Load Monitoring Data Research Articles

An L1-and-L2-regularized nonnegative tensor factorization for power load monitoring data imputation

An L1-and-L2-regularized nonnegative tensor factorization for power load monitoring data imputation

Position-specific workload of professional rugby union players during tactical periodization training.

The positional workload characteristics in rugby union on three acquisition days (i.e. strength, endurance, and speed days) of tactical periodization are still relatively unknown. Therefore, the primary aim of this study was to shed light on the positional external workload variables (10 Hz Global Positioning System and accelerometer microtechnology) and internal workload indicators (the session rating of perceived exertion) of players in a professional rugby union team by utilizing and comparing two tactical periodization models. Twenty-six male players (15 forwards and 11 backs) were recruited from a French second-division rugby club. Data were obtained over 10 weeks of in-season home games: a total of 780 observations were analyzed. Student's t-test observed different external workload profiles between positions among acquisition days. Mean external workload values, except PlayerLoadslow, were significantly higher (p≤0.01; effect size: 0.41-1.93) for backs than forwards for all acquisition days. Moreover, forwards perceived a higher internal workload than backs on the strength day of both models. The findings demonstrate that applying these two tactical periodization models could result in effective rugby union training. Validating external and internal workload characteristics on tactical periodization acquisition days enables extensive analysis of training load monitoring data; these data can be utilized to discover the unique characteristics of each position and design position-specific acquisition days to improve performance.

Evaluation of the effects of reservoir construction on the relationship between runoff and sediment load in the upper reaches of the Yellow River.

In order to evaluate the impact of the Longyangxia Reservoir construction on the relationship between runoff and sediment load (RRSL) in the upper reaches of the Yellow River, the runoff and sediment load monitoring data are used to identify variation point by sliding correlation coefficient method. Then with Copula function, the various countering situations of runoff and sediment load before and after variation are proposed to innovatively reveal their changing relations. The results demonstrate that (1) the reservoir construction exerts a great impact on the RRSL in the upper reaches of the Yellow River with the occurring variation point in 1987. The correlation of runoff and sediment load is presented better before variation but tends to worse decrease after variation. (2) Either before or after variation, runoff follows the generalized extreme value distribution, while sediment load before variation obeys the normal distribution, but the lognormal distribution after variation. Meanwhile, Frank Copula function accurately simulates the RRSL before variation, whereas Clayton Copula function is selected after variation. (3) The probability of synchronous rich and poor runoff and sediment load is higher before variation. After variation, the RRSL decreases significantly; their synchronous probability decreases by 45.67%. Meanwhile, the asynchronous probability of their extreme events evidently increases. The joint recurrence and co-recurrence intervals before variation are smaller than those after variation, along with the decreasing of their volume peak. This study provides new knowledge of runoff and sediment load influenced by reservoir construction, and also offers guidance for flood control and sediment load-discharge schemes of reservoir.

Human Immunodeficiency Virus Viral Load Monitoring and Rate of Virologic Suppression Among Patients Receiving Antiretroviral Therapy in Democratic Republic of the Congo, 2013-2020.

Antiretroviral therapy (ART) expansion and viral load as a treatment monitoring approach have increased the demand for viral load testing. Many hurdles affect the coverage, quality, and use of viral load results. Estimates of viral load monitoring and viral suppression rates are needed to assess the performance of ART programs and improve human immunodeficiency virus (HIV) management outcomes. People with HIV (PWH) viral load monitoring data were routinely collected in 84 health facilities in Kinshasa, Democratic Republic of the Congo (DRC), between 2013 and 2020. The number of PWH under ART, the number of participants with at least 1 viral load test result, the rate of viral suppression (defined as ≤1000 HIV ribonucleic acid copies per mL), and the mean turnaround time from sample collection to release of viral load test results were collected together with clinical data. A total of 14 057 PWH were included in the analysis. People with HIV were mainly enrolled after the "test and treat" implementation. The patients were followed for a median period of 27 months. The proportion of PWH with at least 1 available viral load largely increased in recent years. The delay from sample collection to release of viral load test results decreased overtime, from 35 days in 2018 to 16 days in 2020. Pregnancy and advanced HIV disease were associated with a lower chance of viral suppression. There has been considerable success in increasing viral load access for all PWH under therapy in DRC. Nevertheless, viral load testing should be intensified with a particular effort to be made in groups at higher risk of viral failure.

Correlation model of deflection, vehicle load, and temperature for in‐service bridge using deep learning and structural health monitoring

Deflection is an important issue in bridge structural health monitoring. An accurate deflection–vehicle load–temperature correlation model is critical to abnormal data identification, deflection prediction under extreme conditions, and bridge structural assessment. However, because of the discrete distribution in time domain of vehicle load and the extreme complexity of the deflection–vehicle load–temperature correlation, the correlation modeling method needs further studies. A novel deflection–vehicle load–temperature correlation modeling method is developed in this study. Based on the concept of deflection influence line (DIL), the raw vehicle load monitoring data are transformed into time-continuous vehicle influence coefficient (VIC). By using gated recurrent unit (GRU) neural network, a correlation model with inputs of VIC and environmental temperature data and output of deflection data is established. Taking a suspension bridge in China as an example, the prediction accuracy of short-, medium-, and long-term correlation models is tested. Moreover, based on the correlation model, a decomposition method of temperature- and vehicle-induced deflection components is proposed. The results show that the predicted deflection of the short-term correlation model is basically consistent with the real-time monitoring data, while the medium- and long-term correlation models have accurate prediction ability for the deflection extreme values in a certain time window. The temperature- and vehicle-induced deflection components separated by using the correlation model are in good agreement with the wavelet decomposition (WD) results, with clear physical meaning and independent of empirical judgment.

Temporal convolution network‐based time frequency domain integrated model of multiple arch dam deformation and quantification of the load impact

Deformation is an intuitive reflection of the safety status of a dam. The construction of a dam deformation prediction model can predict the deformation and interpret the effects of environmental loads. The current research mainly focuses on the predictive ability of the model and rarely involves the interpretation of the load impact on deformation. Meanwhile, the selection of the model factors, such as water pressure factors and temperature factors, mostly relies on prior knowledge. In addition, the complex structure of multiple arch dams makes it difficult to capture the relationship between deformation and environmental loads. Consequently, the performance of conventional models based only on time domain information may be insufficient. In this paper, a deformation prediction model is established by integrating time frequency domain information. First, the deformation and load monitoring data are decomposed and regrouped according to the frequency characteristic of signals via kurtosis index-based VMD. Second, the sequence relationship between the dam deformation and loads under different frequency characteristics is automatically captured based on the temporal convolution network (TCN). Finally, a quantitative method of the load impact is proposed based on the network parameters. The case results show that the proposed modeling paradigm has significantly improved the prediction accuracy. The quantification result of the load impact on the horizontal displacement change of the dam conforms to the actual state of the project during the analysis period. The work effectively supplements the research on the prediction of ML-based models and interpretation of the load impact on deformation.

Value of additional traffic data in the context of bridge service-life management

The assessment of existing road bridges as parts of infrastructure networks is required in consideration of their deterioration and age. Advanced monitoring and management tools are mainly used for landmark bridges while the decision making process for small to medium bridges, which constitute the majority of the bridge network, mainly relies on condition assessment based on experience and conservative analysis related to design codes. In the analysis of the load-carrying capacity of theses bridges, loads imposed by the passing traffic are predominant due to their variable nature and level of uncertainties. The research presented in this paper outlines the value of additional traffic data, collected with both traffic counters and Weigh-in-Motion (WIM) method in the scope of assessment procedures for these bridges. Adequate processing of collected traffic data is crucial for subsequent extrapolation of maximum load effects on a particular bridge over a certain period in time. By taking into account all related costs, the purpose of this paper is to prove the benefits of employing traffic load monitoring data in structural assessment and subsequent decision-making process in service life management of bridges.

A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data

Various countries in the world are vigorously developing energy-saving industries and attaching importance to the improvement of household energy efficiency, but it is difficult to evaluate user power consumption characteristics due to insufficient information and large data granularity. It is, however, possible to evaluate the energy efficiency of household users via non-intrusive load monitoring (NILM). This paper explores the energy efficiency assessment of residential users and proposes a household energy efficiency assessment method based on NILM data. An energy efficiency assessment index of residents is provided by analyzing factors that affect residents’ energy efficiency. This index is clear, operable, and easy to obtain and quantify. Based on NILM information, clustering, and comprehensive evaluation, as well as combining the entropy weight method with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), a user’s energy efficiency can be evaluated and analyzed. Some case studies are provided to verify the validity of the proposed method based on non-intrusive information, to analyze the characteristics and deficiencies of the user’s energy consumption, and to give corresponding energy recommendations.

Recognition and classification of typical load profiles in buildings with non-intrusive learning approach

The recent increasing spread of Advanced Metering Infrastructure (AMI) has enabled the collection of a huge amount of building related-data which can be exploited by both energy suppliers and users to gain insight on energy consumption patterns. In this context, data analytics-based methodologies can play a key role for performing advanced characterization, benchmarking and classification of buildings according to their typical energy use in the time domain. Traditionally, energy customers are classified according to their building end-use category. However, buildings belonging to the same category can exhibit very different energy patterns making ineffective this kind of a-priori categorization. For this reason, load profiling frameworks have been developed in the last decade to identify homogenous groups of buildings with similar daily energy profiles. The present study proposes a non-intrusive customer classification process, which does not use as predictive attributes in-field load monitoring data for the classification of unknown customers, but rather monthly energy bills and additional information on customers’ habits collected by means of a phone survey. The proposed classification process is developed by analysing hourly energy consumption data of 114 electrical customers of an Italian Energy Provider. The representative daily load profiles are grouped using the “Follow the Leader” clustering algorithm and a globally optimal decision tree is employed to build a supervised classification model. The model, compared to a baseline recursive partitioning tree, leads to an increase of accuracy of about 6%. Eventually, the procedure exploits energy bill data also for estimating the magnitude of typical load profiles.

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.

Predictive Modelling of Training Loads and Injury in Australian Football

Abstract To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and relative training load metrics were calculated for each player each day. Injury prediction models (regularised logistic regression, generalised estimating equations, random forests and support vector machines) were built for non-contact, non-contact time-loss and hamstring specific injuries using the first two seasons of data. Injury predictions were then generated for the third season and evaluated using the area under the receiver operator characteristic (AUC). Predictive performance was only marginally better than chance for models of non-contact and non-contact time-loss injuries (AUC<0.65). The best performing model was a multivariate logistic regression for hamstring injuries (best AUC=0.76). Injury prediction models built using training load data from a single club showed poor ability to predict injuries when tested on previously unseen data, suggesting limited application as a daily decision tool for practitioners. Focusing the modelling approach on specific injury types and increasing the amount of training observations may improve predictive models for injury prevention

Resident Load Influence Analysis Method for Price Based on Non-intrusive Load Monitoring and Decomposition Data

In the non-invasive load monitoring mode, the load decomposition can reflect the running state of each load, which will help the user reduce unnecessary energy costs. With the demand side management measures of time of using price, a resident load influence analysis method for time of using price (TOU) based on non-intrusive load monitoring data are proposed in the paper. Relying on the current signal of the resident load classification, the user equipment type, and different time series of self-elasticity and cross-elasticity of the situation could be obtained. Through the actual household load data test with the impact of TOU, part of the equipment will be transferred to the working hours, and users in the peak price of electricity has been reduced, and in the electricity at the time of the increase Electrical equipment, with a certain regularity.

센서스 정보 및 전력 부하를 활용한 전력 수요 예측

국내 전력 수요량 예측을 위한 정확한 분석 모델을 개발하기 위하여 고차원 데이터 군집 분석에 적합한 차원 축소 개념의 부분공간 군집 기법과 SMO 분류 기법을 결합한 전력 수요 패턴 예측 방법을 제안하였다. 전력 수요 패턴 예측은 무선부하감시 데이터 뿐 아니라 소지역 단위의 센서스 정보를 통합하여 시간대별 전력 부하 패턴 분석과 인구통계학 및 지리학적 특성 분석이 가능하다. 서울지역 대상의 센서스 정보 및 전력 부하를 이용한 소지역 전력 수요 패턴 예측 결과 총 18개의 특성 군집을 구성하였으며, 전력 수요 패턴 예측 정확도는 약 85%를 보였다. In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.

Development of overload evaluation system for distribution transformers using load monitoring data

In this paper, we present an improved overload decision system for oil-immersed distribution transformers under 100kVA using load monitoring data. Our study can be categorized into two parts: (a) improvement in the criteria for judging the overload conditions of distribution transformers and (b) development of an overload evaluation system using load monitoring data. In order to determine the overload criteria, overload experiments are performed on sample transformers; the results of these experiments are used to define the relationship between the transformer overload and the increase in the top-oil temperature. To verify the accuracy of the experimental results, field tests are performed using specially manufactured transformers, the loads and top-oil temperatures of which can be measured. For arriving at online overload decisions, we propose methods whereby the measured load curve can be converted into an overload characteristic curve and the overload time can be calculated for any load condition. The developed system is able to evaluate the overload for individual distribution transformers using load monitoring data.

Integration of structural health monitoring and fatigue damage prognosis

This paper presents a Bayesian probabilistic methodology to integrate model-based fatigue damage prognosis (FDP) with online and offline structural health monitoring (SHM) data. The prognosis uses fracture mechanics-based fatigue crack growth modeling, along with quantification of various sources of uncertainty, including natural variability, data uncertainty and model errors. These uncertainty sources are connected using a Bayesian network and a probabilistic sensitivity analysis is performed to assess the uncertainty contributions from these sources. The cycle-by-cycle simulation of fatigue crack growth is expedited via the use of a surrogate modeling technique (Gaussian process model) to replace computationally expensive finite element analysis. Real-time monitoring data of external variable amplitude loading history is used to construct a Bayesian autoregressive integrated moving average (ARIMA) model to predict and update the loading. On-ground crack inspection data is used to quantify the uncertainty in the initial and current size of an existing crack, using the Bayesian approach. Three possible cases of inspection results are considered: (1) crack is not detected; (2) crack is detected but not measured; (3) crack is detected and measured. Different scenarios of data availability (load monitoring data and inspection data) are considered for the prognosis of an individual component in a fleet. A numerical example, surface cracking in a rotorcraft mast under service loading, is implemented to illustrate the proposed methodology. The results of prognosis are validated using Bayesian hypothesis testing.