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With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution networks, making this difficult. This paper proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters. In particular, the proposed method infers existing LV network capacity, as well as losses, across scenarios where only a limited number of customers have Smart Meters installed. Typical daily load profiles across customers with Smart Meters are learned using a Dirichlet sampled Gaussian mixture model (GMM). Learned profiles are then applied to all unmetered customers to estimate network parameters. Method accuracy is assessed by comparing estimations with simulated, fully observed, LV network models. The method is also compared to benchmark models for establishing unobserved demand profiles. Overall, results in the paper show that the proposed method outperforms benchmark models in terms of accurately assessing substation headroom, particularly in scenarios where only 10–50% of customers have Smart Meters installed. [ABSTRACT FROM AUTHOR]

Phase imbalance in the U.K. and European low-voltage (415 V, LV) distribution networks causes additional energy losses. A key barrier against understanding the imbalance-induced energy losses is the absence of high-resolution time-series data for LV networks. It remains a challenge to estimate imbalance-induced energy losses in LV networks that only have the yearly average currents of the three phases. To address this insufficient data challenge, this paper proposes a new customized statistical approach, named as the clustering, classification, and range estimation (CCRE) approach. It finds a match between the network with only the yearly average phase currents (the data-scarce network) and a cluster of networks with time series of phase current data (data-rich networks). Then, CCRE performs a range estimation of the imbalance-induced energy loss for the cluster of data-rich networks that resemble the data-scarce network. Chebyshev's inequality is applied to narrow down this range, which represents the confidence interval of the imbalance-induced energy loss for the data-scarce network. Case studies reveal that, given such a few data from the data-scarce networks, more than 80% of these networks are classified to the correct clusters and the confidence of the imbalance-induced energy loss estimation is 89%. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper extends a popular no-arbitrage affine term structure model to jointly model bond markets and exchange rates across the UK, USA and euro area. Using a monthly data set of forward rates from 1992, we first demonstrate that two global factors account for a significant proportion in the variation of bond yields across countries. We also show that, for an explanation of country-specific movements in yield curves, local factors are required. Although we implement a very general factor structure, we find that our global factors are related to global inflation and global economic activity, whereas local factors are closely linked to monetary policy rates. In this respect, our results are similar to previous work. But an important advantage of our joint international model is that we are able to decompose interest rates into risk-free rates and risk premia. Additionally, we are able to study the implications for exchange rates. We show that whereas differences in risk-free rates matter, to a large extent, changes in the exchange rate are determined by time-varying exchange rate risk premia. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Urban areas are concentrations of flood risk because of the density of development and because they tend to be constructed in low-lying areas. They may be subject to flooding from rivers or the sea but are also vulnerable to the effects of intense direct rainfall, which can overwhelm urban drainage systems, and cause complex and often localised patterns of pluvial flooding. The risk from pluvial flooding is particularly difficult to assess because it is sensitive to the spatial-temporal characteristics of rainfall, local run-off and surface flow processes, the performance of urban drainage systems, and the exact location of buildings. Sampling the variability or uncertainty in all of these processes in order to generate accurate flood risk estimates quickly becomes computationally prohibitive, especially for large urban areas. In this paper, we evaluate alternative approaches for making use of high-resolution spatial-temporal rainfall simulations in urban flood risk analysis. Flood depths are computed with a coupled sewer and surface flood model, and flood damage is estimated using standard depth-damage criteria. Efficient sampling of rainfall events and judicious use of response surfaces that relate rainfall event properties to flood volumes and damages are evaluated and shown to reduce the computational expense of risk analysis by more than 70%. The risk analysis methodology is successfully demonstrated for two contrasting urban locations in the UK. [ABSTRACT FROM AUTHOR]

This paper describes the development of linear regression models to predict the construction cost of buildings, based on 286 sets of data collected in the United Kingdom. Raw cost is rejected as a suitable dependent variable and models are developed for cost/m2, log of cost, and log of cost/m2. Both forward and backward stepwise analyses were performed, giving a total of six models. Forty-one potential independent variables were identified. Five variables appeared in each of the six models: gross internal floor area (GIFA), function, duration, mechanical installations, and piling, suggesting that they are the key linear cost drivers in the data. The best regression model is the log of cost backward model which gives an R2 of 0.661 and a mean absolute percentage error (MAPE) of 19.3%; these results compare favorably with past research which has shown that traditional methods of cost estimation have values of MAPE typically in the order of 25%. [ABSTRACT FROM AUTHOR]

Assessing the costs and benefits of any building – let alone a green one – can be elusive. This paper considers the triple bottom line of economic, human and environmental costs and benefits. It identifies inertia in the system; perceived and actual risks; areas in which value can most easily be added or subtracted at various stages in the process; and where improvements might be made. Much of the supporting information comes from recent studies of occupied UK buildings, and may not represent the situation in other countries. However, there appear to be growing similarities in today's globalizing market. L'évaluation du coût et des atouts d'une construction peut s'avérer difficile (sans parler d'un bâtiment écologique). Le présent document examine le coût et les avantages d'un triple point de vue : économique, humain et environnemental. Il identifie l'inertie du système, les risques perçus et actuels, les secteurs où de la valeur peut être le plus facilement ajoutée ou soustraite différents niveaux du processus et les endroits oùdes améliorations pourront être apportées. La majoritédes informations proviennent d'études récentes menées sur des bâtiments britanniques occupés et pourront ne pas être représentatives de la situation réelle existant dans d'autres pays. Néanmoins, de plus en plus de similitudes apparaissent dans le marchémondial actuel. [ABSTRACT FROM AUTHOR]

This article develops a set of inferential methods for functional factor models that have been extensively used in modelling yield curves. Our setting accommodates both temporal dependence and heteroskedasticity. First, we introduce an estimation approach based on minimizing the least‐squares loss function and establish the consistency and asymptotic normality of the estimators. Second, we propose a goodness‐of‐fit test that allows us to determine whether a specific model fits the data. We derive the asymptotic distribution of the test statistics, and this leads to a significance test. A simulation study establishes the good finite‐sample performance of our inferential methods. An application to US and UK yield curves demonstrates the generality of our framework, which can accommodate both sparsely and densely observed yield curves. [ABSTRACT FROM AUTHOR]

Abstract: Classical approaches to estimating the rate of occurrence of events perform poorly when data are few. Maximum likelihood estimators result in overly optimistic point estimates of zero for situations where there have been no events. Alternative empirical-based approaches have been proposed based on median estimators or non-informative prior distributions. While these alternatives offer an improvement over point estimates of zero, they can be overly conservative. Empirical Bayes procedures offer an unbiased approach through pooling data across different hazards to support stronger statistical inference. This paper considers the application of Empirical Bayes to high consequence low-frequency events, where estimates are required for risk mitigation decision support such as as low as reasonably possible. A summary of empirical Bayes methods is given and the choices of estimation procedures to obtain interval estimates are discussed. The approaches illustrated within the case study are based on the estimation of the rate of occurrence of train derailments within the UK. The usefulness of empirical Bayes within this context is discussed. [Copyright &y& Elsevier]

With an increasingly competitive global market, the UK construction industry finally realised that in order to survive, a marked increase in efficiency and effectiveness have to be achieved in all areas. This paper will describe the UK's approach to planning and designing the concrete operations that form a major part of many civil engineering construction projects. A productivity study has been carried out on three different construction projects and over 200 concrete pours have been observed. The data and knowledge collected on site have been subjected to lean construction philosophies, producing a "lean" measure of productivity" and it has been shown that major productivity increases could be achieved by implementing several relatively simple principles. [ABSTRACT FROM AUTHOR]

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this procedure is strongly operator-dependent, cumbersome, and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation; 2) inter-slice motion detection; 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method—integrating both regression and structured classification models—to extract landmarks and probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank and on 100 cases from the UK Digital Heart Project and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g., on UK Biobank, sensitivity and specificity, respectively, 88% and 99% for heart coverage estimation and 85% and 95% for motion detection), allowing their exclusion from the analyzed dataset or the triggering of a new acquisition. [ABSTRACT FROM AUTHOR]

Purpose - Both quantity surveyors (QSs) and estimators have pivotal roles in the profitability of construction contracts; however, only the QS is classified as a professional. Preliminary examination of the responsibilities of the estimator and the level of qualification required in comparison with the QS suggests that the current failure to recognise estimating as a profession may be an anomaly. The purpose of this inquiry is to consider the role of the estimator relative to the accepted criteria attaching to professional status. Design/methodology/approach - To achieve this, available definitions of a profession were distilled into core criteria which were then used to compare QSs and estimators in terms of remuneration, education and professional representation. Findings - A statistical analysis on each of these criteria suggested that there was no significant difference in terms of salary offered or education required by employers. Further investigation, however, did reveal a considerable difference in educational and professional opportunities available to QSs. With regard to the key defining criteria of a profession and the consideration given by employers, the results indicate that there is no difference between the two groups. Originality/value - Given the information revealed by this research it may therefore be the case that the current standing of estimators should be reconsidered with the purpose of upgrading estimating to a professional status. [ABSTRACT FROM AUTHOR]