251 research outputs found
A Novel Power-Band based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification
This paper presents a novel power-band-based data segmentation (PBDS) method
to enhance the identification of meter phase and meter-transformer pairing.
Meters that share the same transformer or are on the same phase typically
exhibit strongly correlated voltage profiles. However, under high power
consumption, there can be significant voltage drops along the line connecting a
customer to the distribution transformer. These voltage drops significantly
decrease the correlations among meters on the same phase or supplied by the
same transformer, resulting in high misidentification rates. To address this
issue, we propose using power bands to select highly correlated voltage
segments for computing correlations, rather than relying solely on correlations
computed from the entire voltage waveforms. The algorithm's performance is
assessed by conducting tests using data gathered from 13 utility feeders. To
ensure the credibility of the identification results, utility engineers conduct
field verification for all 13 feeders. The verification results unequivocally
demonstrate that the proposed algorithm surpasses existing methods in both
accuracy and robustness.Comment: Submitted to the IEEE Transactions on Power Delivery. arXiv admin
note: text overlap with arXiv:2111.1050
A Modified Sequence-to-point HVAC Load Disaggregation Algorithm
This paper presents a modified sequence-to-point (S2P) algorithm for
disaggregating the heat, ventilation, and air conditioning (HVAC) load from the
total building electricity consumption. The original S2P model is convolutional
neural network (CNN) based, which uses load profiles as inputs. We propose
three modifications. First, the input convolution layer is changed from 1D to
2D so that normalized temperature profiles are also used as inputs to the S2P
model. Second, a drop-out layer is added to improve adaptability and
generalizability so that the model trained in one area can be transferred to
other geographical areas without labelled HVAC data. Third, a fine-tuning
process is proposed for areas with a small amount of labelled HVAC data so that
the pre-trained S2P model can be fine-tuned to achieve higher disaggregation
accuracy (i.e., better transferability) in other areas. The model is first
trained and tested using smart meter and sub-metered HVAC data collected in
Austin, Texas. Then, the trained model is tested on two other areas: Boulder,
Colorado and San Diego, California. Simulation results show that the proposed
modified S2P algorithm outperforms the original S2P model and the
support-vector machine based approach in accuracy, adaptability, and
transferability
An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data
This paper presents an independent component analysis (ICA) based
unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC)
load disaggregation using low-resolution (e.g., 15 minutes) smart meter data.
We first demonstrate that electricity consumption profiles on mild-temperature
days can be used to estimate the non-HVAC base load on hot days. A residual
load profile can then be calculated by subtracting the mild-day load profile
from the hot-day load profile. The residual load profiles are processed using
ICA for HVAC load extraction. An optimization-based algorithm is proposed for
post-adjustment of the ICA results, considering two bounding factors for
enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC
energy bounds computed based on the relationship between HVAC load and
temperature to remove unrealistic HVAC load spikes. Second, we exploit the
dependency between the daily nocturnal and diurnal loads extracted from
historical meter data to smooth the base load profile. Pecan Street data with
sub-metered HVAC data were used to test and validate the proposed
methods.Simulation results demonstrated that the proposed method is
computationally efficient and robust across multiple customers
An Iterative Bidirectional Gradient Boosting Algorithm for CVR Baseline Estimation
This paper presents a novel iterative, bidirectional, gradient boosting
(bidirectional-GB) algorithm for estimating the baseline of the Conservation
Voltage Reduction (CVR) program. We define the CVR baseline as the load profile
during the CVR period if the substation voltage is not lowered. The proposed
algorithm consists of two key steps: selection of similar days and iterative
bidirectional-GB training. In the first step, pre- and post-event temperature
profiles of the targeted CVR day are used to select similar days from
historical non-CVR days. In the second step, the pre-event and post-event
similar days are used to train two GBMs iteratively: a forward-GBM and a
backward-GBM. After each iteration, the two generated CVR baselines are
reconciled and only the first and the last points on the reconciled baseline
are kept. The iteration repeats until all CVR baseline points are generated. We
tested two gradient boosting methods (i.e., GBM and LighGBM) with two data
resolutions (i.e., 15- and 30-minute). The results demonstrate that both the
accuracy and performance of the algorithm are satisfactory.Comment: 5 pages, 8 figures, 2 table
MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations
This paper presents a deep-learning framework, Multi-load Generative
Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in
one shot. The main contribution of MultiLoad-GAN is the capture of
spatial-temporal correlations among a group of loads to enable the generation
of realistic synthetic load profiles in large quantity for meeting the emerging
need in distribution system planning. The novelty and uniqueness of the
MultiLoad-GAN framework are three-fold. First, it generates a group of load
profiles bearing realistic spatial-temporal correlations in one shot. Second,
two complementary metrics for evaluating realisticness of generated load
profiles are developed: statistics metrics based on domain knowledge and a
deep-learning classifier for comparing high-level features. Third, to tackle
data scarcity, a novel iterative data augmentation mechanism is developed to
generate training samples for enhancing the training of both the classifier and
the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms
state-of-the-art approaches in realisticness, computational efficiency, and
robustness. With little finetuning, the MultiLoad-GAN approach can be readily
extended to generate a group of load or PV profiles for a feeder, a substation,
or a service area.Comment: Submitted to IEEE Transactions on Smart Gri
Targeted genetic testing for familial hypercholesterolaemia using next generation sequencing:a population-based study
Background<p></p>
Familial hypercholesterolaemia (FH) is a common Mendelian condition which, untreated, results in premature coronary heart disease. An estimated 88% of FH cases are undiagnosed in the UK. We previously validated a method for FH mutation detection in a lipid clinic population using next generation sequencing (NGS), but this did not address the challenge of identifying index cases in primary care where most undiagnosed patients receive healthcare. Here, we evaluate the targeted use of NGS as a potential route to diagnosis of FH in a primary care population subset selected for hypercholesterolaemia.<p></p>
Methods<p></p>
We used microfluidics-based PCR amplification coupled with NGS and multiplex ligation-dependent probe amplification (MLPA) to detect mutations in LDLR, APOB and PCSK9 in three phenotypic groups within the Generation Scotland: Scottish Family Health Study including 193 individuals with high total cholesterol, 232 with moderately high total cholesterol despite cholesterol-lowering therapy, and 192 normocholesterolaemic controls.<p></p>
Results<p></p>
Pathogenic mutations were found in 2.1% of hypercholesterolaemic individuals, in 2.2% of subjects on cholesterol-lowering therapy and in 42% of their available first-degree relatives. In addition, variants of uncertain clinical significance (VUCS) were detected in 1.4% of the hypercholesterolaemic and cholesterol-lowering therapy groups. No pathogenic variants or VUCS were detected in controls.<p></p>
Conclusions<p></p>
We demonstrated that population-based genetic testing using these protocols is able to deliver definitive molecular diagnoses of FH in individuals with high cholesterol or on cholesterol-lowering therapy. The lower cost and labour associated with NGS-based testing may increase the attractiveness of a population-based approach to FH detection compared to genetic testing with conventional sequencing. This could provide one route to increasing the present low percentage of FH cases with a genetic diagnosis
The cost-effectiveness of increasing alcohol taxes: a modelling study
<p>Abstract</p> <p>Background</p> <p>Excessive alcohol use increases risks of chronic diseases such as coronary heart disease and several types of cancer, with associated losses of quality of life and life-years. Alcohol taxes can be considered as a public health instrument as they are known to be able to decrease alcohol consumption. In this paper, we estimate the cost-effectiveness of an alcohol tax increase for the entire Dutch population from a health-care perspective focusing on health benefits and health-care costs in alcohol users.</p> <p>Methods</p> <p>The chronic disease model of the National Institute for Public Health and the Environment was used to extrapolate from decreased alcohol consumption due to tax increases to effects on health-care costs, life-years gained and quality-adjusted life-years gained, A Dutch scenario in which tax increases for beer are planned, and a Swedish scenario representing one of the highest alcohol taxes in Europe, were compared with current practice in the Netherlands. To estimate cost-effectiveness ratios, yearly differences in model outcomes between intervention and current practice scenarios were discounted and added over the time horizon of 100 years to find net present values for incremental life-years gained, quality-adjusted life-years gained, and health-care costs.</p> <p>Results</p> <p>In the Swedish scenario, many more quality-adjusted life-years were gained than in the Dutch scenario, but both scenarios had almost equal incremental cost-effectiveness ratios: €5100 per quality-adjusted life-year and €5300 per quality-adjusted life-year, respectively.</p> <p>Conclusion</p> <p>Focusing on health-care costs and health consequences for drinkers, an alcohol tax increase is a cost-effective policy instrument.</p
Linked randomised controlled trials of face-to-face and electronic brief intervention methods to prevent alcohol related harm in young people aged 14–17 years presenting to Emergency Departments (SIPS junior)
Background: Alcohol is a major global threat to public health. Although the main burden of chronic alcohol-related disease is in adults, its foundations often lie in adolescence. Alcohol consumption and related harm increase steeply from the age of 12 until 20 years. Several trials focusing upon young people have reported significant positive effects of brief interventions on a range of alcohol consumption outcomes. A recent review of reviews also suggests that electronic brief interventions (eBIs) using internet and smartphone technologies may markedly reduce alcohol consumption compared with minimal or no intervention controls.
Interventions that target non-drinking youth are known to delay the onset of drinking behaviours. Web based alcohol interventions for adolescents also demonstrate significantly greater reductions in consumption and harm among ‘high-risk’ drinkers; however changes in risk status at follow-up for non-drinkers or low-risk
drinkers have not been assessed in controlled trials of brief alcohol interventions
Systems analysis of apoptosis protein expression allows the case-specific prediction of cell death responsiveness of melanoma cells.
Many cancer entities and their associated cell line models are highly heterogeneous in their responsiveness to apoptosis inducers and, despite a detailed understanding of the underlying signaling networks, cell death susceptibility currently cannot be predicted reliably from protein expression profiles. Here, we demonstrate that an integration of quantitative apoptosis protein expression data with pathway knowledge can predict the cell death responsiveness of melanoma cell lines. By a total of 612 measurements, we determined the absolute expression (nM) of 17 core apoptosis regulators in a panel of 11 melanoma cell lines, and enriched these data with systems-level information on apoptosis pathway topology. By applying multivariate statistical analysis and multi-dimensional pattern recognition algorithms, the responsiveness of individual cell lines to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) or dacarbazine (DTIC) could be predicted with very high accuracy (91 and 82% correct predictions), and the most effective treatment option for individual cell lines could be pre-determined in silico. In contrast, cell death responsiveness was poorly predicted when not taking knowledge on protein-protein interactions into account (55 and 36% correct predictions). We also generated mathematical predictions on whether anti-apoptotic Bcl-2 family members or x-linked inhibitor of apoptosis protein (XIAP) can be targeted to enhance TRAIL responsiveness in individual cell lines. Subsequent experiments, making use of pharmacological Bcl-2/Bcl-xL inhibition or siRNA-based XIAP depletion, confirmed the accuracy of these predictions. We therefore demonstrate that cell death responsiveness to TRAIL or DTIC can be predicted reliably in a large number of melanoma cell lines when investigating expression patterns of apoptosis regulators in the context of their network-level interplay. The capacity to predict responsiveness at the cellular level may contribute to personalizing anti-cancer treatments in the future
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