Diversas atividades humanas são fortemente dependentes do clima e da sua variabilidade, especialmente aquelas relacionadas ao uso da água. A operação integrada de reservatórios com múltiplos usos requer uma série de decisões que definem quanta água deve ser alocada, ao longo do tempo para cada um dos usos, e quais os volumes dos reservatórios a serem mantidos. O conhecimento antecipado das condições climáticas resulta de vital importância para os operadores de reservatórios, pois o insumo dos reservatórios é a vazão dos rios, que por sua vez é dependente de condições atmosféricas e hidrológicas em diferentes escalas de tempo e espaço. A pesquisa trata sobre três importantes elementos de subsídio à tomada de decisão na operação de reservatórios baseada na previsão de variáveis hidrológicas: (a) as previsões de vazão de curto prazo; (b) as previsões de precipitação de longo prazo e (c) as medidas de desempenho das previsões. O reservatório de Furnas, localizado na bacia do Rio Grande, em Minas Gerais, foi selecionado como estudo de caso devido, principalmente, à disponibilidade de previsões quantitativas de chuva e pela importância desse reservatório na região analisada. A previsão de curto prazo de vazão com base na precipitação foi estimada com um modelo empírico (rede neural artificial) e a previsão de precipitação foi obtida pelo modelo regional ETA. Uma metodologia de treinamento e validação da rede neural artificial foi desenvolvida utilizando previsões perfeitas de chuva (considerando a chuva observada como previsão) e utilizando o maior número de dados disponíveis, favorecendo a representatividade dos resultados obtidos. A metodologia empírica alcançou os desempenhos obtidos com um modelo hidrológico conceitual, mostrando-se menos sensitiva aos erros na previsão quantitativa de precipitação nessa bacia. Os resultados obtidos mostraram que as previsões de vazão utilizando modelos empíricos e conceituais e incorporando previsões quantitativas de precipitação são melhores que a metodologia utilizada pelo ONS no local de estudo. A redução dos erros de previsão relativos à metodologia empregada pelo ONS foi em torno de 20% quando usadas previsões quantitativas de precipitação definidas pelo modelo regional ETA e superiores a 50% quando usadas previsões perfeitas de precipitação. Embora essas últimas previsões nunca possam ser obtidas na prática, os resultados sugerem o quanto o incremento do desempenho das previsões quantitativas de chuva melhoraria as previsões de vazão. A previsão de precipitação de longo prazo para a bacia analisada foi também estimada com um modelo empírico de redes neurais artificiais e utilizando índices climáticos como variáveis de entrada. Nesse sentido, foram estimadas previsões de precipitação acumulada no período mais chuvoso (DJF) utilizando índices climáticos associados a fenômenos climáticos, como o El Niño - Oscilação Sul e a Oscilação Decadal do Pacífico, e a modos de variabilidade climática, como a Oscilação do Atlântico Norte e o Modo Anular do Hemisfério Sul. Apesar das redes neurais artificiais terem sido aplicadas em diversos problemas relacionados a hidrometeorologia, a aplicação dessas técnicas na previsão de precipitação de longo prazo é ainda rara. Os resultados obtidos nesse trabalho mostraram que consideráveis reduções dos erros da previsão relativos ao uso apenas da média climatológica como previsão podem ser obtidos com a metodologia utilizada. Foram obtidas reduções dos erros de, no mínimo 50%, e chegando até um valor próximo a 75% nos diferentes testes efetuados no estudo de caso. Uma medida de desempenho da previsão foi desenvolvida baseada no uso de tabelas de contingência e levando em conta a utilidade da previsão. Essa medida de desempenho foi calculada com base nos resultados do uso das previsões por um modelo de operação de reservatório, e não apenas na comparação de vazões previstas e observadas. Nos testes realizados durante essa pesquisa, ficou evidente que não existe uma relação unívoca entre qualidade das previsões e utilidade das previsões. No entanto, em função de comportamentos particulares das previsões, tendências foram encontradas, como por exemplo nos modelos cuja previsão apresenta apenas defasagem. Nesses modelos, a utilidade das previsões tende a crescer na medida que a qualidade das mesmas aumenta. Por fim, uma das grandes virtudes da medida de desempenho desenvolvida nesse trabalho foi sua capacidade de distinguir o desempenho de modelos que apresentaram a mesma qualidade. / Several human activities are strongly dependent on climate and its variability, especially those related to water use. The operation of multi-purpose reservoirs systems defines how much water should be allocated and the reservoir storage volumes to be maintained, over time. Knowing in advance the weather conditions helps the decision making process, as the major inputs to reservoirs are the streamflows, which are dependent on atmospheric and hydrological conditions at different time-space scales. This research deals with three important aspects towards the decision making process of multi-purpose reservoir operation based on forecast of hydrological variables: (a) short-term streamflow forecast, (b) long-range precipitation forecast and (c) performance measures. The Furnas reservoir on the Rio Grande basin was selected as the case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generation system. Short-term streamflow forecasts were estimated by an empirical model (artificial neural network – ANN) and incorporating forecast of rainfall. Quantitative precipitation forecasts (QPFs), defined by the ETA regional model, were used as inputs to the ANN models. A methodology for training and validating the ANN models was developed using perfect precipitation forecasts (i.e., using the observed precipitation as if it was a forecast) and considering the largest number of available samples, in order to increase the representativeness of the results. The empirical methodology achieved the performance obtained with a conceptual hydrological model and seemed to be less sensitive to precipitation forecast error relative to the conceptual hydrological model. Although limited to one reservoir, the results obtained show that streamflow forecasting using empirical and conceptual models and incorporating QPFs performs better than the methodology used by ONS. Reduction in the forecast errors relative to the ONS method was about 20% when using QPFs provided by ETA model, and greater than 50% when using the perfect precipitation forecast. Although the latter can never be achieved in practice, these results suggest that improving QPFs would lead to better forecasts of reservoir inflows. Long-range precipitation forecast was also estimated by an empirical model based on artificial neural networks and using climate indices as input variables. The output variable is the summer (DJF) precipitation over the Furnas watershed. It was estimated using climate indices related to climatic phenomena such as El Niño - Southern Oscillation and the Pacific Decadal Oscillation and modes of climate variability, such as the North Atlantic Oscillation and the Southern Annular Mode. Despite of ANN has been applied in several problems of hydrometeorological areas, the application of such technique for long-range precipitation forecast is still rare. The results obtained demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices. Reductions in the forecast errors achieved by using only the climatological mean as forecast were considerable, being at least of 50% and reaching values close to 75% in several tests. A performance measure based on the use of contingency tables was developed taking into account the utility of the forecast. This performance measure was calculated based on the results of the use of the forecasts by a reservoir operation model, and not only by comparing streamflow observed and forecast. The performed tests show that there is no unequivocal relationship between quality and utility of the forecasts. However, when the forecast has a particular behavior, trends were found in the relationship between utility and quality of the forecast, such as models that generate streamflow forecast with lags in comparison to the observed values. In these models, the utility of the forecasts tends to enhance as the quality increases. Finally, the ability to distinguish the performance of forecast models having similar quality was one of the main merits of the performance measure developed in this research.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2018-07-20T15:07:16Z No. of bitstreams: 1 eudesioeduimdasilva.pdf: 771603 bytes, checksum: f4e775ab970d99d2708823edc10c427c (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-09-03T16:17:46Z (GMT) No. of bitstreams: 1 eudesioeduimdasilva.pdf: 771603 bytes, checksum: f4e775ab970d99d2708823edc10c427c (MD5) / Made available in DSpace on 2018-09-03T16:17:46Z (GMT). No. of bitstreams: 1 eudesioeduimdasilva.pdf: 771603 bytes, checksum: f4e775ab970d99d2708823edc10c427c (MD5) Previous issue date: 2018-06-05 / Esta tese tem como objetivo estudar os erros de previsão para inflação e os determinantes do erro de previsão fiscsal na União Econômica e Monetária do Oeste Africano (UEMOA). No que concerne a previsão de inflação, se comparados dois períodos: aquele em que o Banco Central da UEMOA (BCEAO) utiliza o modelo de metas para inflação (entre 2011 e 2015) e o período anterior (entre 1997 e 2010). Para tal, foram estimadas as Raízes dos Erros Quadrados Médios (REQMs) de diferentes modelos econométricos e de suas combinações (similares àqueles utilizados na previsão da inflação da Zona Monetária do Euro). Os resultados mostram uma redução dos erros de previsão da inflação, após a implementação do modelo de metas. Em relação aos determinantes do erro de previsão do saldo orçamentário na zona da União, Econômica e Monetária da África Ocidental (UEMOA) no período entre 2000 e 2015, a análise preliminar dos dados mostra que a maioria dos países da UEMOA apresentam erros de previsão positivos, sugerindo uma postura prudente em relação a previsão do saldo orçamentário. Destarte, foram feitas estimações por meio de quatro métodos econométricos: Mínimos quadrados ordinários em Painel (POLS), Mínimos quadrados ordinários com efeito fixo (FE-OLS), método generalizado de momentos em diferença (DGMM) e método generalizado de momentos sistêmico (S-GMM). Os resultados mostram a relevância dos fatores econômicos na explicação do erro de previsão do saldo orçamentário, especialmente o erro de previsão do PIB. Por outro lado, a hipótese do efeito da crise de subprime de 2008 não foi confirmada na zona da UEMOA. Os fatores políticos, institucionais e de governança também não tiveram relevância na determinação do erro de previsão fiscal. / The main objective of this thesis is to study inflation forecasting errors and the determinants of fiscal forecast error in the West African Economic and Monetary Union (WAEMU). Concerning inflation forecasting, two periods are compared: the one in which the UEMOA (BCEAO) uses the inflation targeting model (between 2011 and 2015) and the previous period (between 1997 and 2010). In order to do so, the Mean Square Error Roots (REQMs) of different econometric models and their combinations (similar to those used in inflation forecasting of the Euro Monetary Zone) were estimated. The results show a reduction of inflation forecast errors after the implementation of the target model. Regarding the determinants of the forecast error of the budget balance in the West African Economic and Monetary Union (WAEMU) area between 2000 and 2015, preliminary data analysis shows that most WAEMU countries have positive forecast errors, suggesting a cautious approach to forecasting the budget balance. Thus, estimations were made through four econometric methods: Ordinary least squares in Panel (POLS), Ordinary least squares with fixed effect (FE-OLS), generalized method of moments in difference (D-GMM) and systemic generalized method of moments (S-GMM). The results show the relevance of the economic factors to explain forecast error of the budget balance, especially the forecast error of GDP. On the other hand, the hypothesis of the effect of the 2008 subprime crisis was not confirmed in the UEMOA zone. The political, institutional and governance factors were also not relevant in determining the fiscal forecast error.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Load forecasting is a very important activity on electric power system operation and planning, because many studies on electricity sector depend on future behavior of the system, requiring the electricity demand forecast for its realization. The very short-term load forecasting has a horizon of few minutes to a few hours and it seeks to translate more accurately the instantaneous profile of load. There are several factors that should be considered in forecasting methods, climatic variables have a major influence on demand trends in the very short term, therefore, they should be incorporated into the projection model. In Brazil, has been growing use of electricity production through the photovoltaic generation, so, for this feature to be used efficiently, energy produced by the solar panels forecast is a tool that contributes to this type of energy act reliably. The main objective of this work is to develop a methodology for load and solar power generation forecasting in the very short-term considering the influence the climatic variables. The methodology for load, wind and solar power generation forecasting considers the climatic variables: temperature, relative humidity, wind speed, solar radiation and atmospheric pressure. The study presents data load for a typical year of a substation of the metropolitan region of Rio Grande do Sul, analyzed with data from a weather station in the region. For calculate the solar power generation forecasting the method uses a model that considers the solar radiation and the temperature to calculate the power produced by the photovoltaic module. The method for the forecast was performed using Excel VBA tool, by grouping the load and climate variables data of history and is based on multiple linear regression. The projection algorithm was tested and compared computationally, based on actual data, presenting significant results, because as it is projected to hours ahead, the data is updated with the actual data every hour, reducing forecast errors, confirming that the considered climatic variables are very important to refine load and generation forecasting methods, essential for system planning. Compared to other existing methods, the proposed method stands out by the fact to consider climatic variables for the projection, and uses the methodology to perform the projection of solar power generation. / A previsão de carga é uma atividade de grande importância inserida na operação e no planejamento do sistema elétrico de potência, pois muitos estudos referentes ao setor elétrico dependem do comportamento futuro do sistema, sendo necessária a previsão de demanda de energia elétrica para sua realização. A previsão de demanda de eletricidade para curtíssimo prazo possui um horizonte de poucos minutos até algumas horas e ela procura traduzir com maior exatidão o perfil instantâneo da carga. Há vários fatores que devem ser considerados nos métodos de previsão, as variáveis climáticas apresentam grande influência na evolução de demanda no curtíssimo prazo, portanto, devem ser incorporadas no modelo de projeção. No Brasil, tem sido crescente a utilização da produção de energia elétrica através da geração fotovoltaica, sendo assim, para que esse recurso seja utilizado de forma eficiente, a previsão da energia produzida pelos painéis solares é uma ferramenta que contribui para que esse tipo de energia atue de forma confiável. O objetivo principal deste trabalho é o desenvolvimento de uma metodologia para previsão de carga e geração de energia solar para o horizonte de curtíssimo prazo, considerando a influência das variáveis climáticas. A metodologia para previsão de carga e geração de energia solar considera as variáveis climáticas: temperatura ambiente, umidade relativa do ar, velocidade do vento, radiação solar e pressão atmosférica. O estudo apresenta dados de carga de uma subestação da região metropolitana do estado do Rio Grande do Sul, analisados com dados de uma estação meteorológica da região. Para o cálculo da previsão da geração solar o método utiliza um modelo que considera a radiação solar e a temperatura para o cálculo da potência produzida pelo módulo fotovoltaico. O método para a previsão foi realizado utilizando a ferramenta VBA do Excel, através do agrupamento dos dados de carga e das variáveis climáticas do histórico e baseia-se na regressão linear múltipla. O algoritmo de previsão foi testado e comparado computacionalmente com base nos dados reais, apresentando resultados significativos, pois como a projeção é para horas a frente, os dados são atualizados com os dados reais a cada hora, diminuindo os erros da previsão, confirmando que as variáveis climáticas consideradas tem grande importância para refinar métodos de previsão de carga e geração de energia solar, fundamental para o planejamento do sistema elétrico. Em relação aos demais métodos já existentes, o método proposto se destaca pelo fato de considerar variáveis climáticas para a projeção de carga, e utiliza a metodologia para realizar a projeção da geração solar.

This diploma thesis deals with a demand forecasting in electronic shop focused on electronics Alza.cz. The aim of the thesis is to evaluate several forecasting methods for different groups of products and to determine which of them provides the most accurate forecasts. The theoretical part is focused on electronic business, logistics cost, demand forecast, demand forecasting methods and forecast accuracy measuring methods. In practical part, selected methods are applied on data of past demand to calculate the forecasts. Afterwards the forecast accuracy is measured. At the end the thesis provides evaluation of forecast accuracy of the methods.

Les erreurs des modèles de prévision numérique du temps (PNT) peuvent être réduites par des méthodes de post-traitement (dites d'adaptation statistique ou AS) construisant une relation statistique entre les observations et les prévisions. L'objectif de cette thèse est de construire des AS de prévisions de vent pour la France sur la grille de plusieurs modèles de PNT, pour les applications opérationnelles de Météo-France en traitant deux problèmes principaux. Construire des AS sur la grille de modèles de PNT, soit plusieurs milliers de points de grille sur la France, demande de développer des méthodes rapides pour un traitement en conditions opérationnelles. Deuxièmement, les modifications fréquentes des modèles de PNT nécessitent de mettre à jour les AS, mais l'apprentissage des AS requiert un modèle de PNT inchangé sur plusieurs années, ce qui n'est pas possible dans la majorité des cas.Une nouvelle analyse du vent moyen à 10 m a été construite sur la grille du modèle local de haute résolution (2,5 km) de Météo-France, AROME. Cette analyse se compose de deux termes: une spline fonction de la prévision la plus récente d'AROME plus une correction par une spline fonction des coordonnées du point considéré. La nouvelle analyse obtient de meilleurs scores que l'analyse existante, et présente des structures spatio-temporelles réalistes. Cette nouvelle analyse, disponible au pas horaire sur 4 ans, sert ensuite d'observation en points de grille pour construire des AS.Des AS de vent sur la France ont été construites pour ARPEGE, le modèle global de Météo-France. Un banc d'essai comparatif désigne les forêts aléatoires comme meilleure méthode. Cette AS requiert un long temps de chargement en mémoire de l'information nécessaire pour effectuer une prévision. Ce temps de chargement est divisé par 10 en entraînant les AS sur des points de grille contigü et en les élaguant au maximum. Cette optimisation ne déteriore pas les performances de prévision. Cette approche d'AS par blocs est en cours de mise en opérationnel.Une étude préalable de l'estimation du « continuous ranked probability score » (CRPS) conduit à des recommandations pour son estimation et généralise des résultats théoriques existants. Ensuite, 6 AS de 4 modèles d'ensemble de PNT de la base TIGGE sont combinées avec les modèles bruts selon plusieurs méthodes statistiques. La meilleure combinaison s'appuie sur la théorie de la prévision avec avis d'experts, qui assure de bonnes performances par rapport à une prévision de référence. Elle ajuste rapidement les poids de la combinaison, un avantage lors du changement de performance des prévisions combinées. Cette étude a soulevé des contradictions entre deux critères de choix de la meilleure méthode de combinaison : la minimisation du CRPS et la platitude des histogrammes de rang selon les tests de Jolliffe-Primo. Il est proposé de choisir un modèle en imposant d'abord la platitude des histogrammes des rangs. / Errors of numerical weather prediction (NWP) models can be reduced thanks to post-processing methods (model output statistics, MOS) that build a statistical relationship between the observations and associated forecasts. The objective of the present thesis is to build MOS for windspeed forecasts over France on the grid of several NWP models, to be applied on operations at Météo-France, while addressing the two main issues. First, building MOS on the grid of some NWP model, with thousands of grid points over France, requires to develop methods fast enough for operational delays. Second, requent updates of NWP models require updating MOS, but training MOS requires an NWP model unchanged for years, which is usually not possible.A new windspeed analysis for the 10 m windspeed has been built over the grid of Météo-France's local area, high resolution (2,5km) NWP model, AROME. The new analysis is the sum of two terms: a spline with AROME most recent forecast as input plus a correction with a spline with the location coordinates as input. The new analysis outperforms the existing analysis, while displaying realistic spatio-temporal patterns. This new analysis, now available at an hourly rate over 4, is used as a gridded observation to build MOS in the remaining of this thesis.MOS for windspeed over France have been built for ARPEGE, Météo-France's global NWP model. A test-bed designs random forests as the most efficient MOS. The loading times is reduced by a factor 10 by training random forests over block of nearby grid points and pruning them as much as possible. This time optimisation goes without reducing the forecast performances. This block MOS approach is currently being made operational.A preliminary study about the estimation of the continuous ranked probability score (CRPS) leads to recommendations to efficiently estimate it and to generalizations of existing theoretical results. Then 4 ensemble NWP models from the TIGGE database are post-processed with 6 methods and combined with the corresponding raw ensembles thanks to several statistical methods. The best combination method is based on the theory of prediction with expert advice, which ensures good forecast performances relatively to some reference forecast. This method quickly adapts its combination weighs, which constitutes an asset in case of performances changes of the combined forecasts. This part of the work highlighted contradictions between two criteria to select the best combination methods: the minimization of the CRPS and the flatness of the rank histogram according to the Jolliffe-Primo tests. It is proposed to choose a model by first imposing the flatness of the rank histogram.

Ensembles are today routinely applied to estimate uncertainty in numerical predictions of complex systems such as the weather. Instead of initializing a single numerical forecast, using only the best guess of the present state as initial conditions, a collection (an ensemble) of forecasts whose members start from slightly different initial conditions is calculated. By varying the initial conditions within their error bars, the sensitivity of the resulting forecasts to these measurement errors can be accounted for. The ensemble approach can also be applied to estimate forecast errors that are due to insufficiently known model parameters by varying these parameters between ensemble members. An important (and difficult) question in ensemble weather forecasting is how well does an ensemble of forecasts reproduce the actual forecast uncertainty. A widely used criterion to assess the quality of forecast ensembles is statistical consistency which demands that the ensemble members and the corresponding measurement (the ``verification\'\') behave like random independent draws from the same underlying probability distribution. Since this forecast distribution is generally unknown, such an analysis is nontrivial. An established criterion to assess statistical consistency of a historical archive of scalar ensembles and verifications is uniformity of the verification rank: If the verification falls between the (k-1)-st and k-th largest ensemble member it is said to have rank k. Statistical consistency implies that the average frequency of occurrence should be the same for each rank. A central result of the present thesis is that, in a statistically consistent K-member ensemble, the (K+1)-dimensional vector of rank probabilities is a random vector that is uniformly distributed on the K-dimensional probability simplex. This behavior is universal for all possible forecast distributions. It thus provides a way to describe forecast ensembles in a nonparametric way, without making any assumptions about the statistical behavior of the ensemble data. The physical details of the forecast model are eliminated, and the notion of statistical consistency is captured in an elementary way. Two applications of this result to ensemble analysis are presented. Ensemble stratification, the partitioning of an archive of ensemble forecasts into subsets using a discriminating criterion, is considered in the light of the above result. It is shown that certain stratification criteria can make the individual subsets of ensembles appear statistically inconsistent, even though the unstratified ensemble is statistically consistent. This effect is explained by considering statistical fluctuations of rank probabilities. A new hypothesis test is developed to assess statistical consistency of stratified ensembles while taking these potentially misleading stratification effects into account. The distribution of rank probabilities is further used to study the predictability of outliers, which are defined as events where the verification falls outside the range of the ensemble, being either smaller than the smallest, or larger than the largest ensemble member. It is shown that these events are better predictable than by a naive benchmark prediction, which unconditionally issues the average outlier frequency of 2/(K+1) as a forecast. Predictability of outlier events, quantified in terms of probabilistic skill scores and receiver operating characteristics (ROC), is shown to be universal in a hypothetical forecast ensemble. An empirical study shows that in an operational temperature forecast ensemble, outliers are likewise predictable, and that the corresponding predictability measures agree with the analytically calculated ones.

El pronóstico erróneo de huéspedes y materiales en los hoteles de Huancayo es un problema que conlleva a generar gastos ocasionados por rotura de stock y exceso de stock, que a su vez conlleva a pérdida de clientes. De esta forma se diseñó un modelo el cual mediante cuatro métodos de pronóstico puede sugerir al hotel qué cantidad de huéspedes van a arribar al hotel y cuántos productos se debe de comprar para evitar costos adicionales. En esta investigación se dará a conocer los resultados obtenidos con la aplicación de un modelo de pronóstico de la demanda basado en forcast en el hotel “Rey” ubicado en la provincia de Huancayo del departamento de Junín. En el presente artículo se pronosticó cantidades para el año 2020 y se comparan mediante el error MAPE, así se logra sugerir un pronóstico con un porcentaje de error reducido y mejor a comparación con el que se tenía antes de la implementación. / The wrong forecast of guests and materials in Huancayo’s hotels is a big problem that leads to the generation of expenses caused by stock breakage and excess stock, which in turn leads to loss of customers. In this way, a model was designed which, through four forecasting methods, can suggest to the hotel how many guests will arrive at the hotel and how many products should be purchased to avoid additional costs. In this research, the results obtained with the application of a demand forecasting model based on forcast in the “Rey” hotel, located in Huancayo, will be disclosed. In this article, quantities were predicted for the year 2020 and they are compared using the MAPE error, in this way is possible to suggest a forecast with a reduced and better error percentage compared to the one that was had before implementation. / Trabajo de investigación

Bakgrund: Konventionell budgetering har fått utstå betydande kritik under de senaste decennierna, där många menar att den inte är lämplig i dagenssnabbrörliga och globala marknad. Ett flertal organisationer har övergett konventionell budgetering, och rörelsen eller filosofin berördetta kallas för Beyond Budgeting (BB). Grundprinciperna i denna filosofi är en organisationsstruktur där mer ansvar och tillit placeras hos den operativa delen av organisationer. Detta är menat att ge mer frihet till enskilda medarbetare men väcker frågor om agentteori och organisatorisk kontroll. Controllers traditionella roll har setts som ett verktyg för ledningen att utöva kontroll och motverka agentteorins moraliskarisk. Frågan är hur denna roll påverkas av en allt mindre strikt form av kontroll, vilka faktorer som kan tänkas påverka denna roll och hur rollen redan har förändrats. Syfte: Studiens syfte är att undersöka hur controllerrollen och budgetarbete ser ut i dagens organisationer samt om kritiken mot budgetarbete, så som filosofin BB beskriver den, faktiskt stämmer i dagens organisationer. Ytterligare undersöker studien till vilken grad BB som metod har implementerats i dessa organisationer. Metod: Semi-strukturerade intervjuer med sex stycken respondenter inom flertalet olika organisationer har agerat som empirisk data. Denna data harsedantranskriberats och analyserats i enlighet med den deduktiva ansatsen, där de teorier vi funnit har applicerats. Analysen skedde genom en tematisk analys där teman och kategorier funnits för att påvisa samband inför slutsatsen. Slutsats: Studien tyder på att controllers idag har en hög grad av kontroll över deras arbete samt att arbetet huvudsakligen är analytiskt och framåtblickande. Delar av respondenterna anser sig ha en beslutsfattande roll och arbetar huvudsakligen enskilt men har ett brett kontaktnät. Rollen kan till viss del beskrivas som en affärspartner men vi fann stora skillnader mellan respondenterna. Bland respondenterna var det vanligast att använda en toppstyrd budget, där enskilda affärsenheter har mindre inflytande. Denna budget kompletterades sedan med prognoser. Vi fann att delar av kritiken mot konventionell budgetering stämde in bland respondenternas organisationer, samt att aspekter av BB har implementerats. / Background: Conventional budgeting has endured a lot of criticism in the last decades as many argue it is not suitable for today's agile and globalized market. Several organizations have taken the step to abandon conventional budgeting and the movement, or philosophy, regarding this matter is known as Beyond Budgeting (BB). The core principle of this philosophy are an organizational structure where a higher degree of responsibility and trust is placed on the operative sections of the organization. By doing this a much higher degree of liberty is granted to partners in the organization, but it raises questions regarding agent theory and organizational control. The traditional role of the controller has been to function as a tool for upper management to exercise control and counteract the moral hazard of agency theory. The question then is how this role is affected by a much less strict form of control, and which factors can be seen to affect the role as well as which factors that have already affected it.   Purpose: The purpose of the study is to examine the role of the controller and how budgeting of modern organizations are handled as well as how the accurate the critique aimed at budgeting is, as it is described by the philosophy of BB. Furthermore, the study aims to examine to which degree the subject organizations has implemented or embraced the principles of BB.  Method: The study is built on semi-structured interviews with a total of six participants, based in several organizations, which have acted as the empirical data. This data has been transcribed and analyzed in accordance with the studies’ deductive approach upon which our studied theories have been applied. The analysis has been based on a thematic analysis where the goal has been to find common themes and categories.    Conclusion: The study has found that controllers today have a high degree of control regarding how they choose to perform their work, which has been found to be primarily analytical and forward-looking in nature. Some of the controllers consider themselves to have a decision-making role, and while their work is primarily done alone, they have a wide array of contacts. The role can to a degree be described as a business partner, but the study has found large differences among participants in this regard. Among the participants it was most common to use a conventional budget, set by upper management where local business units have limited influence. This budget was complemented with forecasts. The study also found that among the participant’s organizations, some of the critique towards conventional budgeting was accurate to a certain degree. In addition, we found that aspects of BB had been implemented.

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Pumped hydro energy storages commonly plan their operations on short-term forecasts of the upcoming electricity prices, meaning that errors in these forecasts would entail suboptimal operations of the energy storage. Despite the high investment costs of pumped hydro energy storages, few studies take a holistic approach to the uncertainties involved in such investment decisions. The aim of this study is to investigate how forecast errors in electricity prices affect the chosen size configuration in investment decisions for pumped hydro energy storages. Moreover, sizing decisions are made in the long-term and involve long-term uncertainties in electricity prices. A robust decision-making framework including long-term electricity price scenarios is therefore used to evaluate the effects of including forecast errors in the sizing decision. By simulating the day-to-day operation of the energy storage with short-term forecasts, the effects of including the errors are compared to using perfect information. Using this approach, the most robust capacity is shown to increase by 25 MW, from 2 375 MW to 2 400 MW, when including forecast errors instead of assuming perfect information in the simulations. This indicates that the deviations in short-term forecasts require the pumped hydro energy storage operator to be more flexible in their operations, thus requiring a higher capacity. In addition, the profitability of the energy storage decreased significantly when including forecast errors in the simulations, showing the importance of taking the short-term forecast errors into account in sizing and investment decisions of pumped hydro energy storage. / Driften av pumpkraftverk optimeras med hjälp av kortsiktiga prognoser av elpriser, vilket innebär att fel i dessa prognoser leder till suboptimal drift. Trots att investeringar i pumpkraftverk är kostsamma, har få studier ett holistisk synsätt kring osäkerheter i investeringsbeslutet. Målet med denna studie är att undersöka hur kortsiktiga prognosfel i elpriser påverkar den optimala dimensionering av pumpkraftverk. Investeringsbeslut i pumpkraftverk är långsiktiga och kräver estimat av framtida elpriser, vars karakteristik är osäker. Ett ramverk som bygger på robust beslutstagande, med scenarier över framtida elpriser, används därför för att bedöma effekten av att inkludera kortsiktiga prognosfel i investeringsbeslutet. Genom att simulera den dagliga driften av energilager, undersöks effekten av att inkludera prognosfel jämfört med perfekt information. Med detta tillvägagångsätt ökade den mest robusta kapaciteten med 25 MW, från 2 375 MW till 2 400 MW, när prognosfel inkluderades. Detta visar på att fel i kortsiktiga prognoser kräver pumpkraftverket av vara mer flexibelt, vilket ges av höjdkapacitet. Lönsamheten minskade också signifikant när prognosfel inkluderades, vilket visar på vikten av att ta hänsyn till kortsiktiga prognosfel i beslut kring dimensionering och investering av pumpkraftverk.