Consumption Monitoring Research Articles

Bernard, an energy intelligent system for raising residential users awareness

Energy efficiency is still a hot topic today. Coming roughly the 25% of the energy consumption in EU from the residential sector, very few cheap and simple tools to promote energy efficiency in home users have been developed. The purpose of this paper is to present Bernard, a concept proof designed for filling this gap. This aims that householders become aware of their energy habits and have useful information that help them to redirect their consumption pattern. To achieve these goals, Bernard offers, through a mobile application, the home energy consumption monitoring in real time, the energy price forecast for the next hour and the appliances which are switched on, among others. Furthermore, it is important to highlight that the system has been designed with the premises of being cheap, non-intrusive, reliable and easily scalable, in order that utilities can gradually deploy and provide it to their customers, gaining at the same time valuable information for decision making and improving its corporate social image. Therefore, the adopted solution is based on a real time streaming data architecture suitable for handling huge volumes of data and applying predictive techniques on a cloud-computing environment. The paper provides a detailed description of the system and experimental results evaluating the performance of the predictive modules built. As case study, REFIT and REDD datasets were used.

Assessing water consumption and aquifer discharge through springs based on the joint use of rain and flow data in the Yarmouk River Basin

The method is proposed for assessing water consumption in the transboundary basin under the scarce available data on exploitation of water resources. The method is based on analysis of the dynamic rain–flow relationship which confirms the consistent flow decline during subsequent sub-periods as a result of the increased water consumption. The proposed method was applied to the Yarmouk River Basin up to the Maqaren Station. The study area (6058 km2) includes Syrian (more 80%) and Jordanian territories. The present study provided complete information on consumption from the Yarmouk River in Syria (1971–2009) and confirmed its significant impact on flow reaching Jordan (downstream area) and on aquifer discharge through springs. During 1997–2009 the total water consumption was estimated as 76% of the Yarmouk Natural Flow vs. 24% for the measured flow at Maqaren Station. Similar estimates for surface water and for groundwater are 82% and 67% vs. 18% and 23%, respectively. In 2006–2009, annual consumption (total, SW and GW) critically increased and reached to 95–100% of the Natural Flow. The perennial base flow of the Yarmouk River at Maqaren Station is caused by outflow of springs in the outlet section of the Hreer tributary, which represents the local aquifer discharge. Its mean annual value was assessed as 120 MCM (40% of the Yarmouk natural flow) but it decreased to almost complete dry-out in 2006–2009 under conditions of the decline in the water level of the basin aquifer and the depletion and drying of the springs as the result of the unrestrained increased pumping of groundwater. These results are useful for water balance estimation as well as for assessment and management of the water resources in Syria and Jordan. The method can be adapted to real-time monitoring of the water consumption from the Yarmouk River (Syria). The proposed method can be applied to other areas with problems of the transboundary water relations under scarce water resources and limited data on the water use.

Maternal Nutrition Practices in Uttar Pradesh, India: Role of Key Influential Demand and Supply Factors (P11-002-19)

ObjectivesDespite strong policy and program commitment, essential maternal nutrition services are not reaching enough women in many countries. This paper examines multi-factorial determinants (personal, family, community, and health services) associated with maternal nutrition practices in Uttar Pradesh, India. MethodsData were from a household survey of pregnant (n = 667) and recently delivered women (n = 1,835). Multivariable regression analyses were conducted to examine the determinants of four outcomes: consumption of diverse diets, iron folic acid (IFA) and calcium tablets, and weight monitoring during pregnancy. Population attributable risk analysis was used to estimate how much the outcomes can be improved under optimal program implementation. ResultsDuring pregnancy, women consumed ∼40 IFA and calcium tablets, 18% consumed diverse diet and 17% were weighed ≥ 3 times. Nutrition knowledge was a key modifiable factor associated with consumption of diverse diet (OR = 2.2 times, β = 0.7 food groups), IFA (2.3 times, 17 tablets), calcium (11.7 times, 18 tablets) and weight monitoring (0.7 times). Beliefs and self-efficacy were associated with IFA (OR = 2.0) and calcium consumption (OR = 4.6). Family support and adequate health services were also associated with better maternal nutrition practices. Under optimal program implementation, we estimate 51% women would have adequate diet diversity, an average consumption of 100 IFA and 90 calcium tablets, and women would be weighed 4.4 times during pregnancy ConclusionsWhile the current situation is bleak, many of the key factors associated with maternal dietary diversity, IFA/calcium consumption and weight gain monitoring are modifiable. Strengthening existing program operations to improve the supply of interventions and to create adequate demand through behavior change communication has the potential to result in large improvements in maternal nutrition practices from their baseline levels. However, these efforts will not be sufficient to meet WHO-recommended levels without also putting in place more favorable socio-economic conditions and additional resources. Funding SourcesBill & Melinda Gates Foundation, through Alive & Thrive, managed by FHI 360. Additional funding support is from CGIAR Research Program on Agriculture for Nutrition and Health (A4NH), led by the International Food Policy Research Institute.

IoT Based Smart Room For Power Consumption Monitoring And Control

IoT Based Smart Room For Power Consumption Monitoring And Control

Edge-nitrogenated biochar for efficient peroxydisulfate activation: An electron transfer mechanism

N-doped biochars (NBCs) were prepared by pyrolyzing corncob biomass and urea in different proportion which manifested superior catalytic performance of peroxydisulfate (PDS) activation for sulfadiazine (SDZ) degradation. Through both dynamic fitting and density functional theory (DFT) calculations, the critical role of edge nitrogenation in biochar (BC) structure was revealed for the first time. The incorporation of edge nitrogen configurations (pyridinic N and pyrrolic N rather than graphitic N) generated reactive sites for the PDS activation. Additionally, a thorough investigation was conducted to explicate the PDS activation mechanism by NBC through chemical quenching experiments, electron spin resonance (ESR) detection, oxidant consumption monitoring and electrochemical analysis. Different from the well-reported singlet oxygen (1O2) dominated nonradical mechanism, an electron transfer pathway involving surface-bound reactive complexes was proved to play a major role in the NBC/PDS system. Benefit from the electron transfer mechanism, the NBC/PDS system not only has wide pH adaptation for real application, but also shows high resistance to the inorganic anions in aquatic environment. We believe this study will deepen the understanding of the carbon-driven persulfate activation mechanism and provide strong technical support for the BC-mediated persulfate activation in practical applications.

The main changes in the consumption of alcohol by rural teenagers of school age in Russia for 15 years

The article deals with the main changes in alcohol consumption by rural teenagers-schoolchildren in Russia 2016-2017 compared 2001-2002 according to all-Russia monitoring of psychoactive substances consumption among 15-17-year-old teenagers.

Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities.

Antibiotic Prescription Patterns among Children Younger than 5 Years in Nouna District, Burkina Faso.

Understanding antibiotic use may help predict emergence of antimicrobial resistance. We evaluated antibiotic prescription trends in rural Burkina Faso, where little is known about antibiotic consumption. Antibiotic prescription data for 20 communities were extracted through record review in six primary health-care facilities serving the communities. The number of antibiotic prescriptions per child-year was calculated using population-based census data from the communities. A total of 1,444 antibiotic prescriptions were made from March to June 2017 among 3,401 children in the communities. The frequency of antibiotic prescription was 1.70 prescriptions per child-year (95% CI: 1.61-1.79). Penicillins were the most common (1.04 prescriptions per child-year, 95% CI: 1.01-1.06), followed by sulfonamides (0.69 prescriptions per child-year, 95% CI: 0.67-0.71) and macrolides (0.38 prescriptions per child-year, 95% CI: 0.37-0.40). Continued monitoring of antibiotic consumption in diverse settings will be important to understand the potential for emergence of antibiotic resistance.

Characterization of Respiratory Burst By 3D CELL Chip with a through-Hole Type Electrochemical Sensing Device

1. IntroductionRapid consumption of oxygen and generation of various reactive oxygen species (ROS), collectively referred as respiratory burst, occur in immune cells while they are in the processes of bacterial killing and phagocytosis as biological defence mechanisms. The production of ROS is in trace amount compared to the oxygen consumption of immune cells. Therefore, it is important to simultaneously measure oxygen consumption and reactive oxygen species and to measure the conversion efficiency of respiratory burst phenomenon. In order to capture this phenomenon, we have developed a through-hole 2ch device for 3-dimensional cultured cells.2．Experiment2.1 Fabrication methodA through-hole type electrochemical sensing device fabrication was carried out in the following step [1-8].1) Sputtering film formation of Cr (20 nm) and Au (500 nm) on Silicon or glass substrate.2) Spin coating of positive photoresist at 3000 rpm, pre-baking at 100°C for 10 min, exposure, development, post-baking at 120°C for 10 min to form the electrode shape. 3) Using photoresist as a mask, wet etching is performed in the order of Au and Cr to form an electrode and the photoresist is removed by immersing in an acetone solution.4) Spin coating the photoresist at 3000 rpm, prebake at 100°C for 2 minutes, exposure, post-exposure bake at 100°C for 2 minutes and developed to form the electrode shape.5) Sputtering film formation in the order of Cr (20 nm) and Pt (300 nm).6) Ultrasonic cleaning while dipping in acetone, forming the electrode lift-off.7) The pore portion of the device was formed by Deep RIE.8) Spin coating the negative type photoresist at 3000 rpm, prebake at 100 °C for 10 minutes, exposure and development, post-baking at 120°C for 10 minutes to form the insulating film shape. 2.2 Preparation of THP-1 cell chip of through-hole type electrochemical sensing deviceTHP-1 cells (7.3 × 105 cells / mL) on the fourth day after passage were suspended in a mixed solution of Cellmatrix Type IA, 5×RPMI-1640 culture solution and buffer for reconstitution, 1uL of which was inoculated into a through hole type device. Thereafter, the cells were incubated at 37 °C and 5% CO2 for 15 minutes to jellify the collagen, thereby forming a cell chip. 2.3 Real-time monitoring of respiratory burst of THP-1 cells with a through-hole electrochemical sensing deviceTHP-1 cells were measured in the sensor well at a constant temperature of 30 ± 0.5° C. Cells were suspended in PBS in presence of glucose at the concentration of 11.4 mM. PMA was added dropwise after 5 min of starting of measurement with a concentration of 200 nM and simultaneous reduction currents were measured. Oxygen reduction current was measured at −0.5 V vs. Ag/AgCl and oxidation current for H2O2 was measured at 0.7 V vs. Ag/AgCl at room temperature. 3. Results and DiscussionFigure 2 shows the result of the simultaneous measurement of the oxygen reduction current and reduction current for H2O2 during the respiratory burst in THP-1 cells with a chip type biosensor device. Oxygen reduction current value was found to decrease immediately after dropwise addition of 200nM PMA. It can be concluded that oxygen consumption and H2O2 production during respiratory burst are a phenomenon which are linked together. The continuous production of H2O2 during the respiratory burst and its longer half-life due to low reactivity can be correlated to the involvement of H2O2 in signalling. ReferenceA. Prasad, H. Kikuchi, K. Inoue, M. Suzuki, Y. Sugiura, T. Sugai, A. Tomonori, M. Tada, M. Kobayashi, T. Matsue, S. Kasai: Simultaneous Real-Time Monitoring of Oxygen Consumption and Hydrogen Peroxide Production in Cells Using Our Newly Developed Chip-Type Biosensor Device.Frontiers in Physiology 7, 109, 1-10 (2016) Figure 1

A Real-Time Wearable Sensor to Monitor Alcohol Consumption through Ethyl Glucuronide Detection

Here we demonstrate for the first time a wearable sensor that can differentiate Ethyl Glucuronide (EtG) concentrations in synthetic human sweat correlating to the predicted alcohol consumption profiles of moderate drinkers. Alcohol consumption monitoring devices typically report blood alcohol content (BAC) through correlation with breath alcohol content (BrAC). However, the rapid decay of expressed ethanol can limit the application space of these devices. Alcohol is metabolized and excreted from the body by different processes or pathways, ethyl Glucuronide (EtG) is one of them and is produced in the liver. EtG is readily expressed and detectable in transdermal human sweat making it a promising candidate for wearable alcohol consumption monitoring. A wearable biosensor device capable of reporting EtG levels in sweat both continuously via low power impedance spectroscopy is reported. The device was not only able to detect and respond to EtG present in the samples, but also able to differentiate each test scenario from one another. The three distinct EtG concentrations was designed as simulated drinking scenarios estimated to be 1, 2, and 3 standard U.S. drinks consumed over a duration of 60 minutes. It shows the ability for this device to act as an accurate reporter of EtG levels in low volume sweat (less than 5 microliter). The device will serve as a candidate tool for researchers to better understand and characterize the relationship between sweat EtG and consumed alcohol in real-time.

Achieving energy resilience through smart storage of solar electricity at dwelling and community level

This paper empirically evaluates the extent of energy resilience achieved in a socially-deprived community in Oxford, through deployment of solar photovoltaic (PV) systems and smart batteries (internet enabled and controllable) across a cluster of 82 dwellings (households). The methodological approach comprised dwelling and household surveys, along with high frequency monitoring of household electricity consumption, solar PV generation, battery charge and discharge data. In the monitored households, average daily electricity consumption was found to be positively related with dwelling size, number of occupants and number of appliances used. Although 117 MWh of PV electricity was generated within a year across 74 dwellings, peak generation did not match peak consumption, demonstrating the need for battery storage. Home batteries were found to increase self-consumption of PV electricity and offset grid demand through discharge of stored PV electricity marginally at an average of 6%, depending on the size of the PV system, surplus PV electricity available and size of the battery. Aggregating solar generation and storage at a community level showed that peak grid electricity demand between 17:00 and 19:00 was reduced by 8% through the use of smart batteries across 74 dwellings. In future, a local energy sharing scheme could be developed, wherein not all dwellings would need to have solar PV systems, but rather have internet enabled batteries that could be monitored and controlled virtually.

Disturbance modelling through steady-state value deviations: The determination of suitable energy indicators and parameters for energy consumption monitoring in a typical sugar mill

The growing emphasis in alternative revenue streams, from the conversion of sugarcane bagasse into energy co-products has prompted a desire by the sugar factories to achieve higher levels of energy efficiency. This study presents a comprehensive overview of the parameters whose steady-state deviation leads to excess energy use in sugar mills. The effect of eight operating parameters on the defined energy indicators was investigated, using a MATLAB simulation of a 250-tonne per hour sugar mill. The captured energy trends were used to develop energy prediction models based on the parameters whose steady-state offsets resulted in excess energy use. High prediction accuracies of over 95% were obtained when the developed energy prediction models were validated for seasonal cane quality and evaporator heat transfer coefficient variations. Increased A-massecuite recycling had a more pronounced effect on the energy used per kilogramme of water in the evaporator unit as compared to the crystallisation unit. Of the eight parameters, elevated imbibition water use and A-massecuite recycling had a cumulative percentage effect of 79% on the overall steam used per tonne of cane and sugar produced. Hence increasing the syrup concentration, decreasing imbibition water use and massecuite recycling are preferred operational strategies for improved energy-efficiency in a sugar mill. Overall, this study strengthens the existing literature by illustrating an approach for developing suitable energy indicators for excess energy use monitoring in sugarcane mills.

A study of the possibility of using 3D modelling and 3D printing for electrical capacitance tomography sensor

Nowadays, the optimization of energy consumption and resources is one of the most urgent topics in worldwide industry. The energy consumption monitoring and control in various multiphase flow industrial applications, where a proper flow characteristic and an optimal phase mixture control is crucial, is hard to perform due to the physical and chemical complexity of the processes. The Electrical Capacitance Tomography (ECT) is one of the relatively cheap non-invasive measurement methods that can help in the monitoring and control of optimal energy and resources dozing in industrial processes. ECT diagnostics systems use unique sensors that can non-intrusively detect spatial capacitance changes caused by spatial changes in the electrical permittivity of industrial process components. One of the latest ECT extensions is a three-dimensional measurement strategy that uses a multilayer structure of the capacitance sensor. In this paper, the authors propose a novel approach to the 3D ECT sensors fabrication process that uses 3D computer modelling and 3D printing to easily get any sensor shape, electrode layout, scale and shielding strategy. This study compares the measurement abilities of a 3D ECT sensor fabricated using a traditional hand-made technique with the 3D printed device. The results have proven the potential of the new 3D print-based sensor regarding its significant fabrication time reduction as well as the improvement of the overall 3D ECT sensor measurement accuracy and stability.

The effects of salience of the sound of food on consumption

In order to promote their products, food marketers use labels (e.g., crunchy) to make salient the sound created by the food when it is eaten. Will merely making the sound of the food salient (without changing its actual sound) affect consumption? The extant literature is silent on this question. Addressing this gap, we propose that the mere salience of the food's sound can impact consumption. In three studies, we find that the salience of the food's sound increases consumption because it enhances consumers' auditory experience of the sound of food and increases taste evaluations. However, the salience of the food's sound decreases consumption when experience of the food's sound is impaired or when consumption monitoring is high. The paper's findings advance research on auditory perceptions of food, food labeling, and food consumption, and offer guidance on how to enhance enjoyment and prevent overeating of sound-salient food.

Anomaly detection based on random matrix theory for industrial power systems

Abstract The identification of abnormal power consumption state is an important but difficult issue in power consumption. The State Grid Corporation of China’s electric energy data acquisition system is only capable of acquiring power consumption big data collected by smart energy meter terminals. In view of this fact, this study presents a method for identifying abnormal power consumption state. First, the spectral distribution of eigenvalues of the covariance matrix of the high-dimensional random matrix of massive volumes of power consumption data is analyzed based on high-dimensional random matrix theory. Then, a power consumption big data-based abnormal power consumption state identification method is proposed based on the statistical properties of random matrices. Finally, simulations are performed based on actual power consumption data from Guizhou Province, China. The simulation results show that the proposed method can not only satisfy urgent requirements of power grids for visualization, timeliness, reliability and security but also provide a new approach for data-driven smart visual monitoring of power consumption.

A Novel Solution to Prevent Diesel Pilferage at Telecom Tower Site

Monitoring of energy consumption is a critical application at a telecom tower site. This paper presents a novel power monitoring application. In addition to power consumption measurement, power monitoring could be useful in preventing the pilferage of diesel used to run generators at a tower site. The problem of diesel pilferage has been analyzed, and a concealed power monitoring solution has been presented. The monitoring algorithm can be integrated with the pulse width modulation controller used in switch-mode power supply rectifier. The measurement philosophy has been validated, designed, and experimentally tested. Simulink models of power supply at a tower site have been used for validation. A prototype for the above-mentioned system has been designed using MATLAB and a microcontroller. The combination of these two tools emulates a power quality analyzer. Power quality analysis is the central theme of this solution. The difference in power quality and the impact of load on the power source have been used to construct features for this solution. The proposed solution has been experimentally tested by recording input power waveforms of an ac-to-dc converter.

Energy Analysis in an Italian Opera House: Highlighting the Difficulties in Refurbishing Historic Buildings Using Energy-Savings Strategies

The refurbishment of historic buildings is a complex task in which the goal of obtaining a more energy-efficient building can conflict with the peculiar characteristics of the building's environment and its intended use. In this article, the authors address this problem for a very specific type of building: a historic opera house located in northern Italy. The results of energy consumption monitoring and spot measurements on selected loads were used as a basis to propose energy-savings strategies.

Eco-management in condition of metallurgical production digitization

Efficient use of natural resources, improvement of living standard and quality are increasingly attracting public attention; as a result, data characterizing company financial and non-financial indicators are in strong demand nowadays. Integrated reporting (complex of financial and non-financial indicators) is becoming one more competitive advantage of companies which are ready to submit transparent and reliable data displaying impact exerted by company activities on economy, environment and social medium to interested parties. Authors, aiming to investigate the information content of environmental constituent reflection in company activities under economy digitization condition, performed comparative analysis of ecological indicators recommended for disclosure in non-financial reporting of Global Reporting Initiative and Russian Union of Industrialists and Entrepreneurs (RUIE). Authors, for fuller reflection of results of company activities in the sphere of greenhouse gases emissions and energy consumption monitoring, identified additional indicators enabling eco-management efficiency assessment. Using the proposed indicators it is possible to form an integrated digital block of indicators enabling the researcher to adequately evaluate the level of enterprise eco-safety.

Modeling Drying-Energy Consumption in Automotive Painting Line Based on ANN and MLR for Real-Time Prediction

By taking energy awareness and efficiency into consideration, this research applies multiple linear regression (MLR) models and artificial neural networks (ANNs) for predicting the real-time heating energy demand and natural gas consumption in the drying chamber of an automotive painting line. With the estimation of the correlation and randomness during the painting process, a total of six fundamental variables that integrate energy, product and process data are considered in order to cover the impact factors of the natural gas consumption. The independent variable value, used to calibrate and evaluate the model, is obtained from our constructed energy consumption monitoring system. In the prediction cases, the ANN-based model, which offers a well performance, provides great precision in the determination of the natural gas demand with an R2 coefficient and other error measurements of over 90%. Based on the MLR model, the process temperature and the corresponding variation are found to be two decisive factors for the natural gas consumption. It is foreseen that the ANN model, which can effectively perform input/output mapping, is reliable and powerful for predicting the natural gas consumption and estimating the energy demand. Being a general data-driven method, our method is convenient for application to other heating process lines and even other energy-consuming plants.

Green Smart Technology for Water (GST4Water): Water Loss Identification at User Level by Using Smart Metering Systems

A real-time household water consumption monitoring and processing system aimed at leakage identification at user level is presented here. The system, developed within the GST4Water project, allows consumption data sent by a generic smart meter installed in a user’s house to be received and transferred to a cloud platform. Here, the consumption data are stored and processed through an empirical algorithm able to automatically identify leakage at the individual user level by looking for non-consumption in certain periods of the day. With reference to a real-life case study, the results obtained show that the algorithm enables leakages on users’ properties to be identified with an accuracy of more than 90%. Therefore, the implementation of this algorithm within a highly innovative smart metering system can represent an efficient tool for reducing water losses at user level.