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

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

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

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

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

Modelos predictivos basados en deep learning para datos temporales masivos

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111El avance en el mundo del hardware ha revolucionado el campo de la inteligencia artificial, abriendo nuevos frentes y áreas que hasta hoy estaban limitadas. El área del deep learning es quizás una de las mas afectadas por este avance, ya que estos modelos requieren de una gran capacidad de computación debido al número de operaciones y complejidad de las mismas, motivo por el cual habían caído en desuso hasta los últimos años. Esta Tesis Doctoral ha sido presentada mediante la modalidad de compendio de publicaciones, con un total de diez aportaciones científicas en Congresos Internacionales y revistas con alto índice de impacto en el Journal of Citation Reports (JCR). En ella se recoge una investigación orientada al estudio, análisis y desarrollo de las arquitecturas deep learning mas extendidas en la literatura para la predicción de series temporales, principalmente de tipo energético, como son la demanda eléctrica y la generación de energía solar. Además, se ha centrado gran parte de la investigación en la optimización de estos modelos, tarea primordial para la obtención de un modelo predictivo fiable. En una primera fase, la tesis se centra en el desarrollo de modelos predictivos basados en deep learning para la predicción de series temporales aplicadas a dos fuentes de datos reales. En primer lugar se diseñó una metodología que permitía realizar la predicción multipaso de un modelo Feed-Forward, cuyos resultados fueron publicados en el International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC). Esta misma metodología se aplicó y comparó con otros modelos clásicos, implementados de manera distribuida, cuyos resultados fueron publicados en el 14th International Work-Conference on Artificial Neural Networks (IWANN). Fruto de la diferencia en tiempo de computación y escalabilidad del método de deep learning con los otros modelos comparados, se diseñó una versión distribuida, cuyos resultados fueron publicados en dos revistas indexadas con categoría Q1, como son Integrated Computer-Aided Engineering e Information Sciences. Todas estas aportaciones fueron probadas utilizando un conjunto de datos de demanda eléctrica en España. De forma paralela, y con el objetivo de comprobar la generalidad de la metodología, se aplicó el mismo enfoque sobre un conjunto de datos correspondiente a la generación de energía solar en Australia en dos versiones: univariante, cuyos resultados se publicaron en International on Soft Computing Models in Industrial and Environment Applications (SOCO), y la versión multivariante, que fué publicada en la revista Expert Systems, indexada con categoría Q2. A pesar de los buenos resultados obtenidos, la estrategia de optimización de los modelos no era óptima para entornos big data debido a su carácter exhaustivo y al coste computacional que conllevaba. Motivado por esto, la segunda fase de la Tesis Doctoral se basó en la optimización de los modelos deep learning. Se diseñó una estrategia de búsqueda aleatoria aplicada a la metodología propuesta en la primera fase, cuyos resultados fueron publicados en el IWANN. Posteriormente, se centró la atención en modelos de optimización basado en heurísticas, donde se desarrolló un algoritmo genético para optimizar el modelo feed-forward. Los resultados de esta investigación se presentaron en la revista Applied Sciences, indexada con categoría Q2. Además, e influenciado por la situación pandémica del 2020, se decidió diseñar e implementar una heurística basada en el modelo de propagación de la COVID-19. Esta estrategia de optimización se integró con una red Long-Short-Term-Memory, ofreciendo resultados altamente competitivos que fueron publicados en la revista Big Data, indexada en el JCR con categoría Q1. Para finalizar el trabajo de tesis, toda la información y conocimientos adquiridos fueron recopilados en un artículo a modo de survey, que fue publicado en la revista indexada con categoría Q1 Big Data.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

Active power dispatch for supporting grid frequency regulation in wind farms considering fatigue load

This paper proposes an active power control method for supporting grid frequency regulation in wind farms (WF) considering improved fatigue load sensitivity of wind turbines (WT). The control method is concluded into two parts: frequency adjustment control (FAC) and power reference dispatch (PRD). On one hand, the proposed Fuzzy-PID control method can actively maintain the balance between power generation and grid load, by which the grid frequency is regulated when plenty of winds are available. The fast power response can be provided and frequency error can be reduced by the proposed method. On the other hand, the sensitivity of the WT fatigue loads to the power references is improved. The explicit analytical equations of the fatigue load sensitivity are re-derived to improve calculation accuracy. In the process of the optimization dispatch, the re-defined fatigue load sensitivity will be used to minimize fatigue load. Case studies were conducted with a WF under different grid loads and turbulent wind with different intensities. By comparing the frequency response of the WF, rainflow cycle, and Damage Equivalent Load (DEL) of the WT, the efficacy of the proposed method is verified

Hybrid energy storage systems via power electronic converters

In recent years, many research lines have focused their efforts on improving energy efficiency and developing renewable energy sources. In this context, the use of energy storage systems is on the rise, as they can contribute to the integration of renewables to the main electrical grid. However, energy storage systems are divided into high energy or high power devices. Due to the lack of a solution covering both aspects, researchers are forced to find alternatives. The hybridization of different energy storage technologies is presented as a suitable solution for this problem, since it combines high power and high energy within the same system. The main goal of this thesis is the design and implementation of a hybrid energy storage system (HESS), capable of improving the performance provided by a single storage technology. As a first step in this direction, this document reviews and classifies the most relevant HESS topologies found in the literature. This allows a better understanding of the drawbacks and benefits of each configuration. To ensure the optimal use of this HESS, it is essential to design a suitable energy management strategy and a proper power electronic converter control. To this end, the control structure has been analyzed from different approaches. On the one hand there would be the classic multilevel control structure, which usually consists of three levels among which are the operating constraints, the power sharing and at the lowest level the control of the converter. On the other hand there would be the single level control structure in which both, the power distribution and the control of the converter, are integrated within the same level by using modern MPC control algorithms. Finally, three different case studies are presented to show the practical application of the developed control strategies together with the main conclusions of the thesis.Azken urteetan, ikerketa-lerro askok eraginkortasun energetikoa hobetzeko eta energia berriztagarriak garatzeko ahaleginak egin dituzte. Testuinguru honetan, energia metatze sistemen erabilera geroz eta handiagoa da, berriztagarrien integrazioa sare elektrikoarekin erraztu dezaketelako. Hala ere, energia altuko edo potentzia altuko metatze sistemak bakarrik aukeratu daitezke. Horregatik, ikertzaileek alternatiba berriak bilatzera behartuta daude. Energia metatze sistema desberdinen hibridazioa, arazo horri irtenbidea ematen dio. Honekin, potentzia eta energia maila altuak sistema bakar batetan batu daitezke. Tesi honen helburu nagusia, energia metatze sistema hibrido (HESS sigla, ingelesetik Hybrid Energy Storage System) bat diseinatzea eta inplementatzea da. Sistema honek, teknologia bakar batek eskaintzen duen errendimendua hobetzeko gai izan beharko luke. Lehen urratsa bezala, dokumentu honek literaturan aurkitutako topologia hibrido garrantzitsuenak laburbildu eta batzen ditu. Honi esker, konfigurazio bakoitzaren abantaila eta desabantailak hobeto ulertzea ahal da. HESS honen erabilera optimoa bermatzeko, ezinbestekoa da energia kudeatzeko estrategia on bat diseinatzea bihurgailu elektronikoaren kontrol egokiarekin batera. Horretarako, kontrol egitura ikuspegi desberdinetatik aztertuko da. Alde batetik, maila anitzeko kontrol egitura klasikoa egongo litzateke, normalean hiru mailaz osatua dagoena. Goi mailan funtzionamendu eta segurtasun mugak egongo lirateke, erdiko mailan potentzia banaketa, eta azkenik bihurgailuaren maila baxuko kontrola. Bestalde, maila bakarreko kontrol egitura egongo litzateke non mugak, potentzia banaketa eta bihurgailuaren kontrola maila berean integratzen dira kontrol iragarleko algoritmoen bidez (MPC). Azkenik, hiru kasu desberdin aurkezten dira garatutako kontrolen aplikazio praktikoa erakusteko tesiaren ondorio nagusiekin batera.En los últimos años, numerosas líneas de investigación han centrado sus esfuerzos en mejorar la eficiencia energética junto con el desarrollo de fuentes de generación renovables. En este contexto, el uso de sistemas de almacenamiento de energía está al alza, ya que estos pueden contribuir a la integración de las renovables en la red eléctrica convencional. Sin embargo, la necesidad de elegir entre dispositivos de alta energía o alta potencia, obliga a los investigadores a buscar otras alternativas. La hibridación de diferentes sistemas de almacenamiento se presenta como una solución apropiada para este problema, ya que combina alta energía y alta potencia dentro de un mismo sistema. El objetivo principal de esta tesis es el diseño e implementación de un sistema híbrido de almacenamiento de energía (sigla HESS, del inglés Hybrid Energy Storage System), capaz de mejorar las prestaciones que proporcionaría el uso de una única tecnología. Como primer paso en esta dirección, en este documento resume y clasifica las topologías de hibridación más relevantes encontradas en la literatura. Esto permite una mejor comprensión de los beneficios e inconvenientes de cada configuración. Para garantizar el uso óptimo de dicho HESS, es esencial diseñar una estrategia adecuada de gestión de energía junto con un control óptimo del convertidor electrónico de potencia. Para lograr este fin, la estructura de control ha sido analizada desde diferentes enfoques. Por un lado se encontraría la estructura de control multinivel clásica, la cual generalmente consta de tres niveles. En el nivel más alto se encontrarían las restricciones operativas y de seguridad, en el intermedio se encontraría la división de potencia, y por último el control de nivel bajo del convertidor. Por otro lado, se encontraría la estructura de control de un único nivel, en la que tanto las restricciones, el reparto de potencia y el control del convertidor se integran dentro del mismo nivel mediante algoritmos de control predictivo (MPC). Finalmente, se presentan tres casos de estudio para mostrar la aplicación práctica de las estrategias de control desarrolladas junto con las principales conclusiones de la tesis