Spatio-temporal Modeling and Analysis for Wind Energy Applications

The promising potential of wind energy as a source for carbon-free electricity is still hampered by the uncertainty and limited predictability of the wind resource. The overarching theme of this dissertation is to leverage the advancements in statistical learning for developing a set of physics-informed statistical methods that can enrich our understanding of local wind dynamics, enhance our predictions of the wind resource and associated power, and ultimately assist in making better operational decisions. At the heart of the methods proposed in this dissertation, the wind field is modeled as a stochastic spatio-temporal process. Specifically, two sets of methods are presented. The first set of methods is concerned with the statistical modeling and analysis of the transport effect of wind—a physical property related to the prevailing flow of wind in a certain dominant direction. To unearth the influence of the transport effect, a statistical tool called the spatio-temporal lens is proposed for understanding the complex spatio-temporal correlations and interactions in local wind fields. Motivated by the findings of the spatio-temporal lens, a statistical model is proposed, which takes into account the transport effect in local wind fields by characterizing the spatial and temporal dependence in tandem. Substantial improvements in the accuracy of wind speed and power forecasts are achieved relative to several existing data-driven approaches. The second part of this dissertation comprises the development of an advanced spatio-temporal statistical model, called the calibrated regime-switching model. The proposed model captures the regime-switching dynamics in wind behavior, which are often reflected in sudden power generation ramps. Tested on 11 months of data, double-digit improvements in the accuracy of wind speed and power forecasts are achieved relative to six approaches in the wind forecasting literature. This dissertation contributes to both methodology development and wind energy applications. From a methodological point of view, the contributions are relevant to the literatures on spatiotemporal statistical learning and regime-switching modeling. On the application front, these methodological innovations can minimize the uncertainty associated with the large-scale integration of wind energy in power systems, thus, ultimately boosting the economic outlook of wind energy

Time dissemination and synchronization methods to support Galileo timing interfaces

Precise timing is an important factor in the modern information-oriented society and culture. Timing is one of the key technologies for such basic and everyday things, like cellular communications, Internet, satellite navigation and many others. Satellite navigation systems offer cost-efficient and high-performance timing services, and GPS is presently the unchallenged market leader. However, GPS is under military control and does not offer availability and performance guarantees. From a user perspective, this situation will change with the advent of the European satellite navigation system Galileo which shall be operated on a commercial basis by civil entities and shall accept certain liabilities for its services providing also guaranteed service performances. This work is motivated by the new opportunities and challenges related to Galileo timekeeping and applications, and in particular by the necessity to (a) produce and maintain a stable, accurate and robust system timescale which can serve for both accurate prediction of satellite clocks and for the metrological purposes, (b) establish accurate and reliable timing interface to GPS to facilitate Galileo interoperability, (c) maximize user benefits from the new system features like service guarantees and support application development by enabling their certification. The thesis starts with overview of atomic clocks, timekeeping and timing applications. Further Galileo project and system architecture are described and details on Galileo timekeeping concept are given. In addition, the state-of-the-art timekeeping and time dissemination methods and algorithms are presented. Main findings of the thesis focus on (a) Galileo timekeeping. Various options for generation of Galileo system time are proposed and compared with respect to the key performance parameters (stability and reliability). Galileo System Time (GST) stability requirements driven by its navigation and metrological functions are derived. In addition, achievable level of GST stability (considering hardware components) is analyzed. Further, optimization of the present baseline with respect to the design of Galileo Precise Timing Facility (PTF), and its redundancy and switching concepts is undertaken. Finally, performance analysis of different options for generation of the ensemble time is performed and considerations with respect to the role of the ensemble time in Galileo are provided, (b) GPS Galileo timing interface. The magnitude and statistical properties of the time offset are investigated and the impact of the time offset onto the user positioning and timing accuracy is studied with the help of simulated GPS and Galileo observations. Here a novel simulation concept which is based on utilization of GPS data and their scaling for Galileo is proposed. Both GPS and Galileo baseline foresees that the GPS/Galileo time offset shall be determined and broadcast to users in the navigation messages. For this purposes, the offset shall be predicted using available measurement data. Simulations of GPS Galileo time offset determination and prediction are presented. The prediction is made relying on both traditional method and on the advanced techniques like Box-Jenkins prediction (based on the autoregressive moving average approach) and Kalman filter. The end-to-end budgets for different options of GPS Galileo time offset determination are also presented. (c) Galileo interface to timing users (Galileo timing service). The relevance of GST restitution from the metrological point of view is discussed and recognition of GST as a legal time reference is proposed. Assessment of the accuracy of the Galileo timing service is presented. Finally, recommendations for Galileo are provided based on the findings of the thesis

Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009

Dieser Tagungsband enthält die Beiträge des 19. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 2.-4. Dezember 2009 im Haus Bommerholz bei Dortmund stattfindet

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available