Until recent times, weather forecasts were deterministic in nature. For example, a forecast might state ``The temperature tomorrow will be $20^\circ$C.'' More recently, however, increasing interest has been paid to the uncertainty associated with such predictions. By quantifying the uncertainty of a forecast, for example with a probability distribution, users can make risk-based decisions. The uncertainty in weather forecasts is typically based upon `ensemble forecasts'. Rather than issuing a single forecast from a numerical weather prediction (NWP) model, ensemble forecasts comprise multiple model runs that differ in either the model physics or initial conditions. Ideally, ensemble forecasts would provide a representative sample of the possible outcomes of the verifying observations. However, due to model biases and inadequate specification of initial conditions, ensemble forecasts are often biased and underdispersed. As a result, estimates of the most likely values of the verifying observations, and the associated forecast uncertainty, are often inaccurate. It is therefore necessary to correct, or post-process ensemble forecasts, using statistical models known as `ensemble post-processing methods'. To this end, this thesis is concerned with the application of statistical methodology in the field of probabilistic weather forecasting, and in particular ensemble post-processing. Using various datasets, we extend existing work and propose the novel use of statistical methodology to tackle several aspects of ensemble post-processing. Our novel contributions to the field are the following. In chapter~3 we present a comparison study for several post-processing methods, with a focus on probabilistic forecasts for extreme events. We find that the benefits of ensemble post-processing are larger for forecasts of extreme events, compared with forecasts of common events. We show that allowing flexible corrections to the biases in ensemble location is important for the forecasting of extreme events. In chapter~4 we tackle the complicated problem of post-processing ensemble forecasts without making distributional assumptions, to produce recalibrated ensemble forecasts without the intermediate step of specifying a probability forecast distribution. We propose a latent variable model, and make a novel application of measurement error models. We show in three case studies that our distribution-free method is competitive with a popular alternative that makes distributional assumptions. We suggest that our distribution-free method could serve as a useful baseline on which forecasters should seek to improve. In chapter~5 we address the subject of parameter uncertainty in ensemble post-processing. As in all parametric statistical models, the parameter estimates are subject to uncertainty. We approximate the distribution of model parameters by bootstrap resampling, and demonstrate improvements in forecast skill by incorporating this additional source of uncertainty in to out-of-sample probability forecasts. In chapter~6 we use model diagnostic tools to determine how specific post-processing models may be improved. We subsequently introduce bias correction schemes that move beyond the standard linear schemes employed in the literature and in practice, particularly in the case of correcting ensemble underdispersion. Finally, we illustrate the complicated problem of assessing the skill of ensemble forecasts whose members are dependent, or correlated. We show that dependent ensemble members can result in surprising conclusions when employing standard measures of forecast skill.

Orientador: Sérgio Valdir Bajay / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-16T12:23:43Z (GMT). No. of bitstreams: 1 Sharma_RobertaFerreiraCarrijo_M.pdf: 2600627 bytes, checksum: 8b69dde10e9ba16f17ca06e980e96bea (MD5) Previous issue date: 2009 / Resumo: O setor industrial brasileiro é responsável por cerca de 20% do Produto Interno Bruto e 35% do consumo energético nacional. Trata-se de um setor bastante heterogêneo, com inúmeras cadeias produtivas envolvendo diversos usos finais da energia. Projeções da demanda energética deste setor devem levar em conta esta heterogeneidade, através de uma desagregação adequada. Os modelos de projeção desta demanda também devem ser capazes de simular mudanças tecnológicas, impactos de programas de eficiência energética e o efeito de variáveis macroeconômicas. Neste trabalho, o setor industrial do País foi dividido em quatorze segmentos, que se caracterizam por serem energointensivos, ou possuírem forte participação no Produto Interno Bruto. Esta dissertação apresenta, inicialmente, uma ampla análise retrospectiva do desempenho econômico e do consumo energético destes segmentos industriais, utilizando vários parâmetros capazes de capturar as principais influências tecnológicas e econômicas neste consumo. Em seguida, são apresentadas projeções, a longo prazo, da demanda energética destes segmentos,utilizando o modelo de desagregação estrutural. Este modelo utiliza parâmetros levantados durante a análise retrospectiva e permite a simulação de rupturas dos padrões históricos das demandas energéticas a serem projetadas. Através do uso de cenários alternativos, foi possível explorar os impactos, nestas demandas, de diversas configurações de crescimento econômico e de políticas de conservação de energia. / Abstract: Brazilian industry is responsible for about 20% of the Gross Domestic Product and 35% of the national energy consumption. It is a very heterogeneous sector of the economy, with several productive chains involving various end-uses for energy. Energy demand forecasts for this industry should take into account this heterogeneity, through an adequate disaggregation. The forecasting models for this demand should also be able to simulate technological changes, the impacts of energy efficiency programs and the effect of macroeconomic variables. The industrial sector of the country was divided here into fourteen branches, which are either energy-intensive, or have a strong contribution to Gross Domestic Product. This thesis presents, initially, a broad retrospective analysis of the economic performance and energy consumption of these industrial branches, using several parameters capable to capture the main technological and economic influences in this consumption. Then, long-term forecasts of the energy demand of these branches are produced, using the structural disaggregation model. This model uses parameters obtained during the retrospective analysis and allows the simulation of ruptures in the historical pattern of the energy demands to be projected. Using alternative scenarios, it was possible to explore the impacts, in these demands, of several schemes of economic growth and energy conservation policies. / Mestrado / Planejamento de Sistemas Energeticos / Mestre em Planejamento de Sistemas Energéticos

Au cours des années récentes, les véhicules électriques sont revenus sur le devant de la scène des politiques publiques en matière de transport. Considérés comme un remède possible à diverses préoccupations pressantes des pouvoirs publics, ils bénéficient d'un soutien croissant de leur part. De telles mesures de soutien demeurent contestées : en effet, leur impact sur le décollage effectif des ventes, leur soutenabilité, leur utilité et leur justification sont loin d'aller de soi. Cette étude vise à éclairer l'impact des politiques publiques destinées à influencer la demande sur i) le taux de pénétration des véhicules électriques auprès des ménages français, et ii) les finances publiques. Dans un premier temps sera brossé le tableau du contexte dans lequel les véhicules électriques ont vocation à se développer. Il sera proposé un panorama large des opportunités potentielles offertes par l'introduction des véhicules électriques. Une revue internationale des politiques publiques est conduite, qui décrit les leviers de politique publique qui sont aujourd'hui actionnés en soutien au véhicule électrique de par le monde. L'accent y est mis sur les mesures destinées à agir sur la demande. Des conclusions préliminaires seront proposées sur l'efficacité de ces mesures au regard des taux observés de pénétration du véhicule électrique. Dans un deuxième temps, l'étude s'attache à évaluer le marché potentiel des véhicules électriques auprès des ménages français. L'analyse porte non seulement sur les déterminants financiers de la demande, mais aussi sur les obstacles socio-économiques à l'adoption des véhicules électriques par ces ménages. S'appuyant sur une analyse par scénarios qui permet de rendre compte des nombreuses incertitudes relatives aux évolutions à prévoir des véhicules, des coûts et des tendances de marché, une prévision du potentiel de demande à l'horizon 2023 est avancée. L'approche désagrégée qui est appliquée à partir de la base de données de l'Enquête Nationale Transports et Déplacements 2007/2008 permet d'identifier les combinaisons de instruments financiers de politique publique les plus à même de garantir certains niveaux de pénétration du véhicule électrique dans la prochaine décennie. Enfin, l'impact sur les finances publiques du remplacement d'un véhicule conventionnel par un véhicule électrique est étudié. L'analyse porte à la fois sur les phases de production et d'usage du véhicule. Le modèle d'évaluation développé à cet effet tient compte des impacts directs et indirects sur les finances publiques. Sont pris en compte les subventions directes à l'achat, les allègements fiscaux, les recettes fiscales, ainsi que les effets sur l'emploi. Les conclusions et observations tirées de l'étude permettent de formuler diverses suggestions à l'attention des constructeurs automobiles et des décideurs publics affichant la volonté de soutenir l'essor du véhicule électrique / In recent years, electric vehicles have come to the forefront of public transport policies. They are seen as remedy for various pressing public concerns and are thus increasingly benefiting from supportive policy measures. Such measures remain contested: their impact on actual vehicle uptake rates, their sustainability, usefulness and justification are far from being self-evident. This study aims at uncovering the effect of financial demand-side public policy measures on i) the uptake rate of electric vehicles among private households in France, and ii) the public budget. First, the context within which electric vehicles are to evolve is sketched. A comprehensive overview of the potential opportunities that come with the introduction of electric vehicles is given. An international policy review depicts public policy levers that are currently deployed in order to support the uptake of electric vehicles. A focus is put on financial demand-side measures. Preliminary conclusions on their effectiveness with regards to observed electric vehicle uptake rates in the various countries reviewed are drawn. Next, the potential market for electric vehicles among French households is explored. Besides financial aspects, socio-economic obstacles to electric vehicle uptake among private households are analysed. With the aid of scenario analysis that accounts for the many uncertainties with regards to future vehicle developments, costs and market trends, a forecast of the electric vehicles' potential up until 2023 is given. The applied disaggregate approach based on the database of the French National Transport Survey 2007/2008 allows identifying the most promising sets of financial public policy measures that are likely to guarantee certain electric vehicle uptake rates over the next decade. Lastly, the effect of replacing one conventional vehicle by one electric vehicle on the public budget is investigated. Both, vehicle manufacture and use aspects are considered. The set up valuation model hereby accounts for direct and indirect financial impacts on the public budget. These comprise direct purchase subsidies, tax breaks, and tax income, as well as effects of changing employment situations that alter the amount of social contributions and unemployment benefits .The study's findings and considerations allow for various suggestions for vehicle manufacturers and policy makers willing to support the uptake of electric vehicles. These are listed in the conclusions section which also sketches directions for further research

This bachelor thesis examines the part of the EU-project CoordiNet based on Vattenfall Distribution AB and their commitment in the project. The aim and purpose of CoordiNet are to find possible solutions on capacity shortage through coordination between customer -DSO -TSO achieving a more efficient usage of the electrical grid.  The report evaluates the outcomes of the implementation of the CoordiNet project, focusing on the demonstration in Uppsala. An investigation of the flexibility market used in CoordiNet is made with the aim to examine the impact on capacity shortage. Additionally the report looks into the profits of making flexibility calls and how accurate the load forecasting for the project has been and its impact on the result. All the calculations and evaluations are based on analyses from the available data sets. Statistical methods are used together with diagrams to make the evaluation possible.  The conclusion is that CoordiNet and its flexibility market in Uppsala has led to reduced subscription overruns, which in turn has an positive impact on both the capacity in the electrical grid and also costs regarding excess charges. The load forecasting model used in the project proved to be better and more stable than the previous model and it is neither markedly affected by temperature or energy prices. This CoordiNet demonstration is the first one in Uppsala and to a large extent it has been a test period. There will be further demonstrations in the future and this first demonstration is a great learning experience for upcoming projects.

<div>As on 2017, US Energy Information Administration (US EIA) claims that 50 % of the total US energy consumption are contributed by Commercial and Industrial (C&I) end-users.</div><div>Most of the energy consumption by these users is in the form of the electric power. Electric utilities, who usually supply the electric power, tend to care about the power consumption profiles of these users mainly because of the scale of consumption and their significant contribution</div><div>towards the system peak. Predicting and managing the peaks of C&I users is crucial both for the users themselves and for utility companies.</div><div>In this research, we aim to understand and predict the daily peaks of individual C&I users. To empirically understand the statistical characteristics of the peaks, we perform an extensive exploratory data analysis using a real power consumption time series dataset. To accurately predict the peaks, we investigate indirect and direct learning approaches. In the indirect approach, daily peaks are identified after forecasting the entire time series for the day whereas in the direct approach, the daily peaks are directly predicted based on the historical data available for different users during different days of the week. The machine learning models used in this research are based on Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN).</div>

Warren Buffet started an investment partnership of $100 in 1956 and has gone on to accumulate a personal net worth of over $60 billion. He started primarily as a value investor, and gradually changed over time to a strategy which uses the PEG ratio as its main tool. Peter Lynch, one of the most successful fund managers in history and had a compound annual growth rate of 29% for 13 years, was the man to first introduce the world to the PEG ratio. With such prominence, however, widespread use of previously successful strategies tend to render them ineffective due to everyone using them, and today the PEG ratio’s effectiveness as a valuation tool remains a topical debate between market commentators.This study sets out to determine if the PEG ratio adds value using JSE Main Board data from 2002 to 2012. Returns from five portfolios constructed directly from share quintiles based on PEG ratio magnitude are compared to returns of a portfolio constructed from the optimum quintile of value shares. The PEG ratio portfolio returns are examined based on 3 rebalancing period strategies, and on relative performance between the quintiles within each strategy.It is found that a 24 monthly rebalancing strategy provides superior returns to that of 3 or 12 monthly rebalancing for PEG quintiles of selected stocks. Furthermore, the lowest PEG ratio quintile in this strategy outperforms the value portfolio by a compound annual growth rate of 4.3%. The second lowest PEG ratio quintile portfolio performs slightly better to ensure that 40% of stocks selected based on the PEG ratio produced sustained superior returns to the optimum quintile value portfolio. / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

Probabilistic forecasts represent a potentially indispensable tool for policy advising, strategic planning, or provision of possible scenarios of future development. It is clear, however, that inaccurate forecasts can entail serious consequences. At best, unsuccessful forecasting attempts may discredit such potentially valuable method in the eyes of decision-making elites. At worst, wrong predictions may lead to the misallocation of scarce resources or to the unnecessary securitization. Nonetheless, probabilistic forecasts have seldom been used in the realm of the Czech security analyses, studies, or debates. Thus, the European Values Think- Tank's research project is a pioneering attempt to utilize the probabilistic forecasting in the Czech politico-security sphere. Due to the fact that the think-tank developed its probabilistic forecasts to help the Czech security elite with strategic planning, the thesis aims to verify the accuracy and predictive capabilities of the European Values. The broader goal is to bring, by the accuracy assessment, the rigor into the Czech probabilistic-forecasting debate. Additionally, the thesis also compares the predictive capabilities of the European Values with the alternative - foreign - forecasts, as well as with other means of accuracy verification. The results...

Earth observation data is increasingly ubiquitous, easily accessible, freely available, and generally usable due to improvements in software, data standards, network infrastructure, and national policies. As a result, greater opportunities arise for using these data in a wider field of application including decision support for local and regional environmental and water resources management efforts. In parts of the world where in situ data are less readily available, global Earth observation data used in such decision support tools can be a boon to underfunded government and private water management agencies. The United Nations Group on Earth Observations Global Water Sustainability initiative (GEOGloWS) works to coordinate such solutions, bringing global water management capabilities to local decision makers. The recent development and deployment of a global hydrologic modelling system based on historical simulations and daily ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) using Earth observations and streamflow routing on every river of the world results in a highly informative and potentially transformative dataset for users at local scales. However, for this data to reach its full potential at the local level, it needs to be subsetted at a regional or local scale, presented in a local geographic context, and interpreted in terms of local water management challenges. Furthermore, this subsetting allows for customization to support the way information is used and the kinds of decisions that are made. This paper presents the design, development, and experimental testing of the GEOGloWS HydroViewer, which is an open source, web-based software that effectively localizes global ECMWF forecasts to meet the needs of water managers and decision makers through subsetting the mapping and modelling services and supporting other customization as needed. The unique Software-as-a-Service (SaaS) deployment method, developed and tested here, allows for individual water management agencies to automatically generate custom HydroViewer applications that can be managed and/or customized depending on need and capacity in-country without reliance on external software and capacity, removing typical interdependence relationships that often define technology transfer to developing countries.

Utdelningspolitiska beslut representerar ett väsentligt ställningstagande för företag avseende kapitalallokering. Ekonomiska utsikter tenderar att föregå fluktuationer i den ekonomiska aktiviteten, som i slutändan samspelar med företags operationella verksamhet och förmåga att generera kapital. Informationen kan således vara betydelsefull på bolagsnivå för att bedöma om utdelningspolitiska beslut är genomförbara med hänsyn till framtida förväntningar. Studien undersöker empiriskt hur ekonomiska utsikter påverkar företags benägenhet att betala kontant utdelning och återköpa aktier i Sverige under perioden 2000–2019. Mer specifikt undersöks det med hjälp av att tillämpa en logistisk regressionsanalys för att beräkna sannolikheten att företag betalar kontant utdelning, höjer den kontanta utdelningen, sänker den kontanta utdelningen och återköper aktier när de ekonomiska utsikterna är positiva alternativt negativa. Ekonomiska utsikter baseras på data från Konjunkturinstitutets konjunkturbarometer. Finansiell bolagsdata och återköpsdata inhämtas från Thomson Reuters Eikon och Nasdaq. Resultatet indikerar att ekonomiska utsikter endast är positivt relaterat till svenska företags kontanta utdelningsökningar. / Economic outlook provides substantial information about future economic activity, information that could be used to decide whether payout policies will be implementable given future expectations. Dividend policy represents a major commitment from firm decision-makers regarding capital planning. Economic outlook predicts future economic activity, which ultimately interacts with firm’s future operational activities, and in the end, firm’s ability to generate future capital that could be used to fund payouts. This paper empirical examines how economic outlook affects the propensity for firms to pay cash dividends and repurchase shares using public available data from The National Institute of Economic Research in Sweden, financial statement data from Thomson Reuters and repurchase data from Nasdaq. Our results suggest that economic outlooks are positively related to dividend increase.

Air quality forecasts are of great value since several pollutants in our environment effect both human health, global climate, vegetation, crop yields, animals, materials and acidification of forests and lakes. Air-quality forecasts help to make people aware of the presence and the quantity of pollutants, and give them a chance to protect themselves, their business and the Earth. Many different air-quality models are in daily use all over the world, providing citizens with forecasts of air quality and warnings of unhealthy air quality if recommended highest concentrations are exceeded. This study adapts the WRF meteorological model (Weather research and Forecasting model) to be a driver of the CMAQ air-quality model (models-3 Community Multiscale Air Quality model). Forecasts of ozone and nitrogen dioxide concentrations from this coupled WRF/CMAQ modelling system are tested against observed data during a four-day period in May, 2006. The Lower Fraser Valley study area is a fertile valley surrounded by mountain chains in southwest British Columbia, Canada. The valley stretches from the Pacific coast eastwards towards the Rocky Mountains. This valley hosts more than 2 million people and it is west Canada’s fastest growing region. The Lower Fraser Valley holds a big city, Vancouver, several suburbs, numerous industries and a widespread agricultural production. During the analysed four-day period in May, a synoptic high-pressure built over the region, favoring high concentrations of pollutants as ozone and nitrogen dioxide. The created WRF/CMAQ model forecasted an acceptable magnitude of nitrogen dioxide but the daily variations are not recreated properly by the model. The WRF/CMAQ model forecasts the daily variation of ozone in a satisfying way, but the forecasted concentrations are overestimated by between 20 and 30 ppb throughout the study. Factors that could contribute to the elevated ozone concentrations were investigated, and it was found that the weather forecasting model WRF was not generating fully reliable meteorological values, which in turn hurt the air-quality forecasts. As the WRF model usually is a good weather forecasting model, the short spin-up time for the model could be a probable cause for its poor performance. / Prognoser över luftkvaliteten är mycket värdefulla, då flera luftföroreningar i vår närmiljö påverkar människans hälsa, det globala klimatet, vegetation, djur, material och bidrar till försurning av skog och vattendrag. Luftkvalitetsprognoser gör människan mer medveten om närvaron av luftföroreningar och i vilken mängd de finns. De ger människan en chans att vidta skyddsåtgärder för att skydda sig själv, sitt eventuella levebröd, och Jorden. Många olika luftkvalitetsmodeller används idag dagligdags över hela världen och förser invånare med prognoser för luftkvaliteten och varningar om koncentrationerna av föroreningar överstiger rekommenderade värden. I denna studie används väderprognosmodellen WRF (Weather Research and Forecasting model) för att driva luftkvalitetsmodellen CMAQ (models-3 Community Multiscale Air Quality model). Prognoser av ozon- och kvävedioxidhalterna i luften från den kopplade WRF/CMAQ modellen analyseras mot observerade data under en fyra dagars period i maj, 2006. Studieområdet Lower Fraser Valley är en bördig dalgång som är omgiven av bergskedjor i sydvästra British Columbia, Kanada. Dalen sträcker sig från Stilla havskusten och österut mot Klippiga bergen. I denna dalgång bor mer än 2 miljoner människor och det är västra Kanadas snabbast växande region. Lower Fraser Valley rymmer en storstad, Vancouver, flera förorter, många industrier och även stora jordbruksområden. Den fyra dagars period i maj som analyseras karaktäriseras av ett högtrycksbetonat synoptiskt väderläge med lokala variationer, vilka tillsammans är gynnsamma för att uppmäta höga koncentrationer av luftföroreningar som ozon och kvävedioxid. Den skapade WRF/CMAQ modellen prognostiserar godtagbar magnitud hos kvävedioxid men den dagliga variationen återskapas inte av modellen. Modellen prognostiserar den dagliga variationen av ozonkoncentration på ett tillfredsställande sätt, men storleksmässigt ligger koncentrationerna en faktor 20-30 ppb för högt rakt av under hela studien. Kringliggande faktorer som kan påverka koncentrationen ozon studeras närmare och det framkommer att den meteorologiska prognosmodellen WRF inte genererar fullt tillförlitliga värden för en rättvisande luftkvalitetsprognos. Då WRF modellen vanligtvis är en bra prognosmodell kan den korta initialiseringstiden för modellen vara en trolig orsak till dess otillräckliga prestation.