Técnicas de identificación algebraicas y espectrales de señales armónicas. Aplicaciones en mecatrónica y economía

La identificación de señales armónicas abarca un amplio rango de aplicaciones procedentes de disciplinas como la mecatrónica o la economía. En esta tesis se trata el problema de la identificación de señales armónicas utilizando técnicas de identificación de sistemas y análisis de series temporales. En referencia a las aplicaciones mecatrónicas se han utilizado técnicas derivativas algebraicas para diseñar algoritmos capaces de estimar en línea los parámetros de una o varias ondas sinusoidales con y sin amortiguamiento en un tiempo inferior al periodo de dicha señal. Con el fin de validar estos estimadores se han aplicado a la monitorización de vibraciones procedentes de brazos flexibles experimentales, comparando los resultados obtenidos con otros estimadores de frecuencia recientemente publicados como son los filtros adaptativos de ranura. Además se han combinado los estimadores con controles en lazo cerrado y en lazo abierto para realizar controles adaptativos. Estos controles adaptativos han mostrado ser robustos frente al problema de cambios de masa en el extremo de brazos manipuladores flexibles. Se ha aprovechado el conocimiento adquirido en el análisis de vibraciones de estructuras flexibles para abordar señales armónicas procedentes de aplicaciones económicas. Concretamente se ha tratado el problema de la predicción a corto plazo de la demanda y precios de energía eléctrica en el mercado liberalizado. Se han elegido estas series temporales ya que poseen un fuerte componente periódico, es decir tienen una estacionalidad diaria, semanal y un ciclo anual. Se han utilizado técnicas de identificación en el dominio de la frecuencia junto con modelos en espacio de los estados (EE) para la predicción de estas series temporales. La representación en EE permite extraer componentes no observables de la serie temporal como son la tendencia, la estacionalidad o el término irregular por otro lado, la estimación en el dominio la frecuencia permite realizar predicciones automáticas sin necesidad de cambiar los modelos cada cierto tiempo. Estos resultados obtenidos mejoran a otras metodologías típicas del análisis de series temporales. El mismo modelo desarrollado en EE se adapta para realizar predicciones de la demanda a medio y largo plazo. Por último, es interesante el punto de vista que esta tesis aporta sobre el análisis del ciclo económico, donde se utilizan técnicas de identificación algebraica y filtros adaptativos de ranura para poder estudiar la evolución del ciclo de un indicador económico típico

Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates

In order to integrate solar energy into the grid it is important to predict the solar radiation accurately, where forecast errors can lead to significant costs. Recently, the increasing statistical approaches that cope with this problem is yielding a prolific literature. In general terms, the main research discussion is centered on selecting the ``best'' forecasting technique in accuracy terms. However, the need of the users of such forecasts require, apart from point forecasts, information about the variability of such forecast to compute prediction intervals. In this work, we will analyze kernel density estimation approaches, volatility forecasting models and combination of both of them in order to improve the prediction intervals performance. The results show that an optimal combination in terms of prediction interval statistical tests can achieve the desired confidence level with a lower average interval width. Data from a facility located in Spain are used to illustrate our methodology

Optimising forecasting models for inventory planning

Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter

Impact of Demand Nature on the Bullwhip Effect. Bridging the Gap between Theoretical and Empirical Research

The bullwhip effect (BE) consists of the demand variability amplification that exists in a supply chain when moving upwards. This undesirable effect produces excess inventory and poor customer service. Recently, several research papers from either a theoretical or empirical point of view have indicated the nature of the de- mand process as a key aspect to defining the BE. Nonetheless, they reached different conclusions. On the one hand, theoretical research quantified the BE depending on the lead time and ARIMA parameters, where ARIMA functions were employed to model the demand generator process. In turn, empirical research related nonlinearly the demand variability extent with the BE size. Although, it seems that both results are contradictory, this paper explores how those conclusions complement each other. Essentially, it is shown that the theoretical developments are precise to determine the presence of the BE based on its ARIMA parameter estimates. Nonetheless, to quan- tify the size of the BE, the demand coefficient of variation should be incorporated. The analysis explores a two-staged serially linked supply chain, where weekly data at SKU level from a manufacturer specialized in household products and a major UK grocery retailer have been collected

Un análisis econométrico de la relación de sustitución entre el AVE y el transporte aéreo

La implantación del AVE en España se ha convertido en un ejemplo a nivel mundial, pero conlleva grandes costes, además de competir con las líneas aéreas ya existentes, y con las nuevas LCC que han surgido durante este periodo. Es este trabajo analizamos desde una perspectiva dinámica la relación de sustitución que existe entre estos dos medios de transporte, observando una relación mucho menos estrecha que la prevista en estudios realizados a priori

Ingenium Research Group, Universidad de Castilla-La Mancha Ciudad Real, Edificio Politécnica 13071, Spain [email protected]

Renewable energy is being one of the options to cover the demand due to the environmental restrictions. One of the most relevant renewable energy sources is the solar energy, where the concentrated solar power is nowadays the source that is getting more importance. The correct performance of solar receiver is crucial because its failure can result in significant costs and availability of the energy service. Non-destructive testing is broadly used in structural health monitoring systems in order to detect and diagnose faults/failures. The aim of this paper is to present a fault detection and diagnosis approach based on long range ultrasonic technology, together with novel analytical procedures on signal processing of high frequency waves (Lamb waves). These waves flow through the material via the piezoelectric transducers, where these transducers are also employed as sensors. The fault can be detected and diagnosed by changes in the signal when it is modified by the fault. The experimental platform consists of: i) a data logger able to generate and read voltage signals at high frequency; ii) a sensing system based on piezoelectric transducers placed at the solar collector. A novel method of analyzing the data generated in the platform by means of time series is employed

An adaptive pneumatic suspension based on the estimation of the excitation frequency

A pneumatic suspension that can adapt itself to the incoming vibration is presented in this paper. A switching control strategy between two different configurations is proposed and studied. The objective is to avoid undesirable resonant frequencies. The control procedure is based on the pre-knowledge of the incoming vibration frequency, and when this frequency is unknown, a very efficient prediction technique is used. The results show that the adaptable suspension has improved performance as compared to any of its passive counterparts. The transient response when switching typically takes less than three cycles and does not hinder the suspension performance

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

A novel time-varying bullwhip effect metric. An application to promotional sales

Bullwhip effect is a problem of paramount importance that reduces competitiveness of supply chains around the world. A significant effort is being devoted by both practitioners and academics to understand its causes and to reduce its pernicious consequences. Nevertheless, limited research has been carried out to analyze potential metrics to measure it, that typically are summarized in the coefficient of variation ratio of different echelons demand. This work proposes a new metric based on a time-varying extension of the aforementioned bullwhip effect metric by employing recursive estimation algorithms expressed in the State Space framework to provide at each single time period a real-time bullwhip effect estimate. In order to illustrate the results, a case study based on a serially-linked supply chain of two echelons from the chemical industry is analyzed. Particularly, this metric is employed to analyze the effect of promotional campaigns on the bullwhip effect on a real-time fashion. The results show that, effectively, the bullwhip effect is not constant along time, but interestingly, it is reduced during the promotional periods and it is bigger before and after the promotion takes place

Measuring the substitution effects between High Speed Rail and air transport in Spain

The main objective of this paper is to estimate the impact that the expansion of the HSR network has had on air transport in Spain by estimating the substitution effect between the two types of transportation. This paper considers the way that the HSR network has grown and how this growth could have affected air transport dynamically. The findings show that a dynamic vision of this substitution rate should be adopted, as opposed to assuming that the rate is constant, as has been the case in previous references. Although the rate varies significantly over the study period, only 13.9% of HSR passenger demand was found to have come from air travel during the 1999–2012 period, meaning that HSR and airlines would seem to offer more independent services than at first it might appear. This confirms the hypothesis as to the HSR’s great ability to generate its own demand. The substitution rate between the two transport modes seems to be closely linked to the way that any new stations are incorporated into the HSR network. Convergence between the seasonality of HSR and air transport has also been examined. The results show that it is difficult to talk of a real HSR transport network in Spain