Projection de la mortalité aux âges avancées au Canada : comparaison de trois modèles

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Integración técnica y económica de energías renovables, en un sistema eléctrico liberalizado, a partir de vectores energéticos

El objetivo de la tesis es estudiar la bondad del almacenamiento de energía en hidrógeno para minorar los desvíos de energía respecto a su previsión de parques eólicos y huertas solares. Para ello se ha partido de datos de energías horarias previstas con 24 h de antelación y la energía real generada. Se ha procedido a dimensionar la planta de hidrógeno, a partir de una modelización de la operación de la misma, teniendo siempre como objetivo la limitación de los desvíos. Posteriormente, se ha procedido a simular la operación de la planta con dos objetivos en mente, uno limitar los desvíos y por otro lado operar la planta como una central de bombeo, generando hidrógeno en horas valle y generando electricidad en horas punta. Las dos simulaciones se han aplicado a tres parques eólicos de diferentes potencias, y a una huerta solar fotovoltaica. Se ha realizado un estudio económico para determinar la viabilidad de las plantas dimensionadas, obteniendo como resultado que no son viables a día de hoy y con la estimación de precios considerada, necesitando disminuir considerablemente los costes, dependiendo fuertemente de la bondad de los métodos de previsión de viento. Por último se ha estudiado la influencia de la disminución de los desvíos generados sobre una red tipo de 30 nudos, obteniendo como resultado, que si bien no disminuyen sensiblemente los extra costes generados en regulación, sí que mejora la penetración de las energías renovables no despachables en la red. Se observa disminuyen los vertidos eólicos cuando se usa la planta de hidrógeno. ABSTRACT The aim of this thesis is to study the benefit of hydrogen energy storage to minimize energy deviations of Wind Power and Solar Photovoltaic (PV) Power Plants compared to its forecast. To achieve this goal, first of all we have started with hourly energy data provided 24 h in advance (scheduled energy), and real generation (measured energy). Secondly, It has been sized the hydrogen plant, from a modeling of its working mode, always keeping the goal in mind of limiting energy imbalances. Subsequently, It have been simulated the plant working mode following two goals, one, to limit energy imbalances and secondly to operate the plant as a pumping power plant, generating hydrogen-in valley hours and generating electricity at peak hours. The two simulations have been applied to three wind power plants with different installed power capacities, and a photovoltaic solar power plant. It has been done an economic analysis in order to determine the viability of this sized plants, turning out not viable plants today with the estimated prices considered, requiring significantly lower costs, depending heavily on the reliability of the Wind Power forecast methods. Finally, It has been studied the influence of decreasing measured imbalances (of energy) in a 30 grid node, resulting that, while it not reduces significantly the extra costs generated by reserve power, it does improve the penetration of non-manageable renewable energy on the grid, by reducing the curtailments of power of these plants.

Effektiv lagerstyrning med AHP och tvådimensionell artikelklassificering : En fallstudie på Permobil AB, Timrå

Costs related to inventory are usually a significant amount of the company’s total assets. Despite this, companies in general don’t pay a lot of interest in it, even if the benefits from effective inventory are obvious when it comes to less tied up capital, increased customer satisfaction and better working environment. Permobil AB, Timrå is in an intense period when it comes to revenue and growth. The production unit is aiming for an increased output of 30 % in the next two years. To make this possible the company has to improve their way to distribute and handle material,The purpose of the study is to provide useful information and concrete proposals for action, so that the company can build a strategy for an effective and sustainable solution when it comes to inventory management. Alternative methods for making forecasts are suggested, in order to reach a more nuanced perception of different articles, and how they should be managed. Analytic Hierarchy Process (AHP) was used in order to give specially selected persons the chance to decide criteria for how the article should be valued. The criteria they agreed about were annual volume value, lead time, frequency rate and purchase price. The other method that was proposed was a two-dimensional model where annual volume value and frequency was the criteria that specified in which class an article should be placed. Both methods resulted in significant changes in comparison to the current solution. For the spare part inventory different forecast methods were tested and compared with the current solution. It turned out that the current forecast method performed worse than both moving average and exponential smoothing with trend. The small sample of ten random articles is not big enough to reject the current solution, but still the result is a reason enough, for the company to control the quality of the forecasts.

Report of the Working Group for the Bay of Biscay and the Iberian waters Ecoregion (WGBIE). 13-19 May 2016, ICES HQ, Copenhagen, Denmark

The ICES Working Group for the Bay of Biscay and the Iberic waters Ecoregion (WGBIE) met in Copenhagen, Denmark during 13–14 May 2016. There were 22 stocks in its remit distributed from ICES Divisions 3.a–4.a though mostly distributed in Sub Areas 7, 8 and 9. There were 21 participants, some of whom joined the meeting re-motely. The group was tasked with conducting assessments of stock status for 22 stocks using analytical, forecast methods or trends indicators to provide catch forecasts for eight stocks and provide a first draft of the ICES advice for 2016 for fourteen stocks. For the remaining stocks, the group had to update catch information and indices of abundance where needed. Depending on the result of this update, namely if it would change the perception of the stock, the working group drafted new advice. Analytical assessments using age-structured models were conducted for the northern and southern stocks of megrim and the Bay of Biscay sole. The two hake stocks and one southern stock of anglerfish were assessed using models that allow the use of only length-structured data (no age data). A surplus-production model, without age or length structure, was used to assess the second southern stocks of anglerfish. No ana-lytical assessments have been provided for the northern stocks of anglerfish after 2006. This is mostly due to ageing problems and to an increase in discards in recent years, for which there is no reliable data at the stock level. The state of stocks for which no analytical assessment could be performed was inferred from examination of commer-cial LPUE or CPUE data and from survey information. Three nephrops stocks from the Bay of Biscay and the Iberian waters are scheduled for benchmark assessments in October 2016. The WGBIE meeting spent some time review-ing the progress towards the benchmark (see Annex 6) together with longer term benchmarks (2017 and after, see section 1.) for sea bass in the Bay of Biscay, all an-glerfish and hake stocks assessed by the WG. For the northern megrim stock, the sched-ule an inter-benchmark meeting was completed successfully and the group reviewed the outcome and accepted the category 1 update assessment. A recurrent issue significantly constrained the group’s ability to address the terms of reference this year. Despite an ICES data call with a deadline of six weeks before the meeting, data for several stocks were resubmitted during the meeting which lead to increased workloads during the working group, as in that case, the assessments could not be carried out in National Laboratories prior to the meeting as mentioned in the ToRs. This is an important matter of concerns for the group members. Section 1 of the report presents a summary by stock and discusses general issues. Sec-tion 2 provides descriptions of the relevant fishing fleets and surveys used in the as-sessment of the stocks. Sections 3–18 contains the single stock assessments.

Comparison of different methods to forecast hospital bed needs

Objectives : This study compares three methods to forecast the number of acute somatic hospital beds needed in a Swiss academic hospital over the period 2010-2030. Design : Information about inpatient stays is provided through a yearly mandatory reporting of Swiss hospitals, containing anonymized data. Forecast of the numbers of beds needed compares a basic scenario relying on population projections with two other methods in use in our country that integrate additional hypotheses on future trends in admission rates and length of stay (LOS).

Forecast comparison with nonlinear methods for brazilian industrial production

Este trabalho avalia as previsões de três métodos não lineares — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model e Autometrics com Dummy Saturation — para a produção industrial mensal brasileira e testa se elas são mais precisas que aquelas de preditores naive, como o modelo autorregressivo de ordem p e o mecanismo de double differencing. Os resultados mostram que a saturação com dummies de degrau e o Logistic Smooth Transition Autoregressive Model podem ser superiores ao mecanismo de double differencing, mas o modelo linear autoregressivo é mais preciso que todos os outros métodos analisados.

Forecast comparison with nonlinear methods for Brazilian industrial production

This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.

Methodology to forecast the harvest date of banana bunches

Introduction. This method is used to forecast the harvest date of banana bunches from as early as the plant shooting stage. It facilitates the harvest of bunches with the same physiological age. The principle, key advantages, time required and expected results are presented. Materials and methods. Details of the four steps of the method ( installation of the temperature sensor, tagging bunches at the flowering stage, temperature sum calculation and estimation of bunch harvest date) are described. Possible problems are discussed. Results. The application of the method allows drawing a curve of the temperature sum accumulated by the bunches which have to be harvested at exactly 900 degree-days physiological age.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

Technical and economic resources management in smart grids using heuristic optimization methods

Intensive use of Distributed Generation (DG) represents a change in the paradigm of power systems operation making small-scale energy generation and storage decision making relevant for the whole system. This paradigm led to the concept of smart grid for which an efficient management, both in technical and economic terms, should be assured. This paper presents a new approach to solve the economic dispatch in smart grids. The proposed methodology for resource management involves two stages. The first one considers fuzzy set theory to define the natural resources range forecast as well as the load forecast. The second stage uses heuristic optimization to determine the economic dispatch considering the generation forecast, storage management and demand response

Metaheuristic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Metaheuristhic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Random walk forecast of urban water in Iran under uncertainty

There are two significant reasons for the uncertainties of water demand. On one hand, an evolving technological world is plagued with accelerated change in lifestyles and consumption patterns; and on the other hand, intensifying climate change. Therefore, with an uncertain future, what enables policymakers to define the state of water resources, which are affected by withdrawals and demands? Through a case study based on thirteen years of observation data in the Zayandeh Rud River basin in Isfahan province located in Iran, this paper forecasts a wide range of urban water demand possibilities in order to create a portfolio of plans which could be utilized by different water managers. A comparison and contrast of two existing methods are discussed, demonstrating the Random Walk Methodology, which will be referred to as the â On uncertainty pathâ , because it takes the uncertainties into account and can be recommended to managers. This On Uncertainty Path is composed of both dynamic forecasting method and system simulation. The outcomes show the advantage of such methods particularly for places that climate change will aggravate their water scarcity, such as Iran.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.