Diane Palmer

Association of Deprivation with Worse Outcomes in Chronic Kidney Disease: Findings from a Hospital-Based Cohort in the United Kingdom

Background: Chronic kidney disease (CKD) prevalence and complications are known to be associated with deprivation, but there is limited understanding of the underlying reasons for inequalities. Aims: To evaluate the association of both individual and area level socioeconomic status (SES) with heavy proteinuria at presentation, progression of CKD, end-stage renal disease (ESRD) and death. Methods: A retrospective study of 918 CKD patients using integral multivariate logistic regression to adjust for known clinical and demographic explanatory variables. Results: During 3 years of median follow-up, 34% of the study population had progression of their CKD and of these, 32% experienced rapid progression. 23% presented with heavy proteinuria (urine protein:creatinine ratio ≥300 mg/mmol), 4% developed ESRD requiring renal replacement therapy and 10% died. Area level deprivation was independently associated with heavy proteinuria, progression and rapid progression of CKD. People living in the most deprived areas were more likely to develop ESRD. Unskilled professionals were more likely to experience a higher mortality rate. Conclusion: Area level SES is inversely associated with both heavy proteinuria on presentation and progression as well as rapid progression of CKD. In contrast, individual level SES, unskilled professionals found to have a marginally significant association with increased risk of mortality. People living in more deprived areas presenting with CKD are likely to be at increased risk of poor outcomes and may need more active management and earlier referral.

Extensive validation of CM SAF surface radiation products over Europe

This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to 2015. The products used have been developed by the Satellite Application Facility on Climate Monitoring (CM SAF) and are one geostationary climate dataset (SARAH-JRC), one polar-orbiting climate dataset (CLARA-A2) and one geostationary operational product. Further, the ERA-Interim reanalysis is also included in the comparison. The main objective is to determine the quality level of the daily means of CM SAF datasets, identifying their limitations, as well as analyzing the different factors that can interfere in the adequate validation of the products. The quality of the pyranometer was the most critical source of uncertainty identified. In this respect, the use of records from Second Class pyranometers and silicon-based photodiodes increased the absolute error and the bias, as well as the dispersion of both metrics, preventing an adequate validation of the daily means. The best spatial estimates for the three datasets were obtained in Central Europe with a Mean Absolute Deviation (MAD) within 8–13 W/m2, whereas the MAD always increased at high-latitudes, snow-covered surfaces, high mountain ranges and coastal areas. Overall, the SARAH-JRCs accuracy was demonstrated over a dense network of stations making it the most consistent dataset for climate monitoring applications. The operational dataset was comparable to SARAH-JRC in Central Europe, but lacked of the temporal stability of climate datasets, while CLARA-A2 did not achieve the same level of accuracy despite predictions obtained showed high uniformity with a small negative bias. The ERA-Interim reanalysis shows the by-far largest deviations from the surface reference measurements.

Assessment of PV System Performance with Incomplete Monitoring Data

An analysis of PV system performance requires both meteorological and electrical data for the assessment period. However, actual in-field data acquisition is rarely 100%, often resulting in a significant amount of incomplete data sets for performance assessment. These gaps, if not taken into account, may add noticeable bias in yield assessment and thus estimations of the lacking data need to be made. An approach of back-filling the required data is given and validated here. Three different categories of data loss are identified and case-specific methods of synthesising missing data are developed. The integrity of the performance assessment process is assessed. The three cases of data loss are defined as: missing meteorological data only, missing electrical monitoring data only and missing both electrical and meteorological data. Case-specific methods are proposed and their performance against measured data is evaluated statistically by means of: root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE). The inferred monthly performance ratio on two of the selected cases showed accurate agreement against measured data presenting significantly low MBE values, equal or less than -0.01.

Detection of roof shading for PV based on LiDAR data using a multi-modal approach

There is a current drive to increase rooftop deployment of PV. Suitable roofs need to be located, especially as regards shading. A shadow cast on one small section of a solar panel can disproportionately undermine output of the entire system. Nevertheless, few shading figures are available to researchers and developers. This paper reviews and categorizes a number of methods of determining shade losses on photovoltaic systems. Two existing methods are tested on case study areas: shadow simulation from buildings and ambient occlusion. The first is conceptually simple and was found to be useful where data is limited. The second is slightly more demanding in terms of data input and mathematical models. It produces attractive shadow maps but is intended for speed and represents an approximation to ray-tracing. Accordingly, a new model was developed which is fast, flexible and accurately models solar radiation.

Modelling and prediction of PV module energy yield

Abstract At the heart of a photovoltaic (PV) system model is the modelling of the actual PV module, which is a group of PV cells in a weatherproof laminate. This chapter describes the physical and empirical approaches which are commonly used and why different applications favour certain models. The main input parameters for these models are described with a brief discussion of the commonly used datasets. The operating environment for PV is discussed alongside analysis of the primary variables and physical factors affecting net yield and generation time, with an overview of modelling techniques for these effects. An overview is given of advanced considerations such as mismatch and shading. Shading models of varying complexity are discussed, noting the assumptions and simplifications used in many commercial software packages in order to reduce computational time. Finally, a discussion of the modelling uncertainties finds that the greatest source of uncertainty lies with the accuracy of input data, such as the reference environmental conditions and predicted degradation rate. The chapter concludes that, for the most part, it is not the choice of model that makes the greatest contribution to modelling uncertainty but the input data. Therefore input data quality should be the focus for further reductions in modelling uncertainty and the associated project financial risks.

Inference of missing PV monitoring data using neural networks

Complete photovoltaic monitoring data are required in order to evaluate PV system performance and to ensure confidence in project financing. Monitoring sub-system failures are common occurrences, reducing data availability in meteorological and electrical datasets. A reliable backfilling method can be applied in order to mitigate the impact of long monitoring gaps on system state and performance assessment. This paper introduces a method of inferring in-plane irradiation from remotely obtained global horizontal irradiation, by means of a neural network approach. Generation output is then calculated utilizing a simple electrical model with fitted coefficients. The proposed method is applied to a UK case study for which the mean absolute error in monthly system output was reduced significantly, to as low as 0.9%. This yielded more accurate results in backfilling the missing datasets when compared to standard approaches. The impact of missing data on monthly performance ratio is also investigated. Using backfilling to synthesize lost data increases performance ratio prediction accuracy significantly when compared to simply omitting such periods from the calculation.

A fast and effective approach to modelling solar energy potential in complex shading environments

A fast and effective model for the computation of solar energy potential in complex shading environments is presented. Accurate calculation and identification of solar energy potential profiles is demonstrated over large areas. Calculation time is exceptionally fast, even on an average specification PC (typically under 1 min per 1 km2). Problems with commonly used low-resolution sky domes that can lead to irradiance calculation errors of ~5% are identified. Ideal placements are easily visually identified from resultant irradiance/irradiation profile images. Image processing techniques for spatially distributed optimization problems are described and an example of energy value optimization is presented by means of individual dwelling demand separation & comparison.