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1
Khanal, Anup. "Inflow Forecasting for Nepalese Catchments." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for vann- og miljøteknikk, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22774.
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Due to the tropical climate, Nepalese rivers experience the large floods during monsoon season. Prediction of flood in advance is very essential not only for the successful hydropower operation but also for establishing effective flood warning system. Though developed country like Norway has been using inflow forecasting as a part of reservoir operation and flood warning system since long ago, so far no study related to inflow forecasting has been carried out in Nepal. This study is the first initiation of work in the field of inflow forecasting for Nepalese catchment. It attempts to establish the inflow forecasting system to the Kulekhani reservoir, employ the forecasted inflow in reservoir operation and present an example of flood warning system.The outputs of the Global Forecast System (GFS) model which is run in spatial resolution of approximately 50km x 50km and temporal resolution of 3 hrs were selected as meteorological forecasts to carry out the inflow forecast simulation. The spatial resolution of GFS model is on the range of Regional Circulation Model (RCM) so no further downscaling was done but modeled data were subjected to bias correction after comparing it to observed data. Two advanced methods of bias correction viz. empirical adjustment method and statistical bias correction method were applied to the precipitation and temperature forecasts. The empirical adjustment method did not perform very well in bias correction of precipitation forecasts as it requires long series of observational and forecast data. So the statistical method was applied for the bias correction of precipitation forecasts. But in the case of bias correction of temperature forecasts, the empirical adjustment method was found satisfactory. Due to difficulty in getting real time meteorological data of Kulekhani catchment from Trondheim, a historical period was chosen for the HBV model setup and inflow forecast simulation. The model calibration was done based on the observed hydrometerological data and the best value of goodness of fit as described by R2 was found to be 0.76. This low value of R2 is characterized by the uncertainties in observed inflow since observed inflow was calculated indirectly based on the daily energy production and reservoir level. The model was updated by adjusting values in precipitation and temperature, and model state variables. Then the forecast simulation was run on 8 consecutive days. Large degree of uncertainty was found in inflow forecast due to use of meteorological forecasts produced in coarser spatial resolution and unavailability of measured inflow during HBV model calibration. The inflow forecast was further used in existing reservoir operational model to examine whether Kulekhani project can meet the energy demand or not in relation with forecasted inflow up to 7 days in advance. The forecasted inflow was also analyzed in terms of flood forecast to set up an effective flood warning system. In conclusion, this study has been successful to carry out inflow forecasting based on meteorological forecasts. However, large degree of uncertainty in inflow forecasting is observed. The reservoir operation and flood warnings are also affected by the uncertainty seen in inflow forecasting. Improvements on this study can be made by using meteorological forecasts with finer spatial resolution and carrying out calibration of the HBV model with measured inflow for sufficiently long period.
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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. The proposed forecasting methods considered were the linear regression, the exponential smoothing technique, the periodic autoregressive moving average (PARMA) method, and the neural network procedure. An illustrative application was carried out using 25 years (1970--1994) of monthly rainfall and inflow data from the Pedu-Muda reservoirs in Kedah, Malaysia. The first 18 years (1970--1987) were used for calibration while the remaining 7 years (1988--1994) were used for verification of the proposed models. (Abstract shortened by UMI.)
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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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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 conceptual and historic forecasts to generate a variety of operating policies. The operation of the project is then simulated using the derived operating policies and several test years of real data, to determine the potential energy generation for each scenario. The analysis is performed for several reservoir sizes and for deterministic and two stochastic representations of the data.
The analysis concludes that conceptual forecasting is most useful when the annual flow is significantly different from the average annual flow of the basin. If an historic forecast is used, a deterministic representation of the data is most valuable. If a conceptual forecast is used stochastic analysis gives the most efficient operation. Forecasting of either kind is valuable for reservoir sizes greater than 25% of the mean annual flow, but the value decreases as the volume approaches 100% of the mean annual flow.Applied Science, Faculty of
Civil Engineering, Department of
Graduate
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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 meteorological observations. Thus, the M2M ensemble explicitly samples inflow forecast uncertainty caused by errors in the hydrologic models, their parameterizations, and in the initial and boundary conditions (i.e., meteorological data) used to drive the model forecasts.
Bias is removed from the individual ensemble members using a simple degree-of-mass-balance bias correction scheme. The M2M ensemble is then transformed into a probabilistic inflow forecast by applying appropriate uncertainty models during different seasons of the water year. The uncertainty models apply ensemble model output statistics to correct for deficiencies in M2M spread. Further improvement is found after applying a probability calibration scheme that amounts to a re-labelling of forecast probabilities based on past performance.
Each component of the M2M ensemble has an associated cost in terms of time and/or money. The relative value of each ensemble component is assessed by removing it from the ensemble and comparing the economic gains associated with the reduced ensembles to those achieved using the full M2M system. Relative value is computed using a simple (static) cost-loss decision model in which the reservoir operator takes action (lowers the reservoir level) when significant inflows are predicted with probability exceeding some threshold.
The probabilistic reservoir inflow forecasting system developed in this dissertation is applied to the Daisy Lake hydroelectric reservoir located in the complex terrain of southwestern British Columbia, Canada. The hydroclimatic regime of the case study watershed is such that flashy fall and winter inflows are driven by Pacific frontal systems, while spring and summer inflows are dominated by snow and glacier melt. Various aspects of ensemble and probabilistic forecast performance are evaluated over a period of three water years.
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Claesson, Jakob, and Sam Molavi. "Intelligent hydropower : Making hydropower more efficient by utilizing machine learning for inflow forecasting." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279609.
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Inflow forecasting is important when planning the use of water in a hydropower plant. The process of making forecasts is characterized by using knowledge from previous events and occurrences to make predictions about the future. Traditionally, inflow is predicted using hydrological models. The model developed by the Hydrologiska Byråns Vattenbalansavdelning (HBV model) is one of the most widely used hydrological models around the world. Machine learning is emerging as a potential alternative to the current HBV models but needs to be evaluated. This thesis investigates machine learning for inflow forecasting as a mixed qualitative and quantitative case study. Interviews with experts in various backgrounds within hydropower illustrated the key issues and opportunities for inflow forecasting accuracy and laid the foundation for the machine learning model created. The thesis found that the noise in the realised inflow data was one of the main factors which affected the quality of the machine learning inflow forecasts. Other notable factors were the precipitation data from the three closest weather stations. The interviews suggested that the noise in the realised inflow data could be due to faulty measurements. The interviews also provided examples of additional data such as snow quantity measurements and ground moisture levels which could be included in a machine learning model to improve inflow forecast performance. One proposed application for the machine learning model was as a complementary tool to the current HBV model to assist in making manual adjustments to the forecasts when considered necessary. The machine learning model achieved an average Mean Absolute Error (MAE) of 1.39 compared to 1.73 for a baseline forecast for inflow to the Lake Kymmen river system 1-7 days ahead over the period 2015-2019. For inflow to the Lake Kymmen river system 8-14 days ahead the machine learning model achieved an average MAE of 1.68 compared to 2.45 for a baseline forecast. The current HBV model in place had a lower average MAE than the machine learning model over the available comparison period of January 2018.Tillrinningsprognostisering är viktig vid planeringen av vattenanvändningen i ett vattenkraftverk. Prognostiseringsprocessen går ut på att använda tidigare kunskap för att kunna göra prediktioner om framtiden. Traditionellt sett har tillrinningsprognostisering gjorts med hjälp av hydrologiska modeller. Hydrologiska Byråns Vattenbalansavdelning-modellen (HBV-modellen) är en av de mest använda hydrologiska modellerna och används världen över. Maskininlärning växer för tillfället fram som ett potentiellt alternativ till de nuvarande HBV-modellerna men behöver utvärderas. Det här examensarbetet använder en blandad kvalitativ och kvantitativ metod för att utforska maskininlärning för tillrinningsprognostisering i en fallstudie. Intervjuer med experter med olika bakgrund inom vattenkraft påtalade nyckelfrågor och möjligheter för precisering av tillrinningsprognostisering och lade grunden för den maskininlärningsmodell som skapades. Den här studien fann att brus i realiserade tillrinningsdata var en av huvudfaktorerna som påverkade kvaliteten i tillrinningsprognoserna av maskininlärningsmodellen. Andra nämnvärda faktorer var nederbördsdata från de tre närmaste väderstationerna. Intervjuerna antydde att bruset i realiserade tillrinningsdatana kan bero på felaktiga mätvärden. Intervjuerna bidrog också med exempel på ytterligare data som kan inkluderas i en maskininlärningsmodell för att förbättra tillrinningsprognoserna, såsom mätningar av snömängd och markvattennivåer. En föreslagen användning för maskininlärningsmodellen var som ett kompletterande verktyg till den nuvarande HBV-modellen för att underlätta manuella justeringar av prognoserna när det bedöms nödvändigt. Maskininlärningsmodellen åstadkom ett genomsnittligt Mean Absolute Error (MAE) på 1,39 jämfört med 1,73 för en referensprognos för tillrinningen till Kymmens sjösystem 1–7 dagar fram i tiden under perioden 2015–2019. För tillrinningen till Kymmens sjösystem 8–14 dagar fram i tiden åstadkom maskininlärningsmodellen ett genomsnittligt MAE på 1,68 jämfört med 2,45 för en referensprognos. Den nuvarande HBV-modellen hade ett lägre genomsnittligt MAE jämfört med maskininlärningsmodellen under den tillgängliga jämförelseperioden januari 2018.
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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 capacity and flow patterns are also discussed.
The operation of a hypothetical but typical project is modelled using stochastic dynamic
programming. A simple model of streamflow is formulated based on the historical statistics ( means and deviations).
The conclusions are: The inflow forecasts can improve the operational efficiency of the reservoir considerably because of the reduction in forecasting uncertainty. The maximum release constraints affect the additional expected values. The benefits from the forecasts increase as the discharge limits reduce. Flow predictions in the high flow season are most valuable when the runoff in that time period dominates the annual flow pattern. However flow predictions at other times of the year also have value.Applied Science, Faculty of
Civil Engineering, Department of
Graduate
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Silva, Henderson Amparado de Oliveira. "Power Map Explorer: uma ferramenta para visualização e previsão de vazões." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-11122007-140030/.
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A complexidade inerente ao processo de produção de energia apresenta um desafio aos especialistas quando estes se deparam com o dimensionamento e operação de sistemas de recursos hídricos. A produção energética de um sistema hidroelétrico depende fundamentalmente das séries de vazões afluentes às diversas usinas hidrelétricas do sistema. No entanto, a incerteza das vazões futuras e sua aleatoriedade são obstáculos que dificultam todo o planejamento da operação do sistema energético brasileiro. A inexistência de um software específico para análise de séries de vazões ocorridas nas usinas hidrelétricas, associada à importância desse tipo de dado no contexto energético, motivou a concepção de uma ferramenta gráafica para visualização e previsão desses dados. Acredita-se que a visualização desses dados por meio de representações apropriadas e altamente interativas possa promover hipóteses e revelar novas informações dos fenômenos associados a essas quantidades, melhorando a qualidade das decisões de planejamento do sistema energético. Este trabalho de mestrado apresenta em detalhes o sistema desenvolvido, chamado Power Map Explorer, e das técnicas nele implementadasThe complexity inherent to the process of energy production introduces a challenge to the experts when they are faced with dimension and operation of water resources systems. The energy production of a hidroeletric system depends on streamflow time series from hydroelectric plants located on different rivers of the system. However, the uncertainty and randomness of future streamflow series impose difficulties to the planning and operation of the brazilian energy system. The lack of a software suite to support the analysis of inflow series from hydroelectric plants, and the importance of this data in the energy context motivated the conception and implementation of a graphical tool to visualize and forecast this type data. The appropriate level of visualization and interaction with this type of data can spring new hypotheses and reveal new information, leading to performance improvement of the task of energetic planning. This work presents a software for visualization and forecast of inflow data series, the Power Map Explorer, in detail
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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 HidalgoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica
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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 sistema hidrotérmico brasileiro: a previsão de vazões. A ferramenta de suporte à avaliação dos modelos matemáticos é o SISPREV, gerenciador/executor de estudos de previsão de vazões desenvolvido na UNICAMP. Esta ferramenta permite ao usuário prever vazões diárias e mensais com base em modelos de Regressão Linear (RL) e Sistema de Inferência Neuro-Fuzzy (SINF). Avaliou-se a qualidade das previsões diárias e mensais dos modelos RL e SINF através da metodologia de mineração de dados Cross Industry Standard Process for Data Mining (CRISP-DM). A CRISP-DM é baseada em um modelo hierárquico de processos comumente usados na descoberta de conhecimento. Os resultados mostram que o modelo RL apresenta um desempenho melhor para previsões diárias e o modelo SINF para as previsões mensais. Além disso, o modelo RL tem a tendência a ter bom desempenho de previsão nas situações típicas de chuva-vazão, enquanto os melhores índices de desempenho do modelo SINF caem nas condições atípicas, em particular com a contemporaneidade de altas vazões e baixas precipitações
Abstract: This work presents a comparison between two different mathematical models developed to predict inflows to hydropower plants. The purpose is to explore the aspects that determine the quality of an important input variable for operation planning of the Brazilian hydrothermal system: the inflows forecasting. The tool that supports the evaluation of the mathematical models is called SISPREV. It is a manager/runner of inflows forecasting studies developed at UNICAMP. This tool allows the user to predict daily and monthly inflows based on Linear Regression (RL) models and Neuro-Fuzzy Inference System (SINF). In this thesis, was evaluated the quality of daily and monthly forecasts of RL and SINF models using the methodology Cross Industry Standard Process for Data Mining. CRISP-DM is used in the discovery of knowledge and based on a hierarchical process model. The results show that the RL model performs better for daily predictions and the SINF model for monthly predictions. Furthermore, the RL model tends to have better performance in typical situations of rainfall-inflow, while the best performance indices of the SINF model fall in atypical conditions, in particular with the simultaneous high inflow rates and low precipitation
Mestrado
Planejamento de Sistemas Energeticos
Mestre em Planejamento de Sistemas Energéticos
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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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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 forecasting of monthly inflow is undertaken using relationships with satellite precipitation estimates as well as reanalysis precipitation and temperature products. Second, mean summer inflows to reservoirs are conditioned on the tercile of preceding winter large scale climate modes (El Niño Southern Oscillation, North Atlantic Oscillation, or Indian Ocean Dipole). The transferability of both approaches is evaluated through implementation to a basin in Morocco. A methodology for operationalising seasonal forecasts of inflows to Nurek reservoir in Tajikistan is also presented. The statistical models outperformed the long-term average mean monthly inflows into Toktogul and Andijan reservoirs at lead times of 1-4 months using operationally available predictors. Stratifying models to forecast monthly inflows for only summer months (April-September) improved skill over long term average mean monthly inflows. Individual months Niño 3.4 during October-January were significantly (p < 0.01) correlated to following mean summer inflows Toktogul, Andijan and Nurek reservoirs during the period 1941-1980. Significant differences (p < 0.01) occurred in summer inflows into all reservoirs following opposing phases of winter Niño 3.4 during the period 1941-1980. Over the period 1941-2016 (1993-1999 missing), there exists only a 22% chance of positive summer inflow anomalies into Nurek reservoir following November-December La Niña conditions. Cross validated model skill assessed using the Heidke Hit Proportion outperforms chance, with a hit rate of 51-59% depending upon the period of record used. This climate mode forecasting approach could be extended to natural hazards (e.g. avalanches and mudflows) or to facilitate regional electricity hedging (between neighbouring countries experiencing reduced/increased demand). Further research is needed to evaluate the potential for forecasting winter energy demand, potentially reducing the impact of winter energy crises across the region.
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Zambelli, Monica de Souza. "Planejamento da operação energetica do sistema interligado nacional baseado em modelo de controle preditivo." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261137.
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Orientador: Secundino Soares FilhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: O planejamento da operação energética do Sistema Interligado Nacional (SIN) é uma tarefa complexa realizada por meio de uma cadeia de modelos de médio, curto e curtíssimo prazo acoplados entre si, cada um com considerações pertinentes à etapa que aborda. A proposta deste trabalho é apresentar uma alternativa para o planejamento da operação energética de médio prazo. Foi desenvolvida uma metodologia baseada em modelo de controle preditivo, abordando os aspectos estocásticos do problema de forma implícita pela utilização de valores esperados das vazões, e fazendo uso de um modelo determinístico de otimização a usinas individualizadas, que possibilita uma representação mais precisa do sistema hidrotérmico. A análise de desempenho é feita através de simulações da operação, considerando os parques hidrelétrico e termelétrico que compõem o SIN, com restrições operativas reais, em configuração dinâmica, com plano de expansão e a possibilidade de intercâmbio e importação de mercados vizinhos. Os resultados são comparados aos fornecidos pela metodologia em vigor no setor elétrico brasileiro, notadamente o modelo NEWAVE, que determina as decisões de geração por subsistema, e o modelo Suishi-O, que as desagrega por usinas individualizadas
Abstract: The long term hydrothermal scheduling of the Brazilian Integrated System (SIN) is a complex task solved by a chain of long, medium and short term coupled models, each one with considerations pertinent to the stage of operation that it deals with. The proposal of this work is to present an alternative for the long term hydrothermal scheduling. A methodology based on model predictive control was developed, implicitly handling stochastic aspects of the problem by the use of inflows expected values, and making use of a deterministic optimization model to obtain the optimal dispatch for individualized plants, what makes possible a more accurate representation of the hydrothermal system. The performance analysis is made through simulations of the operation, taking into consideration all the hydro and thermal plants that compose the SIN, with real operative constraints, in dynamic configuration, with its expansion plan and the possibility of interchange and importation from neighboring markets. The results are compared with those provided by the approach actually in use by the Brazilian electric sector, specifically the NEWAVE model, which defines the generation decisions for the subsystems, and the Suishi-O model, that disaggregates them for the individualized plants
Doutorado
Energia Eletrica
Doutor em Engenharia Elétrica
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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, was selected as the study site. The performance of these forecasting models, in terms of forecasting accuracy, efficiency of model development and adaptability for future prediction, were compared to one another. All models performed well during the dry season (inflow with low variability), while the non-linear ANNs were superior to other models in frontal rainy season and typhoon season (inflow with high variability). The performance of ANN models were hardly affected by the high degree of uncertainty and variability inherent to the rainy season. Stepwise selection was very helpful in identifying significant variables for regression models and ANNs. This procedure reduced ANN's size and greatly improved forecasting accuracy for ANN models. The impact of training data series, model architecture and network internal parameters on ANNs performances were also addressed in this study. The overall evaluation indicates that ANNs are an effective and robust tool for input-output mapping under more extreme and variable conditions. ANNs provide an alternative forecasting approach to conventional time series forecasting models for daily reservoir inflow prediction.
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Westra, Seth Pieter Civil & 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 average rainfall, which in turn has important implications for the design and operation of reservoirs for hydroelectricity generation, irrigation and municipal water supply. Understanding and accounting for this variability through well designed prediction systems is thus an important part of improving the planning, management and operation of complex water resources systems. This thesis outlines the application of two statistical techniques: wavelets and independent component analysis, to identify sources of hydrological variability, and then use this information to probabilistically generate multivariate seasonal forecasts or develop extended synthetic sequences of hydrological time series. The research is divided into four main parts. The first part outlines an application of the method of wavelets to analyse sources of Australian rainfall variability, and shows that there are coherent regions of variability in addition to the ENSO phenomenon that should be considered when developing seasonal forecasts. The second part examines the capability of three component extraction techniques: principal component analysis (PCA), Varimax and independent component analysis (ICA), in identifying and interpreting modes of variability in the global sea surface temperature dataset. The third part outlines a new technique that uses ICA to factorise multivariate reservoir inflow time series into a set of independent univariate time series, so that univariate methods can be used to develop multivariate synthetic sequences and probabilistic seasonal forecasts. Finally, the fourth part synthesises the previous three parts by demonstrating a wavelets- and correlation-based methodology for assessing sources of climate variability, and then using ICA to generate probabilistic multivariate seasonal forecasts of reservoir inflows that form part of Sydney's water supply system.
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Purdie, Jennifer Margaret. "Model Development for Seasonal Forecasting of Hydro Lake Inflows in the Upper Waitaki Basin, New Zealand." The University of Waikato, 2007. http://hdl.handle.net/10289/2643.
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Approximately 60% of New Zealand's electricity is produced from hydro generation. The Waitaki River catchment is located in the centre of the South Island of New Zealand, and produces 35-40% of New Zealand's electricity. Low inflow years in 1992 and 2001 resulted in the threat of power blackouts, and a national demand for electricity that is currently growing at 2 to 5% a year gives strong justification for better management of the hydro resource. Improved seasonal rainfall and inflow forecasts will result in the better management of the water used in hydro generation on a seasonal basis. Seasonal rainfall forecasting has been the focus of much international research in recent years, but seasonal inflow forecasting is in its relative infancy. Researchers have stated that key directions for both fields are to decrease the spatial scale of forecast products, and to tailor forecast products to end user needs, so as to provide more relevant and targeted forecasts, which will hopefully decrease the enormous socio-economic costs of climate fluctuations. This study calibrated several season ahead lake inflow and rainfall forecast models for the Waitaki river catchment, using statistical techniques to quantify relationships between land-ocean-atmosphere state variables and seasonally lagged inflows and rainfall. Techniques included principal components analysis and multiple linear regression, with cross-validation techniques applied to estimate model error. Many of both the continuous and discrete format models calibrated in this study predict anomalously wet and dry seasons better than random chance, and better than the long term mean as a predictor. 95% confidence limits around most model predictions in this study offer significant skill when compared with the range of all probable inflows (based on the 80 year recording history in the catchment). Models predicting winter Lake Pukaki inflows are those with the strongest predictive relationships in this study. Spring and summer predictions were generally less skilful than those for winter and autumn. Inflows could be predicted with some skill in winter and summer, but not rainfall, and rainfall could be predicted with some skill in autumn and spring, but not inflows. Models predicting inflows and rainfall for different seasons in this study use very different sets of predictor variables to accomplish their seasonal predictability. This may be related to the significant seasonal snow storage in the catchment, so that other factors such as temperature and the number of north-westerly storms may have a large part to play in the magnitude of inflows. Similarly, predicting the same dependent variable but for different seasons led to different contributing variables, leading to the conclusion that different wider physical causative mechanisms are behind the predictability in different seasons, and that they too should be studied separately in any future research. SST5 (sea surface temperature to the north of New Zealand) was found to have more relevance than any other predictor in predicting Waitaki river inflows and rainfall in any season. The models calibrated with SOI and IPO included as predictor variables were almost invariably worse in their predictive skill than those without, and the list of the most important predictor variables in all models did not include equatorial sea surface temperatures, sea level pressures, or 700hpa geopotential height variables. The conclusion from these findings is that equatorial ocean-atmosphere state variables do not have significant relationships with season ahead inflows and rainfall in the South Island of New Zealand. Seasonal climate forecasting on single catchment scale, and focussed to end user needs, is possible with some skill, at least in the South Island of New Zealand.
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Sseguya, Raymond. "Forecasting anomalies in time series data from online production environments." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166044.
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Anomaly detection on time series forecasts can be used by many industries in especially forewarning systems that can predict anomalies before they happen. Infor (Sweden) AB is software company that provides Enterprise Resource Planning cloud solutions. Infor is interested in predicting anomalies in their data and that is the motivation for this thesis work. The general idea is firstly to forecast the time series and then secondly detect and classify anomalies on the forecast. The first part is time series forecasting and the second part is anomaly detection and classification done on the forecasted values. In this thesis work, the time series forecasting to predict anomalous behaviour is done using two strategies namely the recursive strategy and the direct strategy. The recursive strategy includes two methods; AutoRegressive Integrated Moving Average and Neural Network AutoRegression. The direct strategy is done with ForecastML-eXtreme Gradient Boosting. Then the three methods are compared concerning performance of forecasting. The anomaly detection and classification is done by setting a decision rule based on a threshold. In this thesis work, since the true anomaly thresholds were not previously known, an arbitrary initial anomaly threshold is set by using a combination of statistical methods for outlier detection and then human judgement by the company commissioners. These statistical methods include Seasonal and Trend decomposition using Loess + InterQuartile Range, Twitter + InterQuartile Range and Twitter + GESD (Generalized Extreme Studentized Deviate). After defining what an anomaly threshold is in the usage context of Infor (Sweden) AB, then a decision rule is set and used to classify anomalies in time series forecasts. The results from comparing the classifications of the forecasts from the three time series forecasting methods are unfortunate and no recommendation is made concerning what model or algorithm to be used by Infor (Sweden) AB. However, the thesis work concludes by recommending other methods that can be tried in future research.
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Garda, Paula. "Essays on the macroeconomics of labor markets." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/119820.
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This thesis sheds light on several macroeconomic aspects of labor markets. The first chapter focuses on the impact of dual labor markets on human capital investment. Using a large dataset of the Spanish Social Security the wage losses of permanent and fixed term workers after displacement are analyzed. Results indicate that workers under permanent contracts accumulate a higher share of firm specific human capital than workers under fixed term contracts. The impact on aggregate productivity is analyzed using a calibrated model `a la Mortensen and Pissarides (1994) with endogenous investment in human capital and dual labor markets. The second chapter develops a model in order to explain cross countries differences in the cyclical fluctuations of informal employment for developing countries. The explanation can be found in institutional differences between the formal and informal sector. The third chapter proposes a model that uses the flows into and out of unemployment to forecast the unemployment rate. It shows why this model should outperform standard time series models, and quantifies empirically this contribution for several OECD countries.Esta tesis arroja luz sobre varios aspectos macroeconómicos de los mercados laborales. El primer capítulo se centra en el impacto de los mercados duales de trabajo sobre la inversión en capital humano. Usando una base de datos de la Seguridad Social española, se analizan las pérdidas salariales de los trabajadores permanentes y a plazo fijo tras cambiar de empleo. Los resultados indican que los trabajadores con contratos permanentes acumulan una mayor proporción de capital humano específico a la firma, que los trabajadores con contratos de duración determinada. El impacto sobre la productividad es analizado calibrando un modelo `a la Mortensen y Pissarides (1994) con inversión endógena en capital humano y mercado de trabajo dual. El segundo capítulo desarrolla un modelo para explicar las diferencias en las fluctuaciones cíclicas del empleo informal en los países en desarrollo. La explicación se basa en diferencias institucionales entre el sector formal e informal. En el tercer capítulo se propone un modelo que utiliza flujos de entrada y salida del desempleo para pronosticar la tasa de desempleo. Se analizan cuáles son las condiciones bajo las cuales este modelo tiene una performance superior a los modelos estándar de series de tiempo, y cuantifica empíricamente esta contribución para varios países de la OCDE
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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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碩士國立臺灣大學
土木工程學研究所
96
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 data. Then the improve BPNs can be efficiently trained with partial data. An application is conducted to demonstrate the superiority of the improved BPNs. Two kinds of BPN-based (the improved and the conventional BPN-based) reservoir inflow forecasting models are constructed and the comparison between the improved and the conventional BPN-based model is made. The results show that the performance of the improved BPN-based models is as good as that of the conventional BPN-based models, but the improved BPN-based models significantly required less training time than the conventional BPN-based models. As compared to the conventional BPN models, only about 50% of training time is required for the improved BPN-based models. The improved BPN-based models are recommended as an alternative to the existing models because of their efficiency.
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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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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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碩士國立臺灣大學
土木工程學研究所
97
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 results indicate that the SOLO model is more well-performed and efficient than the existing BPN-based models. To further improve the peak inflow forecasting, SOLO with data preprocessing named ISOLO is also proposed. The comparison between SOLO and ISOLO confirms the significant improvement in peak inflow forecasting. The proposed model is recommended as an alternative to the existing models. The proposed modeling technique is also expected to be useful to support reservoir operation systems.
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Saenz, A. Vivian, and 謝薇安. "Forecasting Tourist inflow using Google Trends data The case of Taiwan." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/s28r33.
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碩士國立政治大學
應用經濟與社會發展英語碩士學位學程(IMES)
106
Google Trend is an Online tool that provides information to daily and weekly data on the frequency of certain search keywords, objects, and phrases in a given time period. A number of studies showed that these data can be used to ‘'Nowcasting''and‘'Google Forecasting Econometrics'', it can be concluded that data on the Internet search can be used in predictive purposes in the wider area of economic activity. As many studies have already pointed out the value of data on the Internet searching for the purpose of predictions of tourist demand in the wider and narrower levels destinations, but also on businesses levels. The aim of this paper is to examine whether Google trend could be used to predict tourist arrival in Taiwan. For this study, January 2008 through December 2017 was used as the analyzed period. This study tries to build a forecasting model of visitors to Taiwan by incorporating Google Trend Search Keywords and statistics data from the Taiwan Tourism Bureau. We forecast 5 years in this paper 2018,2019, 2020, 2021,2022. Using forecasting performance of various vector autoregressive (VAR) models we found out that by incorporating the Google Trend Data Search,into autoregressive models improve the predictive ability of the model. The value of this study is the important value of the Big data nowadays and a better and costly way to analyze the consumer behavior.
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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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碩士國立成功大學
水利及海洋工程學系碩博士班
94
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 and monthly model and further comparing the accuracy among ten-day, monthly and daily model, (3) the combining of the modified long-term weather outlook and the continuous rainfall-runoff with different time steps to forecast the monthly and three-month inflows. The results reveal the continuous rainfall-runoff model has good performances on daily, ten-day and monthly flow simulation. The comparison shows that the daily time scale model has better performances than the ten-day and monthly one. Forecasted inflows by using different time scale model are compared with the historical average inflows. The comparison indicates that the proposed inflow forecasting system has better results in inflow forecasting. In conclusion, the daily rainfall-runoff model can get the better accuracy. Daily time scale can be easily used in different time scale forecasting data of Central Weather Bureau in the future. The inflow forecasting may support the reservoir management for operation decision and drought warning.
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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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碩士國立臺灣大學
土木工程學研究所
96
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 demonstrate these three advantages. The results indicate that the proposed SVM-based models are more well-performed, robust and efficient than the existing BPN-based models. In addition to using SVMs instead of BPNs, typhoon characteristics, which are seldom regarded as key input for inflow forecasting, are added to the proposed models to further improve the long lead-time forecasting during typhoon-warning periods. A comparison between models with and without typhoon characteristics is also presented to confirm that the addition of typhoon characteristics significantly improves the forecasting performance for long lead-time forecasting. In conclusion, the typhoon characteristics should be used as input to the reservoir inflow forecasting. The proposed SVM-based models are recommended as an alternative to the existing models because of their accuracy, robustness and efficiency. The proposed modeling technique is expected to be useful to improve the reservoir inflow forecasting.
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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.
Full textAbstract:
碩士國立成功大學
水利及海洋工程學系碩博士班
92
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 combines a continuous rainfall-runoff model with the long-term weather outlook provided by the Central Weather Bureau to forecast three ten-day ahead inflows. There are several tasks in the present study, including (1) developing a rainfall-runoff model based on ten-day time scale in order to match up the time scale of reservoir operation, (2) proposing a transforming method to correct the long-term weather outlook for the study area, (3) forecasting one to three ten-day ahead inflows of reservoir, and (4) developing a window-based long-term inflow forecasting system of reservoir to provide users with convenient operation. Model calibration and verification were performed from 28-year historical records, and the results reveal the continuous rainfall-runoff model has good performances on ten-day flow simulation. One to three ten-day ahead inflows forecasted in the study were compared with the historical average ten-day inflows, which are always chosen as a reference for reservoir classical operation. The comparison indicates that the proposed inflow forecasting system has better results for one to three ten-day inflow forecasting. Finally, we use the Visual-Basic 6.0 software coupled with Fortran software to develop a window-based long-term inflow forecasting system of reservoir.
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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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碩士國立臺灣大學
土木工程學研究所
106
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 the reservoir operator to make operational policies in advance during typhoons. For the aforementioned reasons, a new long lead-time reservoir inflow forecasting approach by means of forcing the rainfall-runoff model with ensemble precipitation forecasts is proposed to yield 1- to 72-h ahead reservoir inflow forecasts of the Shihmen reservoir during typhoons. The structure of this study is composed of three parts: First, a novel rainfall-runoff model which combines the HEC-HMS model with support vector machine is proposed. The proposed rainfall-runoff model is calibrated and validated with twelve typhoons during 2008 to 2011. Second, with ensemble quantitative precipitation forecasts from Taiwan Typhoon and Flood Research Institute being the meteorological forcing, the proposed rainfall-runoff model provides ensemble reservoir inflow forecasts for seven typhoons from 2012 to 2015. Third, the study integrates ensemble reservoir inflow forecasts using random forest. Taking Typhoon Soudelor in 2015 for example, results show that coupling HEC-HMS with SVM provides more reasonable ensemble distribution than using only HEC-HMS. Compare with the ensemble mean, reservoir inflow forecasts from random forest have less uncertainty and the advantage of extra lead time, particularly 48 hours to 72 hours. The proposed model could provide accurate 3-day reservoir inflow forecasts immediately after the Typhoon Soudelor warning was issued. The error of cumulative inflow was only 4.03%. According to the proposed approach, the authority may efficiently operate the reservoir and balance a trade-off between ‘gaining more flood buffer for dam security paying the expense of increasing shortage risk’ and ‘ensuring adequate water resources by enduring the potential of flooding damage’.
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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 that solves the hydrothermal scheduling problem.
The probabilistic model that governs the inflows is based on a dynamic linear model. Due to the cyclical behavior of the inflows, the model incorporates seasonal and regression components. We also incorporate climate variables such as precipitation, El Ni\~no, and other ocean indexes, as predictive variables when relevant.
The model is tested for the power generation system in Brazil with about 140 hydro plants, which are responsible for more than 80\% of the electricity generation in the country. At first, these plants are gathered by basin and classified into 15 groups. Each group has a different probabilistic model that describes its seasonality and specific characteristics.
The inflow forecast derived with the probabilistic model at each stage of the planning horizon is a continuous distribution, instead of a single point forecast. We describe an algorithm to form a finite scenario tree by sampling from the inflow forecasting distribution with interstage dependency, that is, the inflow realization at a specific stage depends on the inflow realization of previous stages.text
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Su, Jia-Huei, and 蘇嘉惠. "Time Series Models on Inflows Forecasting." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/75139261139649802641.
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碩士中原大學
土木工程研究所
98
Abstract In Taiwan,roughly 78% of its yearly rainfall concentrates in the summer and autumn. As the distribution of rainfall is extremely uneven and regional issues, If we can more predict the flow accurately,and it may be ues to estimate inflow for reservoir operator. In this paper, multivariate of time series autoregressive model(Auto-Regressive eXogeneous, called ARX)and multivariate autoregressive moving average model (Auto-Regressive Moving Average eXogeneous, referred to as ARMAX) to establish the relationship between flow and rainfall, for simulation and prediction.
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Liao, Heng-Yi, and 廖珩毅. "Application of Grey System and Fuzzy Theory on Ten-day Inflows Forecasting." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/45507861433906872175.
Full textAbstract:
碩士中原大學
土木工程研究所
89
Forecasting ten-day inflows is very important for reservoir operators. It would be a great help if the ten-day inflows can be estimated accurately. In Taiwan, roughly 78% of its yearly rainfall concentrates in the summer and autumn because of its particular climate and geographic characteristics. That is why forecasting ten-day inflows becomes a difficult work and hardly reaches satisfactory accuracy. The purpose of this paper is to forecast ten-day inflows of Shih-Men reservoir by using grey fuzzy system theory. The Differential Hydrological Grey Model (DHGM) and Grey Model GM(1,1) are both used to forecast ten-day inflows. Estimations by gray system theory would be compared with results by ARX model. The simulated results by using GM(1,1) can reached better accuracy, especially during the non-flood period. During the flood period, ten-day inflows are usually hard to estimate because there are so many disturbances caused by Typhoons. We expect the accuracy of gray system ten-day inflows forecasting will be significantly improved by modifying the estimation in use of fuzzy Typhoon membership degree that was established by the fuzzy theory and fuzzy decision making method. Four models, differential hydrological grey model (DHGM), Grey model (GM(1,1)), ARX model and Grey Fuzzy system model (GFSM) are all used to forecast ten-day inflows, respectively. The results shows that GFSM is a best model, and this paper may be used to estimate ten-day inflows for reservoir operators.
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Purdie, Jennifer. "Model development for seasonal forecasting of hydro lake inflows in the Upper Waitaki Basin, New Zealand /." 2005. http://adt.waikato.ac.nz/public/adt-uow20070223.140731/index.html.
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