Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

The effectiveness of using patient-reported outcome measures as quality improvement tools

Aim: To investigate the value of using PROMs as quality improvement tools. Methods: Two systematic reviews were undertaken. The first reviewed the quantitative literature on the impact of PROMs feedback and the second reviewed the qualitative literature on the use of PROMs in practice. These reviews informed the focus of the primary research. A cluster randomised controlled trial (PROFILE) examined the impact of providing peer benchmarked PROMs feedback to consultant orthopaedic surgeons on improving outcomes for hip replacement surgery. Qualitative interviews with surgeons in the intervention arm of the trial examined the view of and reactions to the feedback. Results: The quantitative review of 17 studies found weak evidence to suggest that providing PROMs feedback to professionals improves patient outcomes. The qualitative review of 16 studies identified the barriers and facilitators to the use of PROMs based on four themes: practical considerations, attitudes towards the data, methodological concerns and the impact of feedback on care. The PROFILE trial included 11 surgeons and 215 patients in the intervention arm, and 10 surgeons and 217 patients in the control arm. The trial found no significant difference in the Oxford Hip Score between the arms (-0.7, 95% CI -1.9-0.5, p=0.2). Interviews with surgeons revealed mixed opinions about the value of the PROMs feedback and the information did not promote explicit changes to their practice. Conclusion: It is important to use PROMs which have been validated for the specific purpose of performance measurement, consult with professionals when developing a PROMs feedback intervention, communicate with professionals about the objectives of the data collection, educate professionals on the properties and interpretation of the data, and support professionals in using the information to improve care. It is also imperative that the burden of data collection and dissemination of the information is minimised.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

R5 clade C SHIV strains with tier 1 or 2 neutralization sensitivity: tools to dissect env evolution and to develop AIDS vaccines in primate models.

BACKGROUND: HIV-1 clade C (HIV-C) predominates worldwide, and anti-HIV-C vaccines are urgently needed. Neutralizing antibody (nAb) responses are considered important but have proved difficult to elicit. Although some current immunogens elicit antibodies that neutralize highly neutralization-sensitive (tier 1) HIV strains, most circulating HIVs exhibiting a less sensitive (tier 2) phenotype are not neutralized. Thus, both tier 1 and 2 viruses are needed for vaccine discovery in nonhuman primate models. METHODOLOGY/PRINCIPAL FINDINGS: We constructed a tier 1 simian-human immunodeficiency virus, SHIV-1157ipEL, by inserting an "early," recently transmitted HIV-C env into the SHIV-1157ipd3N4 backbone [1] encoding a "late" form of the same env, which had evolved in a SHIV-infected rhesus monkey (RM) with AIDS. SHIV-1157ipEL was rapidly passaged to yield SHIV-1157ipEL-p, which remained exclusively R5-tropic and had a tier 1 phenotype, in contrast to "late" SHIV-1157ipd3N4 (tier 2). After 5 weekly low-dose intrarectal exposures, SHIV-1157ipEL-p systemically infected 16 out of 17 RM with high peak viral RNA loads and depleted gut CD4+ T cells. SHIV-1157ipEL-p and SHIV-1157ipd3N4 env genes diverge mostly in V1/V2. Molecular modeling revealed a possible mechanism for the increased neutralization resistance of SHIV-1157ipd3N4 Env: V2 loops hindering access to the CD4 binding site, shown experimentally with nAb b12. Similar mutations have been linked to decreased neutralization sensitivity in HIV-C strains isolated from humans over time, indicating parallel HIV-C Env evolution in humans and RM. CONCLUSIONS/SIGNIFICANCE: SHIV-1157ipEL-p, the first tier 1 R5 clade C SHIV, and SHIV-1157ipd3N4, its tier 2 counterpart, represent biologically relevant tools for anti-HIV-C vaccine development in primates.

Crafting analytical tools to study institutional change

Most powerful analytical tools used in the social sciences are well suited for studying static situations. Static and mechanistic analysis, however, is not adequate to understand the changing world in which we live. In order to adequately address the most pressing social and environmental challenges looming ahead, we need to develop analytical tools for analyzing dynamic situations -particularly institutional change. In this paper, we develop an analytical tool to study institutional change, more specifically, the evolution of rules and norms. We believe that in order for such an analytical tool to be useful to develop a general theory of institutional change, it needs to enable the analyst to concisely record the processes of change in multiple specific settings so that lessons from such settings can eventually be integrated into a more general predictive theory of change. Copyright © The JOIE Foundation 2010.

Can We Learn in the Sandbox Together?: Interprofessional Case Conferences as Facilitation Tools

Duke Medicine utilized interprofessional case conferences (ICCs) from 2008-2012 with the objective of modeling and facilitating development of teamwork skills among diverse health profession students, including physical therapy, physician assistant, medical doctor and nursing. The purpose of this publication was to describe the operational process used to develop and implement the ICCs and measure the success of the ICCs in order to shape future work. The ICCs were offered to develop skills and attitudes essential for participation in healthcare teams. Students were facilitated by faculty of different professions to conduct a comprehensive historical assessment of a standardized patient (SP), determine pertinent physical and lab assessments to undertake, and develop and share a comprehensive management plan. Cases included patient problems that were authentic and relevant to each professional student in attendance. The main barriers to implementation are outlined and the focus on the process of working together is highlighted. Evaluation showed high satisfaction rates among participants and the outcomes from these experiences are presented. The limitations of these results are discussed and recommendations for future assessment are emphasized. The ICCs demonstrated that students will come together voluntarily to learn in teams, even at a research-focused institution, and express benefit from the collaborative exercise.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants.

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated which promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests that behavioral signs can be observed late in the first year of life. Many of these studies involve extensive frame-by-frame video observation and analysis of a child's natural behavior. Although nonintrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are burdensome for clinical and large population research purposes. This work is a first milestone in a long-term project on non-invasive early observation of children in order to aid in risk detection and research of neurodevelopmental disorders. We focus on providing low-cost computer vision tools to measure and identify ASD behavioral signs based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure responses to general ASD risk assessment tasks and activities outlined by the AOSI which assess visual attention by tracking facial features. We show results, including comparisons with expert and nonexpert clinicians, which demonstrate that the proposed computer vision tools can capture critical behavioral observations and potentially augment the clinician's behavioral observations obtained from real in-clinic assessments.

Computer vision tools for the non-invasive assessment of autism-related behavioral markers

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated that promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests behavioral markers can be observed late in the first year of life. Many of these studies involved extensive frame-by-frame video observation and analysis of a child's natural behavior. Although non-intrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are impractical for clinical and large population research purposes. Diagnostic measures for ASD are available for infants but are only accurate when used by specialists experienced in early diagnosis. This work is a first milestone in a long-term multidisciplinary project that aims at helping clinicians and general practitioners accomplish this early detection/measurement task automatically. We focus on providing computer vision tools to measure and identify ASD behavioral markers based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure three critical AOSI activities that assess visual attention. We augment these AOSI activities with an additional test that analyzes asymmetrical patterns in unsupported gait. The first set of algorithms involves assessing head motion by tracking facial features, while the gait analysis relies on joint foreground segmentation and 2D body pose estimation in video. We show results that provide insightful knowledge to augment the clinician's behavioral observations obtained from real in-clinic assessments.

A COMPARISON OF MARKET CLEARING TOOLS IN ELECTRICITY SYSTEMS

To maintain a strict balance between demand and supply in the US power systems, the Independent System Operators (ISOs) schedule power plants and determine electricity prices using a market clearing model. This model determines for each time period and power plant, the times of startup, shutdown, the amount of power production, and the provisioning of spinning and non-spinning power generation reserves, etc. Such a deterministic optimization model takes as input the characteristics of all the generating units such as their power generation installed capacity, ramp rates, minimum up and down time requirements, and marginal costs for production, as well as the forecast of intermittent energy such as wind and solar, along with the minimum reserve requirement of the whole system. This reserve requirement is determined based on the likelihood of outages on the supply side and on the levels of error forecasts in demand and intermittent generation. With increased installed capacity of intermittent renewable energy, determining the appropriate level of reserve requirements has become harder. Stochastic market clearing models have been proposed as an alternative to deterministic market clearing models. Rather than using a fixed reserve targets as an input, stochastic market clearing models take different scenarios of wind power into consideration and determine reserves schedule as output. Using a scaled version of the power generation system of PJM, a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia, and wind scenarios generated from BPA (Bonneville Power Administration) data, this paper explores a comparison of the performance between a stochastic and deterministic model in market clearing. The two models are compared in their ability to contribute to the affordability, reliability and sustainability of the electricity system, measured in terms of total operational costs, load shedding and air emissions. The process of building the models and running for tests indicate that a fair comparison is difficult to obtain due to the multi-dimensional performance metrics considered here, and the difficulty in setting up the parameters of the models in a way that does not advantage or disadvantage one modeling framework. Along these lines, this study explores the effect that model assumptions such as reserve requirements, value of lost load (VOLL) and wind spillage costs have on the comparison of the performance of stochastic vs deterministic market clearing models.

New tools provide a second look at HDV ribozyme structure, dynamics and cleavage.

The hepatitis delta virus (HDV) ribozyme is a self-cleaving RNA enzyme essential for processing viral transcripts during rolling circle viral replication. The first crystal structure of the cleaved ribozyme was solved in 1998, followed by structures of uncleaved, mutant-inhibited and ion-complexed forms. Recently, methods have been developed that make the task of modeling RNA structure and dynamics significantly easier and more reliable. We have used ERRASER and PHENIX to rebuild and re-refine the cleaved and cis-acting C75U-inhibited structures of the HDV ribozyme. The results correct local conformations and identify alternates for RNA residues, many in functionally important regions, leading to improved R values and model validation statistics for both structures. We compare the rebuilt structures to a higher resolution, trans-acting deoxy-inhibited structure of the ribozyme, and conclude that although both inhibited structures are consistent with the currently accepted hammerhead-like mechanism of cleavage, they do not add direct structural evidence to the biochemical and modeling data. However, the rebuilt structures (PDBs: 4PR6, 4PRF) provide a more robust starting point for research on the dynamics and catalytic mechanism of the HDV ribozyme and demonstrate the power of new techniques to make significant improvements in RNA structures that impact biologically relevant conclusions.

The importance of operating costs about making decisions in farm machine labor contractors services : the case of Cordoba province

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