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. (C) 2011 Elsevier Ltd. All rights reserved.

Experimental Investigation of Stretch Blow Molding, Part 1: Instrumentation in an Industrial Environment

In spite of intensive research, computational modeling of the injection stretch blow molding (ISBM) still cannot match the accuracy of other polymer processes such as injection molding. There is a lack of understanding of the interdependence among the machine parameters set up by the operators, process parameters, material behavior, and the resulting final thickness distribution and performance of the molded product. The work presented in this paper describes a set of instrumentation tools developed for investigation of the ISBM process in an industrial setting. Results are presented showing the pressure and air temperature evolution inside the mold, the stretch rod force and displacement history, and the moment of contact of the polymer with seven discrete locations on the mold.

Endothelial Progenitors as Tools to Study Vascular Disease

Endothelial progenitor cells (EPCs) have great clinical value because they can be used as diagnostic biomarkers and as a cellular therapy for promoting vascular repair of ischaemic tissues. However, EPCs also have an additional research value in vascular disease modelling to interrogate human disease mechanisms. The term EPC is used to describe a diverse variety of cells, and we have identified a specific EPC subtype called outgrowth endothelial cell (OEC) as the best candidate for vascular disease modelling because of its high-proliferative potential and unambiguous endothelial commitment. OECs are isolated from human blood and can be exposed to pathologic conditions (forward approach) or be isolated from patients (reverse approach) in order to study vascular human disease. The use of OECs for modelling vascular disease will contribute greatly to improving our understanding of endothelial pathogenesis, which will potentially lead to the discovery of novel therapeutic strategies for vascular diseases.

Impacts of sewage outfalls on rocky shores: incorporating scale, biotic assemblage structure and variability into monitoring tools.

Coastal systems, such as rocky shores, are among the most heavily anthropogenically-impacted marine ecosystems and are also among the most productive in terms of ecosystem functioning. One of the greatest impacts on coastal ecosystems is nutrient enrichment from human activities such as agricultural run-off and discharge of sewage. The aim of this study was to identify and characterise potential effects of sewage discharges on the biotic diversity of rocky shores and to test current tools for assessing the ecological status of rocky shores in line with the EU Water Framework Directive (WFD). A sampling strategy was designed to test for effects of sewage outfalls on rocky shore assemblages on the east coast of Ireland and to identify the scale of the putative impact. In addition, a separate sampling programme based on the Reduced algal Species List (RSL), the current WFD monitoring tool for rocky shores in Ireland and the UK, was also completed by identifying algae and measuring percent cover in replicate samples on rocky shores during Summer. There was no detectable effect of sewage outfalls on benthic taxon diversity or assemblage structure. However, spatial variability of assemblages was greater at sites proximal or adjacent to sewage outfalls compared to shores without sewage outfalls present. Results based on the RSL, show that algal assemblages were not affected by the presence of sewage outfalls, except when classed into functional groups when variability was greater at the sites with sewage outfalls. A key finding of both surveys, was the prevalence of spatial and temporal variation of assemblages. It is recommended that future metrics of ecological status are based on quantified sampling designs, incorporate changes in variability of assemblages (indicative of community stability), consider shifts in assemblage structure and include both benthic fauna and flora to assess the status of rocky shores.

A critical appraisal of tools available for monitoring epigenetic changes in clinical samples from patients with myeloid malignancies

Research over the past decade has confirmed that epigenetic alterations act in concert with genetic lesions to deregulate gene expression in acute myeloid leukemia and myelodysplastic syndromes. In addition, we now have the capability to pharmaceutically target epigenetic modifications, and there is an urgent need forearly validation of the efficacy of the drugs. Also, an improved understanding of the functionality of epigenetic modifications may further pave the road towards an individualized therapy. Here, we provide the pros and cons of the currently most feasible methods used for characterizing the methylome in clinical samples, and give a brief introduction to novel approaches to sequencing that may revolutionize our abilities to characterize the genomes and epigenomes in acute myeloid leukemia and myelodysplastic syndrome patients.

Predicting organic acid concentration from UV/vis spectrometry measurements - A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.