Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations

Tendencias recientes en el pronóstico de velocidad de viento para generación eólica

Este documento tiene como objetivo presentar un marco unificado para discutir, resumir y organizar los principales avances en pronóstico de velocidad de viento para generación eólica utilizando un método auditable, ordenado y reproducible. Los principales hallazgos fueron: La mayor parte de los trabajos provienen de China y Estados Unidos, las series de tiempo usadas poseen una longitud de menos de un año, comúnmente el pronóstico es realizado en un rango de 1 hora a 48 horas hacia adelante. Muchos estudios usan solamente modelos autoregresivos (Lineares y no lineares) o en muchos casos una sola variable explicatoria. Usualmente la variable pronosticada es la velocidad de viento u la potencia generada. La revisión muestra una tendencia en la que los autores están experimentando con modelos híbridos para obtener las ventajas de cada método utilizado, también, una tendencia a utilizar métodos clásicos como redes neuronales, máquinas de vectores de soporte y modelos autorregresivosAbstract: This document aims to provide a unified frame for discussing, summarizing and organizing the main advances in wind power forecasting using an auditable, orderly and reproducible method. Our main findings are the following: most of works forecasting time series from China and United States; time series data usually cover information with a length lower than a year of data. Commonly, the forecast is done for 1 to 48 hours ahead. Many studies using only autorregresive models (linear or no linear) or, in many cases, one explanatory variable. Usually, the variables forecasted are speed and power. The review shows a tendency in which the authors are experimenting with hybrid models to obtain the advantages of each method used, also, a trend to use classical methods such as neural networks, Support Vector Machines and autoregressive models.Maestrí

Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better prediction performance. Aiming at testing its effectiveness, we benchmark the forecasting performance of our approach against four competitors. The daily interval prices of carbon futures contracts traded in the Intercontinental Futures Exchange from August 12, 2010, to November 13, 2014, are used as the experiment dataset. The statistical significance of the interval forecasts is examined. The proposed hybrid approach is found to demonstrate the higher forecasting performance relative to all other competitors. Our application offers practitioners a promising set of results with interval forecasting in carbon futures market

A Framework for Evaluation and Identication of Time Series Models for Multi-Step Ahead Prediction of Physiological Signals

Significant interest exists in the potential to use continuous physiological monitoring to prevent respiratory complications and death, especially in the postoperative period. Smart alarm-threshold based systems are currently used with hospitalized patients. Despite clinical observations and research studies to support benefit from these systems, several concerns remain. For example, a small difference in a threshold may significantly increase the alarm rate. A significant increase in alarm related adverse outcomes has been reported by health care oversight organizations. Also, it has been recently shown that the signaled alarms are indeed late detections for clinical instability leading to a delayed recognition and less successful clinical intervention. This dissertation advances the state of art by moving from just monitoring towards prediction of physiological variables. Moving in this direction introduces research challenges in many aspects. Although existing literature describes many metrics for characterizing the prediction performance of time series models, these metrics may not be relevant for physiological signals. In these signals, clinicians are often concerned about specific regions of clinical interest. This dissertation develops and implements different types of metrics that can characterize the performance in predicting clinically relevant regions in physiological signals. In the era of massive data, biomedical devices are able to collect a large number of synchronized physiological signals recording a significant time history of a patient's physiological state. Directionality between physiological signals and which ones can be used to improve the ability to predict the other ones is an important research question. This dissertation uses a dynamic systems perspective to address this question. Metrics are also defined to characterize the improvement achieved by incorporating additional data into the prediction model of a physiological signal of interest. Although a rich literature exists on time series prediction models, these models traditionally consider the (absolute or square) error between the predicted and actual time series as an objective for optimization. This dissertation proposes two modeling frameworks for predicting clinical regions of interest in physiological signals. The physiological definition of the clinically relevant regions is incorporated in the model development and used to optimize models with respect to predictions of these regions.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116666/1/elmoaqet_1.pd

DATA-DRIVEN PREDICTION, DESIGN, AND CONTROL OF SYSTEM BEHAVIOR USING STATISTICAL LEARNING

The goal in this dissertation is to develop new data-driven techniques for prediction, design, and control of the behavior of a variety of engineering systems. The data used can be obtained from a variety of sources, including from offline, high-fidelity system’s simulation, physical experiments, and online, sparse measurements from sensors. Three inter-related research directions are followed in this dissertation. Following the first direction, the author presents a multi-step-ahead prediction technique for evaluating a single-response (or single-output of the) system’s behavior through an integration of the data obtained offline from the system’s high-fidelity simulation, and online from single sensor measurements. With regard to the first research direction, the key contribution includes a reasonably fast and accurate prediction strategy that can be used, among others, for online, multi-step ahead forecasting of the system’s operational behavior. Building on the work from the first direction, the author follows a second research direction to present a multi-step ahead prediction technique, this time for a multi-response system’s behavior, that can be used for evaluating various system’s designs and corresponding operations. Data in this case is obtained from the offline, high-fidelity system’s simulations, and online sparse measurements from multiple sensors (or limited number of physical experiments). The main contribution for this second direction is in construction of a new data-driven, multi-response prediction framework that has a robust predictive capability. Along the third research direction, a data-driven technique is used for prediction and co-optimization of a system’s design and control. The data in this case is obtained from sensor measurements or a simulator. The main contribution achieved through the third direction is a new data-driven reinforcement learning-based prediction and co-optimization approach. The methods from this dissertation have numerous applications, including those demonstrated here: (i) assessment of safe aircraft flight conditions (Chapters 2 and 3), (ii) evaluation of design and operation of a robotic appendage (Chapter 3), and (iii) design and control of a traffic system (Chapter 4)

Multistep ahead time series prediction

Time series analysis has been the subject of extensive interest in many fields ofstudy ranging from weather forecasting to economic predictions, over the past twocenturies. It has been fundamental to our understanding of previous patterns withindata and has also been used to make predictions in both the short and long termhorizons. When approaching such problems researchers would typically analyzethe given series for a number of distinct characteristics and select the most ap-propriate technique. However, the complexity of aligning a set of characteristicswith a method has increased in complexity with the advent of Machine Learningand the introduction of Multi-Step Ahead Prediction (MSAP). We examine themodel/strategy approaches which are currently applied to conduct multi-step aheadprediction in time series data and propose an alternative MSAP strategy known asMulti-Resolution Forecast Aggregation.Typically, when researchers propose an alternative strategy or method, they demon-strate it on a relatively small set of time series, thus the general breath of use isunknown. We propose a process that generates a diverse set of synthetic time se-ries, that will enable a robust examination of MRFA and other methods/strategies.This dataset in conjunction with a range of popular prediction methods and MSAPstrategies is then used to develop a meta learner that estimates the normalized meansquare error of the prediction approach for the given time serie

Aportes de soft computing en las energías renovables eólicas

La Soft Computing es un conjunto de metodologías que fundamentalmente sirve para resolver problemas provenientes de situaciones inciertas, imprecisas, y otras situaciones en las que con las metodologías clásicas no se pueden abordar, por su dificultad en su representación y modelación así como por su complejidad. En la generación de la energía eólica se presentan diversas situaciones complejas de naturaleza incierta e imprecisa, que han necesitado y necesitan el uso de la soft computing. En este ensayo se hace la recopilación de los artículos publicados en los que se resuelve los problemas asociados con las energías eólicas utlizando las metodologías soft computing. Se ha encontrado gran número de trabajos que utilizan las metodologías fuzzy, redes neuronales artificiales y algoritmos genéticos; escasos trabajos que utilizan los métodos híbridos y ningunos los recientes métodos de búsqueda y relajación.    Palabras clave: Soft computing,  energía eólica

Análisis y gestión óptima de la demanda en sistemas eléctricos conectados a la red y en sistemas aislados basados en fuentes renovables

En esta tesis doctoral se han analizado, de forma detallada, los principales aspectos relacionados con el funcionamiento de los sistemas eléctricos aislados y conectados a la red eléctrica basados en fuentes de energía renovable. En lo referente al análisis de los sistemas aislados de la red eléctrica, se ha analizado el efecto de la eficiencia culómbica y del regulador de carga en la fiabilidad de los sistemas eólicos con baterías. También se ha tratado la estimación de las horas de operación, consumo de combustible y coste neto actualizado de los sistemas que utilizan como respaldo un generador convencional. Por otra lado, se ha desarrollado un modelo probabilístico que permite considerar la incertidumbre existente en la estimación de la vida del banco de baterías, la incertidumbre asociada a los precios del combustible, la producción del generador fotovoltaico, el perfil típico de carga, así como la variabilidad de los recursos eólico y solar. Además, teniendo en cuenta la importancia que tiene el uso racional de la energía eléctrica, en esta tesis se ha desarrollado una novedosa técnica para la gestión de la demanda de sistemas aislados de la red eléctrica que sugiere al usuario del mismo el mejor momento para hacer uso de sus electrodomésticos, reduciendo el consumo de combustible y mejorando el uso de la energía almacenada en el banco de baterías. Finalmente, considerando sistemas conectados a la red eléctrica, se ha desarrollado una estrategia de Adaptación de la Demanda para consumidores residenciales que, haciendo uso de las capacidades de comunicación de la futura Red Eléctrica Inteligente, determina mediante la optimización de la negociación entre el usuario y la empresa de distribución de energía eléctrica, la forma en que el consumidor debe utilizar sus electrodomésticos considerando sus preferencias y su poder adquisitivo. Los resultados obtenidos sugieren importantes mejoras en los modelos que se utilizan habitualmente en la simulación y optimización de sistemas híbridos, específicamente en la consideración del regulador de carga como un importante elemento del sistema, y en la estimación de la vida útil del banco de baterías. Además, las estrategias para la gestión de la demanda, presentadas en este trabajo de investigación, pueden ayudar a que los usuarios de sistemas aislados o conectados a la red eléctrica realicen un uso eficiente de las fuentes de energía locales, y adapten sus patrones de consumo de electricidad a su condición económica actual

Decision support for participation in electricity markets considering the transaction of services and electricity at the local level

[EN] The growing concerns regarding the lack of fossil fuels, their costs, and their impact on the environment have led governmental institutions to launch energy policies that promote the increasing installation of technologies that use renewable energy sources to generate energy. The increasing penetration of renewable energy sources brings a great fluctuation on the generation side, which strongly affects the power and energy system management. The control of this system is moving from hierarchical and central to a smart and distributed approach. The system operators are nowadays starting to consider the final end users (consumers and prosumers) as a part of the solution in power system operation activities. In this sense, the end-users are changing their behavior from passive to active players. The role of aggregators is essential in order to empower the end-users, also contributing to those behavior changes. Although in several countries aggregators are legally recognized as an entity of the power and energy system, its role being mainly centered on representing end-users in wholesale market participation. This work contributes to the advancement of the state-of-the-art with models that enable the active involvement of the end-users in electricity markets in order to become key participants in the management of power and energy systems. Aggregators are expected to play an essential role in these models, making the connection between the residential end-users, electricity markets, and network operators. Thus, this work focuses on providing solutions to a wide variety of challenges faced by aggregators. The main results of this work include the developed models to enable consumers and prosumers participation in electricity markets and power and energy systems management. The proposed decision support models consider demand-side management applications, local electricity market models, electricity portfolio management, and local ancillary services. The proposed models are validated through case studies based on real data. The used scenarios allow a comprehensive validation of the models from different perspectives, namely end-users, aggregators, and network operators. The considered case studies were carefully selected to demonstrate the characteristics of each model, and to demonstrate how each of them contributes to answering the research questions defined to this work.[ES] La creciente preocupación por la escasez de combustibles fósiles, sus costos y su impacto en el medio ambiente ha llevado a las instituciones gubernamentales a lanzar políticas energéticas que promuevan la creciente instalación de tecnologías que utilizan fuentes de energía renovables para generar energía. La creciente penetración de las fuentes de energía renovable trae consigo una gran fluctuación en el lado de la generación, lo que afecta fuertemente la gestión del sistema de potencia y energía. El control de este sistema está pasando de un enfoque jerárquico y central a un enfoque inteligente y distribuido. Actualmente, los operadores del sistema están comenzando a considerar a los usuarios finales (consumidores y prosumidores) como parte de la solución en las actividades de operación del sistema eléctrico. En este sentido, los usuarios finales están cambiando su comportamiento de jugadores pasivos a jugadores activos. El papel de los agregadores es esencial para empoderar a los usuarios finales, contribuyendo también a esos cambios de comportamiento. Aunque en varios países los agregadores están legalmente reconocidos como una entidad del sistema eléctrico y energético, su papel se centra principalmente en representar a los usuarios finales en la participación del mercado mayorista. Este trabajo contribuye al avance del estado del arte con modelos que permiten la participación activa de los usuarios finales en los mercados eléctricos para convertirse en participantes clave en la gestión de los sistemas de potencia y energía. Se espera que los agregadores desempeñen un papel esencial en estos modelos, haciendo la conexión entre los usuarios finales residenciales, los mercados de electricidad y los operadores de red. Por lo tanto, este trabajo se enfoca en brindar soluciones a una amplia variedad de desafíos que enfrentan los agregadores. Los principales resultados de este trabajo incluyen los modelos desarrollados para permitir la participación de los consumidores y prosumidores en los mercados eléctricos y la gestión de los sistemas de potencia y energía. Los modelos de soporte de decisiones propuestos consideran aplicaciones de gestión del lado de la demanda, modelos de mercado eléctrico local, gestión de cartera de electricidad y servicios auxiliares locales. Los modelos propuestos son validan mediante estudios de casos basados en datos reales. Los escenarios utilizados permiten una validación integral de los modelos desde diferentes perspectivas, a saber, usuarios finales, agregadores y operadores de red. Los casos de estudio considerados fueron cuidadosamente seleccionados para demostrar las características de cada modelo y demostrar cómo cada uno de ellos contribuye a responder las preguntas de investigación definidas para este trabajo