In this thesis, we will study issues related to prediction problems and put an emphasis on those arising when recurrent events are involved. First we define the basic concepts of frequentist and Bayesian statistical prediction in the first chapter. In the second chapter, we study frequentist prediction intervals and their associated predictive distributions. We will then present an approach based on asymptotically uniform pivotals that is shown to dominate the plug-in approach under certain conditions. The following three chapters consider the prediction of recurrent events. The third chapter presents different prediction models when these events can be modeled using homogeneous Poisson processes. Amongst these models, those using random effects are shown to possess interesting features. In the fourth chapter, the time homogeneity assumption is relaxed and we present prediction models for non-homogeneous Poisson processes. The behavior of these models is then studied for prediction problems with a finite horizon. In the fifth chapter, we apply the concepts discussed previously to a warranty dataset coming from the automobile industry. The number of processes in this dataset being very large, we focus on methods providing computationally rapid prediction intervals. Finally, we discuss the possibilities of future research in the last chapter.

Annual Average Daily Traffic (AADT) data in the transportation industry today is an important tool used in various fields such as highway planning, pavement design, traffic safety, transport operations, and policy-making/analyses. Systematic literature review was used to identify the current methods of estimating AADT and ranked. Ordinary linear kriging occurred most. Also, factors that influence the accuracy of AADT estimation methods as identified include geographical location and road type amongst others. In addition, further analysis was carried out to determine the most apposite kriging algorithm for AADT data. Three linear (universal, ordinary, and simple), three nonlinear (disjunctive, probability, and indicator) and bayesian (empirical bayesian) kriging methods were compared. Spherical and exponential models were employed as the experimental variograms to aid the spatial interpolation and cross-validation. Statistical measures of correctness (mean prediction and root-mean-square errors) were used to compare the kriging algorithms. Empirical bayesian with exponential model yielded the best result.

Spatial analyses of shoreline recession and accretion, and future shoreline position predictions in coastal countries have considerable importance due to engineering, planning, management and environmental concerns. In spite of this importance, there are only a few studies in Turkey. The aim of this thesis are to determine the shoreline rate-of-change of B&uuml / y&uuml / k Menderes Delta, by geographical information systems for the last fifty-year period, in order to approximate future shoreline position of B&uuml / y&uuml / k Menderes Delta shoreline, and to evaluate appropriate models while predicting the future shoreline position. To achieve the purpose of the study time series shoreline position data is extracted from three sets of topographic maps belonging to 1954-1957, 1977-1978 and 1993 aerial photographs and two sets of high resolution satellite imageries (January 2002 Ikonos, August 2004 QuickBird). Then Coastal script of TNTMips, which uses some statistical shoreline analyses methods, that are End Point Rate (EPR), Average of Rates (AOR), Linear Regression (LR) and Jackknifing (JK) is edited so that it can locate the future shoreline positions on the map. Suitable baselines are created and appropriate transect intervals are decided to analyze the shoreline. Finally, some additional analyses that are Backward Analysis and Oscillation Analysis are done to obtain most suitable future shoreline position with rate-of-changes. The results showed that, shorelines having different geomorphologic characteristics needed to be analyzed separately and the linear methods to model the future shoreline position differ from one geomorphologic region to another.

Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-06-14T13:10:24Z No. of bitstreams: 1 danielmoraisdesouza.pdf: 1277495 bytes, checksum: 7c2517a5b98a6f70aa787d954cd0c84a (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-06-27T15:04:32Z (GMT) No. of bitstreams: 1 danielmoraisdesouza.pdf: 1277495 bytes, checksum: 7c2517a5b98a6f70aa787d954cd0c84a (MD5) / Made available in DSpace on 2018-06-27T15:04:32Z (GMT). No. of bitstreams: 1 danielmoraisdesouza.pdf: 1277495 bytes, checksum: 7c2517a5b98a6f70aa787d954cd0c84a (MD5) Previous issue date: 2018-02-19 / Eletricidade é um insumo de uso generalizado nas economias modernas, penetrando nas mais variadas atividades produtivas e de consumo na sociedade. No entanto, as dificuldades de armazenamento em larga escala dessa forma de energia fazem com que a eletricidade seja muito sensível às condições de oferta, a ponto de que problemas de abastecimento rapidamente se convertem em apagões. Dentre vários dispositivos implementados na re-estrutuação do setor elétrico brasileiro (SEB) ao longo dos últimos 17 anos, estão sistemas de previsão de médio e longo-prazos usados por parte dos agentes públicos e privados do setor para reduzir as incertezas dos processos de abastecimento e expansão. A ANEEL chegou a recomendar na NT 292/2008-SER o uso de três metodologias multivariadas alternativas nesses sistemas de previsão, a saber: modelos VAR e VCE, modelos autorregressivos com defasagens distribuídas (ARDL) e modelos estruturais a espaço de estados. A literatura especializada, em que pese a presença de vários estudos propondo modelos de previsão do consumo de eletricidade para os três segmentos residencial, industrial e comercial, apresenta majoritariamente modelos de tipo VAR e VCE. Este estudo atualiza a literatura no que concerne ao uso de modelos VAR e VCE e ao mesmo tempo os compara em termos preditivos com os modelos ARDL e estruturais a espaço de estados. Os resultados encontrados na análise do desempenho preditivo dos modelos mostraram que para o segmento residencial, o modelo com melhor capacidade preditivo foi o modelo estrutural, enquanto que para o segmento comercial foi o modelo VCE e, para o segmento industrial, foi o modelo ARDL. Previsões de 2014 a 2025 foram feitas com o intuito de informar ao mercado brasileiro a demanda de energia para cada segmento. Foram usadas bases de dados disponíveis e atualizadas provenientes das mesmas fontes usadas nos estudos da literatura. / Electricity is an input of widespread use in modern economies, penetrating in the most varied productive and consumption activities in society. However, the difficulties of large-scale storage make electricity very sensitive to supply conditions, to the point that supply problems quickly turns into blackouts. Among several devices implemented in the re-structuring of the Brazilian electricity sector (SEB) over the last 17 years, medium and long-term forecasting systems are used by public and private sector agents to reduce the uncertainties of the supply processes and expansion. ANEEL recommend in NT 292/2008-SER the use of three alternative multivariate methodologies in these prediction systems, namely: VAR and VCE models, autoregressive models with distributed lags (ARDL), and state space structural models. The specialized literature, despite the presence of several studies proposing models of prediction of the consumption of electricity for the three residential, industrial and commercial segments, mainly presents models of type VAR and VCE. This study updates the literature regarding the use of VAR and VCE models and at the same time compares them in predictive terms with the ARDL and structural state space models. The results found in the predictive model analysis showed that for the residential segment, the model with the best predictive capacity was the structural model, while for the commercial segment it was the VCE model and, for the industrial segment, it was the ARDL model. Forecasts from 2014 to 2025 were made with the intention of informing the Brazilian market the energy demand for each segment. Available and updated databases from the same sources used in literature studies were used.

This master’s thesis deals with protein secondary structure prediction. There is a theoretical introduction followed by study of available tools, proposal and implementation of web application, which combines functionality of several web tools used to predict secondary structure. User is asked to choose prediction methods and insert input sequence as plain text or upload a file. Results collected from selected tools serve to convert data into common format, show the result and create new type of prediction. Finally, the testing is applied and influences of tools are adjusted in order to increase percentage of prediction. The output of application is a result of prediction also available as plain text or as a file.

The battery energy storage system (BESS) is crucial for the energy transition and decarbonisation of the energy sector. However, reliability assessment and capital cost challenges can hinder their widespread deployment. Reliability and cost-benefit analysis help address these challenges and assess BESS adoption's feasibility and viability, which is the aim of this project. A BESS contains various components such as battery packs, inverters, a DC/DC converter, a Battery Thermal Management System (BTMS), electrical protection devices, a transformer, and an Energy Management System (EMS). All these fundamental components must be considered to obtain a complete reliability prediction. Most previous studies focused on the reliability analysis of individual components, but few consider all the abovementioned components in collective reliability analysis. In this thesis, each component is mathematically modelled to estimate failure rates and then used to predict the reliability of the overall BESS system. The model accuracy is verified by comparing the computed reliability indices with the values from standards/references, showing that the proposed reliability prediction methods provide reasonable outcomes. Different scenarios to enhance BESS reliability through component redundancy are explored in this project. It is proved that applying component redundancy can boost the overall BESS reliability at the price of an increased capital cost. However, the enhancement in reliability and lifespan due to component redundancy can also curtail maintenance costs. A cost-benefit analysis assesses each scenario's profitability, considering manufacturers' and owners' perspectives. It helps determine the optimal balance between reliability and profitability. Redundancy applied to components with higher failure rates and lower costs improves the reliability and profitability of the BESS. The finding highlights the importance of strategic component selection for enhancing BESS reliability. Careful reliability and cost analysis should be performed simultaneously to find the most optimised BESS scenario. / Batterienergilagringssystemet (BESS) är avgörande för energiomställningen och avkarboniseringen av energisektorn. Tillförlitlighetsbedömning och utmaningar med kapitalkostnader kan dock hindra deras utbredda användning. Tillförlitlighet och kostnads-nyttoanalys hjälper till att hantera dessa utmaningar och utvärdera BESS-antagandets genomförbarhet och genomförbarhet, vilket är syftet med detta projekt. Ett BESS innehåller olika komponenter som batteripaket, växelriktare, en DC/DC-omvandlare, ett Battery Thermal Management System (BTMS), elektriska skyddsanordningar, en transformator och ett energiledningssystem (EMS). Alla dessa grundläggande komponenter måste beaktas för att få en fullständig tillförlitlighetsförutsägelse. De flesta tidigare studier fokuserade på tillförlitlighetsanalys av enskilda komponenter, men få beaktar alla ovan nämnda komponenter i kollektiv tillförlitlighetsanalys. I denna avhandling modelleras varje komponent matematiskt för att uppskatta felfrekvensen och används sedan för att förutsäga tillförlitligheten hos det övergripande BESS-systemet. Modellens noggrannhet verifieras genom att jämföra de beräknade tillförlitlighetsindexen med värdena från standarder/referenser, vilket visar att de föreslagna metoderna för tillförlitlighetsprediktion ger rimliga resultat. Olika scenarier för att förbättra BESS-tillförlitligheten genom komponentredundans utforskas i detta projekt. Det är bevisat att tillämpning av komponentredundans kan öka den övergripande BESS-tillförlitligheten till priset av en ökad kapitalkostnad. Förbättringen av tillförlitlighet och livslängd på grund av komponentredundans kan dock också minska underhållskostnaderna. En kostnads-nyttoanalys bedömer varje scenarios lönsamhet, med hänsyn till tillverkarnas och ägarnas perspektiv. Det hjälper till att bestämma den optimala balansen mellan tillförlitlighet och lönsamhet. Redundans som tillämpas på komponenter med högre felfrekvens och lägre kostnader förbättrar tillförlitligheten och lönsamheten för BESS. Resultatet belyser vikten av strategiskt komponentval för att förbättra BESS-tillförlitligheten. Noggrann tillförlitlighets- och kostnadsanalys bör utföras samtidigt för att hitta det mest optimerade BESS-scenariot.

Noise caused by road and rail traffic is an environmental issue that is receiving increasing attention. It has long been known that prolonged exposure to noise can lead to a range of health problems, but in recent years, it has also been shown to have a negative impact on biodiversity. In Sweden, the Nordic calculation methods (Nord96) are currently used to assess noise levels generated by road and rail traffic. However, there is a consensus among acousticians that this method is outdated. In Sweden, it has been decided to transition to the more modern Nord2000 method in 2024. Therefore, it is essential to compare the Nordic calculation method with Nord2000 to determine any potential consequences. The responsibility for this transition has been assigned to Kunskapcentrum om buller which has developed a user manual aimed at ensuring that the calculation comparisons provide results as accurately comparable as possible. The aim of this thesis was to identify any shortcomings from an implementation perspective in the developed user manual and to compare the two calculation methods, Nord96 and Nord2000, for road and rail traffic. Twelve scenarios were constructed in the noise modeling program SoundPLAN 9.0 to study potential differences. Furthermore, calculations were made for a real case in an area in Farsta, Stockholm. Findings showed differences in the calculated sound levels between the two calculation methods for road and rail traffic. The results suggest that a transition may lead to it being more challenging to meet guideline values at outdoor areas in shielded locations for road traffic and at facades facing the noise source for rail traffic. Furthermore, several points of improvement regarding the developed user manual were identified. It was concluded that values for input parameters such as road surface temperature and traffic flow distribution should be clarified, and more information on the selection of weather parameters is needed. / I takt med urbaniseringen utsätts fler personer för högre bullernivåer orsakat av väg- och spårtrafik. Det har länge varit känt att långvarig exponering av buller kan leda till en rad olika hälsoproblem, men har även på senare år även visats ha negativ påverkan på biodiversitet. För att kartlägga bullernivåer alstrat av väg- och spårtrafik används i Sverige idag de Nordiska beräkningsmetoderna (Nord96). Det råder dock en konsensus bland akustiker att metoden är utdaterad. I Sverige planeras det att genomföra ett skifte till den mer sofistikerade metoden Nord2000 under år 2024. Det är därmed väsentligt med en jämförelse mellan de Nordiska beräkningsmetoderna och Nord2000 för att utröna eventuella konsekvenser ett skulle kunna medföra. Ansvariga för detta skifte är Kunskapcentrum om buller som tagit fram en användarhandledning som ämnar till att beräkningsjämförelser ger så rättvisande resultat som möjligt. Syftet med detta examensarbete har varit att identifiera eventuella brister utifrån ett genomförandeperspektiv i den framtagna användarhandledningen samt att jämföra de två beräkningsmetoderna Nord96 och Nord2000, för väg- och spårtrafik. Tolv typfall konstruerades med hjälp av beräkningsprogrammet SoundPLAN 9.0 för att studera eventuella skillnader. Vidare gjordes beräkningar för ett verkligt fall för ett område i Farsta, Stockholm. Resultatet visade på differenser i beräknad ljudnivå mellan de två beräkningsmetoderna för väg- och spårtrafik. Resultatet tyder på att ett skifte kan medföra att riktvärden vid uteplatser i skärmade områden för vägtrafik samt vid fasad i riktning mot ljudkälla för spårtrafik kan bli svårare att uppfylla. Vidare kunde ett antal förbättringsmöjligheter med avseende på den framtagna användarhandledningen identifieras. Studien visar att värden på inparametrar för vägytans temperatur och fördelning i trafikflödet bör klargöras samt att mer underlag för valet av värden på väderparametrar efterfrågas.

O objetivo deste trabalho e o desenvolvimento de uma ferramenta estatistica que sirva de base para o estudo do preco spot da energia eletrica do subsistema Sudeste/Centro-Oeste do Sistema Interligado Nacional, utilizando a estimacao por regressao linear e teste de razao de verossimilhanca como instrumentos para desenvolvimento e avaliacao dos modelos. Na analise dos resultados estatsticos descritivos dos modelos, diferentemente do que e observado na literatura, a primeira conclusao e a verificacao de que as variaveis sazonais, quando analisadas isoladamente, apresentam resultados pouco aderentes ao preco spot PLD. Apos a analise da componente sazonal e verificada a influencia da energia fornecida e a energia demandada como variaveis de entrada, com o qual conclui-se que especificamente a energia armazenada e producao de energia termeletrica sao as variaveis que mais influenciam os precos spot no subsistema estudado. Entre os modelos testados, o que particularmente ofereceu os melhores resultados foi um modelo misto criado a partir da escolha das melhores variaveis de entrada dos modelos testados preliminarmente, alcancando um coeficiente de determinacao R2 de 0.825, resultado esse que pode ser considerado aderente ao preco spot. No ultimo capitulo e apresentada uma introducao ao modelo de predicao do preco spot, possibilitando dessa forma a analise do comportamento do preco a partir da alteracao das variaveis de entrada. / The objective of this work is the development of a statistical method to study the spot prices of the electrical energy of the Southeast/Middle-West (SE-CO) subsystem of the The Brazilian National Connected System, using the Least Squares Estimation and Likelihood Ratio Test as tools to perform and evaluate the models. Verifying the descriptive statistical results of the models, differently from what is observed in the literature, the first observation is that the seasonal component, when analyzed alone, presented results loosely adherent to the spot price PLD. It is then evaluated the influence of the energy supply and the energy demand as input variables, verifying that specifically the stored water and the thermoelectric power production are the variables that the most influence the spot prices in the studied subsystem. Among the models, the one that offered the best result was a mixed model created from the selection of the best input variables of the preliminarily tested models, achieving a coeficient of determination R2 of 0.825, a result that can be considered adherent to the spot price. At the last part of the work It is presented an introduction to the spot price prediction model, allowing the analysis of the price behavior by the changing of the input variables.

This thesis addresses spatial interpolation and temporal prediction using air pollution data by several space-time modelling approaches. Firstly, we implement the dynamic linear modelling (DLM) approach in spatial interpolation and find various potential problems with that approach. We develop software to implement our approach. Secondly, we implement a Bayesian spatial prediction (BSP) approach to model spatio-temporal ground-level ozone fields and compare the accuracy of that approach with that of the DLM. Thirdly, we develop a Bayesian version empirical orthogonal function (EOF) method to incorporate the uncertainties due to temporally varying spatial process, and the spatial variations at broad- and fine- scale. Finally, we extend the BSP into the DLM framework to develop a unified Bayesian spatio-temporal model for univariate and multivariate responses. The result generalizes a number of current approaches in this field.

This thesis addresses spatial interpolation and temporal prediction using air pollution data by several space-time modelling approaches. Firstly, we implement the dynamic linear modelling (DLM) approach in spatial interpolation and find various potential problems with that approach. We develop software to implement our approach. Secondly, we implement a Bayesian spatial prediction (BSP) approach to model spatio-temporal ground-level ozone fields and compare the accuracy of that approach with that of the DLM. Thirdly, we develop a Bayesian version empirical orthogonal function (EOF) method to incorporate the uncertainties due to temporally varying spatial process, and the spatial variations at broad- and fine- scale. Finally, we extend the BSP into the DLM framework to develop a unified Bayesian spatio-temporal model for univariate and multivariate responses. The result generalizes a number of current approaches in this field.