Reservoir inflow forecasting using time series and neural network models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0017/MQ54220.pdf.

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3

Barnard, Joanna Mary. "The value of inflow forecasting in the operation of a hydroelectric reservoir." Thesis, University of British Columbia, 1989. http://hdl.handle.net/2429/27759.

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The present study examines the value of conceptual hydrologic forecasting in the operation of a hydroelectric generating project. The conceptual forecasting method used is the UBC Watershed Model. The value of the conceptual forecast is determined by comparing results obtained by use of the forecast to those obtained by use of a forecast based purely on the historic record. The effect of the size of the reservoir on the value of the forecast is also considered. The operation of a hypothetical project is modelled using dynamic programming. The operation of the project is optimized using the co

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4

Zhou, Dequan. "The value of one month ahead inflow forecasting in the operation of a hydroelectric reservoir." Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/30145.

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The research assesses the value of forecast information in operating a hydro-electric project with a storage reservoir. The benefits are the increased hydro power production, when forecasts are available. The value of short term forecasts is determined by comparing results obtained with the use of one month ahead perfect predictions to those obtained without forecasts but a knowledge of the statistics of the possible flows. The benefits with perfect forecasts provide an upper limit to the benefits which could be obtained with actual less than perfect forecasts. The effects of generating capac

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5

Sázel, Jiří. "Střednědobé předpovědi průtoků vody v měrném profilu toku." Doctoral thesis, Vysoké učení technické v Brně. Fakulta stavební, 2015. http://www.nusl.cz/ntk/nusl-234548.

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Thesis is aimed on creation of prediction model for releasing medium-term water stream flow forecasts. Created model create forecasts based on principal of finding most similar historical case. Usefulness of forecasting model is demonstrated for operation of one isolated reservoir in gauge profile Oslavany on river Oslava.

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6

Xie, Ming 1973. "Prediction of daily net inflows for management of reservoir systems." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33043.

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Operational planning of water resource systems like reservoirs and hydropower plants calls for real-time forecasting of reservoir inflow. Reservoir daily inflow forecasts provide a warning of impending floods or drought conditions and help to optimize operating policies for reservoir management based on a fine time scale. The aim of this study was to determine the best model for daily reservoir inflow prediction through linear regression, exponential smoothing and artificial neural network (ANN) techniques. The Hedi reservoir, the third largest reservoir in south China with a 1.144 x 109 m 3,

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7

Dixon, Samuel G. "Seasonal forecasting of reservoir inflows in data sparse regions." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/33524.

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Management of large, transboundary river systems can be politically and strategically problematic. Accurate flow forecasting based on public domain data offers the potential for improved resource allocation and infrastructure management. This study investigates the scope for reservoir inflow forecasting in data sparse regions using public domain information. Four strategically important headwater reservoirs in Central Asia are used to pilot forecasting methodologies (Toktogul, Andijan and Kayrakkum in Kyrgyzstan and Nurek in Tajikistan). Two approaches are developed. First, statistical forecas

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8

Bourdin, Dominique R. "A probabilistic inflow forecasting system for operation of hydroelectric reservoirs in complex terrain." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45173.

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This dissertation presents a reliable probabilistic forecasting system designed to predict inflows to hydroelectric reservoirs. Forecasts are derived from a Member-to-Member (M2M) ensemble in which an ensemble of distributed hydrologic models is driven by the gridded output of an ensemble of numerical weather prediction (NWP) models. Multiple parameter sets for each hydrologic model are optimized using objective functions that favour different aspects of forecast performance. On each forecast day, initial conditions for each differently-optimized hydrologic model are updated using meteorologic

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9

Westra, Seth Pieter Civil &amp Environmental Engineering Faculty of Engineering UNSW. "Probabilistic forecasting of multivariate seasonal reservoir inflows: accounting for spatial and temporal variability." Awarded by:University of New South Wales. Civil & Environmental Engineering, 2007. http://handle.unsw.edu.au/1959.4/40630.

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Hydrological variables such as rainfall and streamfiow vary at a range of temporal scales, from short term (diurnal and seasonal) to the inter annual time scales associated with the El Nino - Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) phenomena, to even longer time scales such as those linked to the Pacific (inter-) Decadal Oscillation (PDO). This temporal variability poses a significant challenge to hydrologists and water resource managers, since a failure to take such variability into account can lead to an underestimation of the likelihood of droughts and sequences of above a

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10

Signoriello, Giuseppe Alessandro 1977. "Modelos matemáticos para previsão de vazões afluentes à aproveitamentos hidrelétricos." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265912.

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Orientador: Ieda Geriberto Hidalgo<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-25T19:15:52Z (GMT). No. of bitstreams: 1 Signoriello_GiuseppeAlessandro_M.pdf: 31629174 bytes, checksum: 1674c1adcccf93d9b3ee9711be3f709e (MD5) Previous issue date: 2014<br>Resumo: Este trabalho apresenta a comparação de dois modelos matemáticos desenvolvidos para prever vazões afluentes à usinas hidrelétricas. O objetivo é abordar os aspectos que determinam a qualidade do insumo fundamental para a programação da operação do

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11

Jeng, Jia-Haur, and 鄭家豪. "Improved back-propagation networks for reservoir inflow forecasting." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/81929178641702467805.

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碩士<br>國立臺灣大學<br>土木工程學研究所<br>96<br>The efficiency is an important issue for neural networks-based models, but the issue has received little attention in the hydrologic domain. Back-propagation networks (BPNs) are the most frequently used convectional neural networks (NNs). However, BPNs are trained by the error back-propagation algorithm which is a very time-consuming iterative process. To improve the efficiency, improved BPNs which are trained by a novel query learning approach are proposed. The proposed query learning approach is capable of selecting informative data from all training dat

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12

Chen, Chien-Hong, and 陳建宏. "The Influence of Rainfall Factor on Reservoir Inflow Forecasting." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/37061468465019297180.

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13

Chang, Chia-Chuang, and 張家銓. "Improved Self-organizing Linear Output Map for Reservoir Inflow Forecasting." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/25754392471904807349.

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碩士<br>國立臺灣大學<br>土木工程學研究所<br>97<br>Based on self-organizing linear output map (SOLO), effective hourly reservoir inflow forecasting models are proposed. As compared with back-propagation neural network (BPNN) which is the most frequently used conventional neural network (NN), SOLO has four advantages: (1) SOLO has better generalization ability; (2) the architecture of the SOLO is simpler; (3) SOLO is trained much more rapidly, and (4) SOLO could provide features that facilitate insight into underlying processes. An application is conducted to clearly demonstrate these four advantages. The re

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14

Li, Yen-Chuan, and 李晏全. "Monthly and Seasonal Inflow Forecasting of Shihmen Reservoir during the Dry Seasons." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/37353739790513337359.

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碩士<br>國立成功大學<br>水利及海洋工程學系碩博士班<br>94<br>If the reservoir inflows can be forecasted precisely beforehand, they may benefit the reservoir operation and management in Taiwan. The long-term inflow forecasting system of reservoir combines a continuous rainfall-runoff model with the long-term weather outlook provided by the Central Weather Bureau to forecast one-month and three -month ahead inflows with the ten-day and one-month time steps. There are several tasks in the present study, including (1) the developing of a rainfall-runoff model based on the daily time step, (2) the developing of ten-day

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15

Chou, Yang-Ching, and 周揚敬. "Using support vector machines to improve reservoir inflow forecasting during typhoon-warning periods." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/44933898752793153567.

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碩士<br>國立臺灣大學<br>土木工程學研究所<br>96<br>In this paper, effective reservoir inflow forecasting models based on the support vector machine (SVM), which is a novel kind of neural networks (NNs), are proposed. Based on statistical learning theory, the SVMs have three advantages over back-propagation netwoks (BPNs), which are the most frequently used convectional NNs. Firstly, SVMs have better generalization ability. Secondly, the architectures and the weights of the SVMs are guaranteed to be unique and globally optimal. Finally, SVM is trained much more rapidly. An application is conducted to clearly de

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16

Wu, Lei-Ken, and 吳雷根. "A study on Long-term Inflow Forecasting of Tsengwen Reservoir during the Dry Seasons." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/qaevh8.

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碩士<br>國立成功大學<br>水利及海洋工程學系碩博士班<br>92<br>Long-term inflows of reservoir form upstream catchment are important information for reservoir operation. If the inflows of reservoir can be forecasted precisely beforehand, that may benefit the reservoir operation and management. Therefore, we attempt to develop a long-term inflow forecasting system of reservoir during the dry seasons and apply the system in the upstream catchment of Tsengwen reservoir, which is to support the reservoir management for operation decision and drought warning. 　　The long-term inflow forecasting system of reservoir that com

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17

Kuo, Sui-An, and 郭隨安. "Long lead-time reservoir inflow forecasting by adapting a rainfall-runoff model with ensemble precipitation forecasts." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/j78qp6.

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碩士<br>國立臺灣大學<br>土木工程學研究所<br>106<br>Taiwan is located on the main track of western Pacific typhoons, and approximately three to four typhoons hit Taiwan per year. Typhoons accompanied by heavy rainfall often results in a huge amount of runoff, which causes downstream floods and induces great disasters. Meanwhile, the reservoir operator should assess flood control operations carefully. The dam security and downstream residents are both taken into consideration. In this case, prerelease for real time flood operation is important. Accurate reservoir inflow forecasting with enough lead time helps t

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18

Lima, Luana Medeiros Marangon. "Modeling and forecast of Brazilian reservoir inflows via dynamic linear models under climate change scenarios." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4687.

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The hydrothermal scheduling problem aims to determine an operation strategy that produces generation targets for each power plant at each stage of the planning horizon. This strategy aims to minimize the expected value of the operation cost over the planning horizon, composed of fuel costs to operate thermal plants plus penalties for failure in load supply. The system state at each stage is highly dependent on the water inflow at each hydropower generator reservoir. This work focuses on developing a probabilistic model for the inflows that is suitable for a multistage stochastic algorithm th

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