Investigating the Ability of Smart Electricity Meters to Provide Accurate Low Voltage Network Information to the UK Distribution Network Operators

This research presents a picture of the current status and the future developments of the LV electricity grid and the capabilities of the smart metering programme in the UK as well as investigating the major research trends and priorities in the field of Smart Grid. This work also extensively examines the literature on the crucial LV network performance indicators such as losses, voltage levels, and cable capacity percentages and the ways in which DNOs have been acquiring this knowledge as well the ways in which various LV network applications are carried out and rely on various sources of data. This work combines 2 new smart meter data sets with 5 established methods to predict a proportion of consumer’s data is not available using historical smart meter data from neighbouring smart meters. Our work shows that half-hourly smart meter data can successfully predict the missing general load shapes, but the prediction of peak demands proves to be a more challenging task. This work then investigates the impact of smart meter time resolution intervals and data aggregation levels in balanced and unbalanced three phase LV network models on the accuracy of critical LV network performance indicators and the way in which these inaccuracies affect major smart LV network application of the DNOs in the UK. This is a novel work that has not been carried out before and shows that using low time resolution and aggregated smart meter data in load flow analysis models can negatively affect the accuracy of critical low voltage network estimates

Development and Delivery of Innovative Engineering Degree Apprenticeship Programmes in Collaboration with Industry

Work Based Learning (WBL) programmes such as Degree Apprenticeships (DAs) have the potential to widen participation in Higher Education and transform lives, while also supplying the engineering industry with high calibre workforce that are committed, knowledgeable, skilled, and professional. In 2019, Aston University’s Professional Engineering Centre (APEC) developed a suite of level 6 BEng Professional Engineering DA programmes in line with three existing DA standards and in collaboration with major UK manufacturing companies. The curriculum was designed to be flexible and responsive to employers’ needs while ensuring academic and professional development of apprentices aligned with the Knowledge, Skills, and Behaviour (KSB) requirements of the DA standards. This work details our approach in working with employers to develop effective, efficient, and flexible curricula for three DA Programmes launched in January 2020. The case study also outlines the student support measures put in place as part of the successful delivery of this programme to ensure simultaneous academic and professional growth of the apprentices while ensuring compliance with the KSB and the End Point Assessment (EPA) requirements of the DA standards. The success of the programmes in meeting the needs of over 70 apprentices and 15 employers since January 2020 while meeting the rigorous academic and regulatory requirements of such programmes is appraised using feedback from the University validation panel, employer feedback, and apprentices’ feedback from Module Evaluation Questionnaires (MEQs) scores and comments, tripartite review meetings, and programme committee meetings

Analyzing the ability of Smart Meter Data to Provide Accurate Information to the UK DNOs

By 2020, smart meters will potentially provide the UK's distribution network operators (DNOs) with more detailed information about the real-time status of the low-voltage (LV) network. However, the smart meter data that the DNOs will receive has a number of limitations including the unavailability of some real-time smart meter data, aggregation of smart meter readings to preserve customer privacy, half-hourly averaging of customer demand/generation readings, and the inability of smart meters to identify the connection phases. This research investigates how these limitations of the smart meter data can affect the estimation accuracy of technical losses and voltage levels in the LV network and the ways in which 1 min losses and correct phasing patterns can be determined despite the limitations in smart data

Using grouped smart meter data in phase identification

Access to smart meter data will enable electricity distribution companies to have a far clearer picture of the operation of their low voltage networks. This in turn will assist in the more active management of these networks. An important current knowledge gap is knowing for certain which phase each customer is connected to. Matching the loads from the smart meter with the loads measured on different phases at the substation has the capability to fill this gap. However, in the United Kingdom at the half hourly level only the loads from groups of meters will be available to the network operators. Therefore, a method is described for using this grouped data to assist with determining each customer's phase when the phase of most meters is correctly known. The method is analysed using the load readings from a data set of 96 smart meters. It successfully ranks the mixed phase groups very highly compared with the single phase groups

The Relationship Between Social Support and Self-care in Patients With Heart Failure: The Role of Illness Related Worries as a Mediator

Objectives: Heart failure (HF), as a chronic disease, is a progressive and debilitating problem in communities. In previous studies, the role of self-care in HF has been emphasized. Illness-related worries and social support are associated with self-care. The aim of this study is to explore the role of illness-related worries as a mediator in the relationship between social support and self-care in patients with HF. Materials and Methods: This descriptive and correlational study was conducted on 149 HF patients based on inclusion and exclusion criteria, in 2016. Patients were selected using availability sampling. For data collection, a demographic information questionnaire, self-care behavior scale, illness-related worries questionnaire, and social support scale were used. In addition, the Pearson correlation coefficient and Sobel test were also conducted. Results: Sobel test results was used for understanding the mediating role of illness related-worries (P <0.01, Sobel test result = -5.16). The correlation between social support and self-care was -0.518, P <0.01, and the correlation between illness-related worries and self-care was -0.71, P < 0.01. Conclusions: This study showed that illness related worries can serve as a mediator in the relationship between social support and self-care. There was a significant correlation between the research variables

The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

Background: Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes. Methods: This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95 confidence interval. Q statistics and I2 were then used to calculate heterogeneity. Results: On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95 CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs. Conclusions: Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription. © 2021, The Author(s)

Interventions for treating hyperemesis gravidarum.

BACKGROUND: Hyperemesis gravidarum is a severe form of nausea and vomiting in pregnancy affecting 0.3% to 1.0% of pregnancies, and is one of the most common indications for hospitalization during pregnancy. While a previous Cochrane review examined interventions for nausea and vomiting in pregnancy, there has not yet been a review examining the interventions for the more severe condition of hyperemesis gravidarum. OBJECTIVES: To assess the effectiveness and safety, of all interventions for hyperemesis gravidarum in pregnancy up to 20 weeks\u27 gestation. SEARCH METHODS: We searched the Cochrane Pregnancy and Childbirth Group\u27s Trials Register and the Cochrane Complementary Medicine Field\u27s Trials Register (20 December 2015) and reference lists of retrieved studies. SELECTION CRITERIA: Randomized controlled trials of any intervention for hyperemesis gravidarum. Quasi-randomized trials and trials using a cross-over design were not eligible for inclusion.We excluded trials on nausea and vomiting of pregnancy that were not specifically studying the more severe condition of hyperemesis gravidarum. DATA COLLECTION AND ANALYSIS: Two review authors independently reviewed the eligibility of trials, extracted data and evaluated the risk of bias. Data were checked for accuracy. MAIN RESULTS: Twenty-five trials (involving 2052 women) met the inclusion criteria but the majority of 18 different comparisons described in the review include data from single studies with small numbers of participants. The comparisons covered a range of interventions including acupressure/acupuncture, outpatient care, intravenous fluids, and various pharmaceutical interventions. The methodological quality of included studies was mixed. For selected important comparisons and outcomes, we graded the quality of the evidence and created \u27Summary of findings\u27 tables. For most outcomes the evidence was graded as low or very low quality mainly due to the imprecision of effect estimates. Comparisons included in the \u27Summary of findings\u27 tables are described below, the remaining comparisons are described in detail in the main text.No primary outcome data were available when acupuncture was compared with placebo, There was no clear evidence of differences between groups for anxiodepressive symptoms (risk ratio (RR) 1.01, 95% confidence interval (CI) 0.73 to 1.40; one study, 36 women, very low-quality evidence), spontaneous abortion (RR 0.48, 95% CI 0.05 to 5.03; one study, 57 women, low-quality evidence), preterm birth (RR 0.12, 95% CI 0.01 to 2.26; one study, 36 women, low-quality evidence), or perinatal death (RR 0.57, 95% CI 0.04 to 8.30; one study, 36 women, low-quality evidence).There was insufficient evidence to identify clear differences between acupuncture and metoclopramide in a study with 81 participants regarding reduction/cessation in nausea or vomiting (RR 1.40, 95% CI 0.79 to 2.49 and RR 1.51, 95% CI 0.92 to 2.48, respectively; very low-quality evidence).In a study with 92 participants, women taking vitamin B6 had a slightly longer hospital stay compared with placebo (mean difference (MD) 0.80 days, 95% CI 0.08 to 1.52, moderate-quality evidence). There was insufficient evidence to demonstrate a difference in other outcomes including mean number of episodes of emesis (MD 0.50, 95% CI -0.40 to 1.40, low-quality evidence) or side effects.A comparison between metoclopramide and ondansetron identified no clear difference in the severity of nausea or vomiting (MD 1.70, 95% CI -0.15 to 3.55, and MD -0.10, 95% CI -1.63 to 1.43; one study, 83 women, respectively, very low-quality evidence). However, more women taking metoclopramide complained of drowsiness and dry mouth (RR 2.40, 95% CI 1.23 to 4.69, and RR 2.38, 95% CI 1.10 to 5.11, respectively; moderate-quality evidence). There were no clear differences between groups for other side effects.In a single study with 146 participants comparing metoclopramide with promethazine, more women taking promethazine reported drowsiness, dizziness, and dystonia (RR 0.70, 95% CI 0.56 to 0.87, RR 0.48, 95% CI 0.34 to 0.69, and RR 0.31, 95% CI 0.11 to 0.90, respectively, moderate-quality evidence). There were no clear differences between groups for other important outcomes including quality of life and other side effects.In a single trial with 30 women, those receiving ondansetron had no difference in duration of hospital admission compared to those receiving promethazine (MD 0.00, 95% CI -1.39 to 1.39, very low-quality evidence), although there was increased sedation with promethazine (RR 0.06, 95% CI 0.00 to 0.94, low-quality evidence) .Regarding corticosteroids, in a study with 110 participants there was no difference in days of hospital admission compared to placebo (MD -0.30, 95% CI -0.70 to 0.10; very low-quality evidence), but there was a decreased readmission rate (RR 0.69, 95% CI 0.50 to 0.94; four studies, 269 women). For other important outcomes including pregnancy complications, spontaneous abortion, stillbirth and congenital abnormalities, there was insufficient evidence to identify differences between groups (very low-quality evidence for all outcomes). In other single studies there were no clear differences between groups for preterm birth or side effects (very low-quality evidence).For hydrocortisone compared with metoclopramide, no data were available for primary outcomes and there was no difference in the readmission rate (RR 0.08, 95% CI 0.00 to 1.28;one study, 40 women).In a study with 80 women, compared to promethazine, those receiving prednisolone had increased nausea at 48 hours (RR 2.00, 95% CI 1.08 to 3.72; low-quality evidence), but not at 17 days (RR 0.81, 95% CI 0.58 to 1.15, very low-quality evidence). There was no clear difference in the number of episodes of emesis or subjective improvement in nausea/vomiting. There was insufficient evidence to identify differences between groups for stillbirth and neonatal death and preterm birth. AUTHORS\u27 CONCLUSIONS: On the basis of this review, there is little high-quality and consistent evidence supporting any one intervention, which should be taken into account when making management decisions. There was also very limited reporting on the economic impact of hyperemesis gravidarum and the impact that interventions may have.The limitations in interpreting the results of the included studies highlights the importance of consistency in the definition of hyperemesis gravidarum, the use of validated outcome measures, and the need for larger placebo-controlled trials

Using smart meters to estimate low voltage losses

Losses on low voltage networks are often substantial. For example, in the UK they have been estimated as being 4% of the energy supplied by low voltage networks. However, the breakdown of the losses to individual conductors and their split over time are poorly understood as generally only the peak demands and average loads over several months have been recorded. The introduction of domestic smart meters has the potential to change this. How domestic smart meter readings can be used to estimate the actual losses is analysed. In particular, the accuracy of using 30 minute readings compared with 1 minute readings, and how this accuracy could be improved, were investigated. This was achieved by assigning the data recorded by 100 smart meters with a time resolution of 1 minute to three test networks. Smart meter data from three sources were used in the investigation. It was found that 30 minute resolution data underestimated the losses by between 9% and 24%. By fitting an appropriate model to the data, it was possible to reduce the inaccuracy by approximately 50%. Having a smart meter time resolution of 10 minutes rather than 30 gave little improvement to the accuracy

Low voltage current estimation using AMI/smart meter data

Knowledge of the currents is a key foundation for smart grid applications. However, knowledge of low voltage currents is generally poor. The new information streams from advanced metering infrastructure (AMI)/smart meters and the monitoring of distribution substations offer the opportunity of rectifying this. Unfortunately, often not all the smart meter readings will be available in real-time. For example, this situation will arise when older (non-compliant) smart meters do not have real-time reporting capabilities. This paper investigates how knowledge of the substation currents can be combined with the available real-time AMI/smart meter readings and the historical readings from the non-real-time meters, to estimate these missing values. It is found that the k-nearest neighbor weighted average approach performs best but that the gains over using simpler methods are relatively modest