Consumption Monitoring Research Articles

Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

Non-invasive tests for alcohol-associated liver disease.

Alcohol consumption is a global phenomenon and a major contributor to alcohol-associated liver disease (ALD). Detecting individuals at risk of ALD has been challenging, with only a small fraction of patients being identified at early stages compared to other chronic liver diseases. In response to this challenge, non-invasive tests (NITs) have become essential tools for the detection of ALD, offering opportunities for early identification and intervention to mitigate the disease burden. Non-invasive alcohol consumption biomarkers are crucial in estimating individuals' recent alcohol intake, providing valuable insights into their drinking patterns. Various NITs have been investigated for the initial screening of asymptomatic individuals at risk of ALD, as well as for identifying specific stages of the disease. These NITs are applied in two main clinical scenarios: population-based stratification for identifying and predicting liver-related symptoms, and diagnosing and prognosticating compensated cirrhosis or advanced chronic liver disease in secondary or tertiary care settings. Moreover, NITs play a significant role in the prognostic assessment of patients with various manifestations of ALD, including alcohol-associated hepatitis, decompensated cirrhosis, and metabolic and alcohol-associated liver disease. These tests guide appropriate treatment decisions and predict outcomes. In this review, various NITs for the early detection and monitoring of alcohol consumption were discussed. Additionally, the evaluation of NITs for screening and predicting ALD and liver complications was addressed comprehensively. Future perspectives of NITs for ALD were explored, alongside a thorough discussion of the opportunities and challenges associated with NITs for ALD screening.

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

An Integrated Smart Metering System Using LORA Technology

Precise meter readings are essential for effectively regulating residential energy systems in India's smart city context. However, due to the high number of users and the absence of daily consumption monitoring, standard meter reading methods are expensive and time-consuming, which causes consumer unhappiness. The suggested approach to address this issue is to put in place an integrated wireless smart energy system that makes use of smart metering technologies. The way utilities manage energy could be completely changed by this method. The integrated system is intended to take the place of the traditional digital energy meters, which are primarily used to record meter readings properly for payment purposes. This allows for automatic meter readings without the need for frequent travels to the location where the meters are located. Low Power Wide Area Networks (LPWAN) technology, which is renowned for its low power consumption, cost-effectiveness, long-range coverage, and efficient penetration, are used in this technology to enable the transfer of accurate and secure data on energy usage in real-time. The experiments that resulted from the trials allowed the energy to successfully communicate and documented reliable readings. By providing users with real-time usage data and guarding against overloaded scenarios, the system also benefits consumers. Additionally, the device reports electricity theft to the utility company. The shortcomings of the conventional systems and design difficulties are addressed by the suggested system.

Techno-economic analysis, optimization, and dispatch strategy development for renewable energy systems equipped with Internet of Things technology

This study examines the benefits of incorporating Internet of Things (IoT) technology in hybrid renewable energy systems. This technology can improve the monitoring of energy storage and consumption as well as remote management capabilities. Employing an IoT controller helps cut down on electricity usage during the electricity grid peak times, ultimately lowering the total cost of distributed energy systems. To determine the optimal size of the residential energy system, a Grey Wolf Optimizer algorithm is conducted and the optimal solution for a total consumption of 225.36 kWh/d is 17 kW of Photovoltaic panels, 49 kWh of battery, and 47 kW of inverter with an overall cost of 90,134 $. The developed predicting dispatch strategy can reduce consumption during the peak of the electricity grid by 91% and prevent blackouts for the investigated area. Furthermore, two different sensitivity analyses are conducted to investigate the effects of changing sell-to-grid price and electrical demand profiles on the optimum solution. The sensitivity analyses for the sale price to the grid from 0.05 $ to 0.15 $ are fully investigated. Also, peak shaving above 90% is repeated for different electrical loads and the optimality of the presented method is confirmed in different load profiles with 225.1 kWh/day, 212.2 kWh/day, and 225.5 kWh/day consumption.

An Economical Internet of Things (IoT) Driven Approach for Non-Intrusive Smart Energy Monitoring

This study focuses on the use of the Internet of Things (IoT) for non-intrusive monitoring of electricity consumption. Using Internet of Things technology, this study attempts to develop and evaluate an affordable and easy-to-navigate energy monitoring system. Literature review, system design, hardware development, software configuration, system integration, and system testing are all included in the methodology. The results demonstrate the successful use of an IoT device to track electrical energy consumption. The system demonstrates promising levels of accuracy in tracking power consumption patterns, despite ongoing testing and calibration challenges. This study provides an affordable and secure IoT-based smart energy monitoring solution.

Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable Data-Driven Models

Efficient energy optimization and scheduling in industrial factories depend on accurate, reasonable, and real-time monitoring of equipment power consumption. However, the power prediction of industrial equipment requires a large number of process data, which could be inevitably contaminated by some imperfect data, due to the harsh environments. Monitoring or predicting equipment power consumption is usually not feasible using data-driven black-box models with imperfect data. This paper proposes a prediction-based approach for power consumption monitoring using an interpretable data-driven model. First, a data preprocessing method is used to remove outliers and fill in missing values. Then, a Volterra polynomial basis function (VPBF) model is built to predict equipment power consumption. This model decomposes power values into a series of basis functions consisting of input parameters. Moreover, to compensate for data dropouts during the power consumption monitoring process, a networked predictive monitoring system is also proposed. Finally, this paper presents two case studies based on actual production equipment in an industrial manufacturing factory. The results demonstrate that the proposed approach can achieve satisfactory monitoring accuracy and adaptation ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problem of power consumption monitoring of industrial equipment in harsh production environments. A conventional solution is to predict the power consumption using data-driven black-box models, with limited feasibility and interpretability. This paper proposes a new power consumption monitoring approach, utilizing an interpretable data-driven model and a networked predictive method. This approach accurately reveals the transparent relationships between the output and input parameters. The power prediction result is consequently interpretable and more reasonable. Meanwhile, this approach actively compensates for data dropouts in the network, which can help operators real-time grasp the power consumption of equipment. Furthermore, this approach can be integrated into energy optimization systems as a basis of optimization and decision-making. Practical applications in an aluminum manufacturing company located in Guangdong Province demonstrate that this approach is feasible and applicable. Future research is to expand this approach further for energy optimization and scheduling.

EPri-MDAS: An efficient privacy-preserving multiple data aggregation scheme without trusted authority for fog-based smart grid

With the increasingly pervasive deployment of fog servers, fog computing extends data processing and analysis to network edges. At the same time, as the next-generation power grid, the smart grid should meet the requirements of security, efficiency, and real-time monitoring of user energy consumption. By utilizing the low-latency and distributed properties of fog computing, it can improve communication efficiency and user service satisfaction in smart grids. For the sake of providing adequate functionality for the power grid, various schemes have been proposed. Whereas, many methods are vulnerable to privacy leakage since the existence of trusted authority may increase the exposure to threats. In this paper, we propose the EPri-MDAS: an Efficient Privacy-preserving Multiple Data Aggregation Scheme without trusted authority based on the ElGamal homomorphic cryptosystem, which achieves both data integrity verification and data source authentication with the most efficient block cipher-based authenticated encryption algorithm. It performs well in energy efficiency with strong security. Especially, the proposed multidimensional aggregation statistics scheme can perform the fine-grained data analyses; it also allows for fault tolerance while protecting personal privacy. The security analysis and simulation experiments show that EPri-MDAS can satisfy the security requirements and work efficiently in the smart grid.

DESIGN OF SECLUDED ENERGY METER FOR TAMPERING AND UNAUTHORIZED ACCESS DETECTION IN SMART GRID

This paper deals with an energy metering system for electricity monitoring for each and every household that is different from the conventional energy meter. This proposed design is meant to be a better alternative for an efficient and more secure line for energy usage by the consumer without any illegal access to their connections so that they don’t have to pay for something they aren’t accountable for in the first place and protect the supplier/provider, which is the government, from incurring any losses from the theft as well as from customers who tamper with the meter values. These things are vital in today’s world. So,we are taking this into account and designing a smart meter with the IoT technology for monitoring data values at the tip of our fingers, readily available at any moment. It serves many purposes, from the monitoring of energy consumption to easily using mobile phones to work on managing the energy usage according to their budget. It is also easy for the supplier to monitor without manually inspecting the location. The WiFi module ESP8266 is used for the purpose of transmitting the energy data to the server. This paper also deals with a smart grid system in which energy meters are used as a two-way communication system as opposed to conventional meters with one-way communication, which will be a more efficient system. This IoT is good in terms of its development towards an easy and advanced metering infrastructure, which also needs secure communication between the supplier and consumer to protect that data.

Green and energy-saving ecological renovation design of buildings based on energy consumption monitoring data

Green and energy-saving ecological renovation design of buildings based on energy consumption monitoring data

Next-Generation Billing Systems: The Smart eBill Revolution

Efficient monitoring of electrical consumption is essential for promoting energy conservation and optimizing electricity usage. This project introduces a "Smart EBill System" using current and voltage sensors, along with an LCD display for real-time energy consumption tracking. The system aims to provide users with instant information on their electricity usage, helping them make informed decisions and promoting energy efficiency. This integration of sensors and a display offers an intelligent solution for smart and user-friendly electricity bill monitoring. In today's digital age, electronic billing systems have become a cornerstone of modern business operations, offering convenience, efficiency, and sustainability compared to traditional paper-based billing methods. However, as electronic billing systems evolve, there is a growing need for smarter, more intuitive solutions that enhance user experience, streamline processes, and leverage emerging technologies. This paper presents Smart eBill, a next-generation electronic billing system designed to address these evolving needs. Smart eBill integrates advanced features such as artificial intelligence, machine learning, and data analytics to revolutionize the billing experience for businesses and consumers alike. The system employs AI algorithms to automate billing processes, including invoice generation, delivery, and payment reconciliation, reducing manual errors and administrative overhead. Furthermore, Smart eBill leverages machine learning techniques to analyze billing data and provide personalized insights and recommendations to users. By understanding user preferences, spending patterns, and historical data, the system offers tailored suggestions for optimizing billing arrangements, reducing costs, and improving financial management.

Association between Iron-Folic Acid Supplementation during Pregnancy and Maternal and Infant Anemia in West Java, Indonesia: A Mixed-Method Prospective Cohort Study.

The Indonesian government has provided iron-folic acid (IFA) supplementation in response to maternal pregnancy iron-deficiency anemia. However, community-based cohorts on IFA's effects on maternal and infant anemia are limited. A mixed-method study design with a primary longitudinal cohort was used to observe the association between IFA and anemia in mothers and infants. Iron-folic acid supplementation was observed throughout pregnancy. Anemia status was based on a single hemoglobin assessment using HemoCue Hb 201 + in the second or third trimester of pregnancy for the mother and at birth for the infant. Qualitative data were collected via in-depth interviews (IDIs) and a forum group discussion (FGD). Iron-folic acid supplementation with >180 tablets throughout pregnancy was associated with lower pregnancy anemia (adjusted relative risk [aRR] = 0.25, 95% CI: 0.092-0.664, P = 0.006) after adjusting for potential confounding variables. Supplementation with IFA was not associated with infant anemia (RR = 1.033, 95% CI: 0.70-1.54, P = 0.873 for 90-180 tablets and RR = 1.07, 95% CI 0.70-1.63, P = 0.774 for >180 tablets). The IDIs and FGD suggested that IFA and multivitamin content knowledge, IFA consumption monitoring, and paternal involvement were important in IFA supplementation and effectiveness in reducing anemia. Iron-folic acid supplementation was associated with reduced maternal but not infant anemia. Because maternal anemia is associated with infant anemia, an anemia monitoring program for women in early pregnancy is vital in addressing infant health. Paternal involvement was also identified as a major factor in maternal and child health.

A Mixed-Methods Approach for Evaluating the Influence of Residential Practices for Thermal Comfort on Electricity Consumption in Auroville, India

With rapid global economic growth and a rise in disposable household income, particularly within a progressively warming planet, the escalating demand for energy to achieve thermal comfort has become a salient concern in the Global South, notably in emerging economies like India. This burgeoning need for cooling solutions has not only underscored the vital role of energy consumption but has also accentuated the imperative of comprehending the ensuing implications for electricity policy and strategic planning, particularly within the ambit of the Global South. This study explored the nuanced landscape of active cooling within an intentional community, Auroville, in southern India, aiming to discern the factors underpinning household preferences and practices in the pursuit of thermal comfort. Employing a mixed-methods approach, this study contributed empirically and methodologically to the interdisciplinary discourse by analysing residential electricity consumption patterns and cooling practices within selected households in the specified community. The study unfolded in three methodological stages: firstly, an analysis of climatic data coupled with an environmental stress index (ESI) assessment; secondly, the monitoring of end-user electricity consumption followed by rigorous data analysis; and lastly, the utilisation of qualitative in-depth interviews and observational techniques. This study’s outcome yielded empirical insights into the unprecedented shifts in the ESI for Auroville since 2014. Furthermore, the study unravelled the intricate complexities inherent in occupant behaviour within residential structures, thereby offering valuable insights into the practices that shape householders’ cooling preferences. This research enriched the understanding of the dynamics of energy consumption in the pursuit of thermal comfort and contributes to the broader discourse on sustainable development and energy policy in the context of climate change.

Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network

With increasing consumption of primary energy and deterioration of the global environment, clean energy sources with large reserves, such as natural gas, have gradually gained a higher proportion of the global energy consumption structure. Monitoring and predicting consumption data play a crucial role in reducing energy waste and improving energy supply efficiency. However, owing to factors such as high monitoring device costs, safety risks associated with device installation, and low efficiency of manual meter reading, monitoring natural gas consumption data at the household level is challenging. Moreover, there is a lack of methods for predicting natural gas consumption at the household level in residential areas, which hinders the provision of accurate services to households and gas companies. Therefore, this study proposes a gas consumption monitoring method based on the K-nearest neighbours (KNN) algorithm. Using households in a residential area in Xi’an as research subjects, the feasibility of this monitoring method was validated, achieving a model recognition accuracy of 100%, indicating the applicability of the KNN algorithm for monitoring natural gas consumption data. In addition, this study proposes a framework for a natural gas consumption prediction system based on a backpropagation (BP) neural network.

Toward cyber-physical systems for monitoring and analyzing energy consumption of machine tools

ABSTRACT This paper presents a digital twin and cyber-physical systems (CPS) environment for monitoring and analyzing the energy consumption of machine tools. Machine tools, which account for a large portion of the manufacturing industry, should be developed in response to environmental issues through carbon neutrality and digital transformation for the Fourth Industrial Revolution. The digital twin, which is at the core of CPS, must support the visualization and analysis of energy consumption issues from various perspectives, and at the same time, support multi-faceted analyses. In this paper, a digital twin that supports the monitoring and analysis of the energy consumption of machine tools and an established CPS environment are proposed. The digital twin consists of a static model containing structural information, a dynamic model expressing the behavior of the object, a visualization model containing a three-dimensional model, and a configuration model linking those three together. In addition, a CPS environment that supports this is implemented through a commercial internet-of-things platform. Lastly, the practicality of the proposed model and system are verified through case studies and a comparative analysis. This research is expected to prompt innovation in the development of cutting-edge machine tools that are more intelligent and closer to carbon neutrality.

The Development of Utilization Rate and Energy Consumption Monitoring and Networking System for Old Machine

The Development of Utilization Rate and Energy Consumption Monitoring and Networking System for Old Machine

Development and validation of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method including 25 novel synthetic opioids in hair and subsequent analysis of a Swiss opioid consumer cohort.

Major public health concern is raised by the evidence that common drugs like heroin are now frequently laced or replaced with highly potent novel synthetic opioids (NSOs). The objective of this study was to explore the prevalence and patterns of NSOs in a cohort of Swiss opioid users by hair analysis. Hair analysis is considered an ideal tool for retrospective consumption monitoring. Hair samples from 439 opioid users in Zurich were analyzed. Study inclusion required a previous positive hair test result for heroin metabolites, oxycodone, fentanyl, methadone, or tramadol. The samples were extracted with a two-step extraction procedure, followed by a targeted LC-MS/MS (QTRAP® 6500+) analysis in multiple reaction monitoring mode for a total of 25 NSOs. The method underwent full validation and demonstrated good selectivity and sensitivity with limits of detection (LOD) as low as 0.1pg/mg. The analyzed sample cohort demonstrated a positivity rate for NSOs of 2.5%, including the following NSOs: butyrylfentanyl, acrylfentanyl, furanylfentanyl, methoxyacetylfentanyl, ocfentanil, U-47700, isobutyrylfentanyl and benzylfentanyl. Furthermore, we were able to identify specific consumption patterns among drug users. The results indicate that hair analysis is a valuable tool for investigating the prevalence of NSOs in drug-using populations, which seems to be low in the case of Swiss opioid users. Nevertheless, the results highlight the need for sensitive analytical detection methods in forensic toxicology to identify and monitor substance distribution in different populations.

Has the digital transformation promoted energy-saving-biased technological progress in China’s manufacturing sector?

ABSTRACT In the context of ‘Digital China’ and the ‘Dual-Carbon’ targets, there is an escalating need for energy saving and pollution reduction in China’s manufacturing sector. By optimizing resource allocation, enhancing precision in energy consumption monitoring and fostering the integration of traditional production factors, digital elements are pivotal in steering the sector towards energy technology progress, thereby affecting overall energy usage and carbon emissions. This study leverages panel data from 2009 to 2020 to quantify the manufacturing sector’s digital transformation and its energy-saving technology progress bias. It also examines the catalytic role of digital transformation in fostering energy-saving technological progress. The findings reveal the following: (1) The trajectory of technological progress within China’s manufacturing sector has progressively improved and predominantly demonstrates an energy-saving bias. (2) Digital transformation has significantly catalysed energy-saving-biased technological progress across various manufacturing subsectors, this conclusion is still significantly credible after a series of robustness tests. (3) Heterogeneity analysis reveals that as the digital transformation progresses from collaborative innovation to comprehensive digitalization phases, it has dramatically optimized the energy-saving-biased technological progress in various industries, especially in pollution-intensive industries. These insights offer substantial implications for policy formulation and industrial evolution.

Employees' engagement, family ownership, or gender diversity? Searching for determinants of SMEs' green behaviors in CEE countries

AbstractResults of this study go against previous expectations regarding the positive effects of boards' and owners' gender diversity and family ownership on SMEs' green behaviors, expressed by the implementation of proactive environmental strategies (PES) and energy consumption monitoring (ECM). We show that such generally accepted and recommended concepts may not always work, specifically within Central and Eastern European (CEE) countries. Based on the foundations of stakeholder theory, we surprisingly reject hypotheses expecting positive effects of boards' gender diversity on SMEs' green behaviors. Compared to the research to date, we newly examine the effect of the frequency of manager–employee meetings on SMEs' green behaviors. We show that SMEs' implementation of PES and ECM requires a different frequency of employee engagements. In addition, we confirm that the presence of environmental managers is fruitful for the implementation of PES in family firms. We also reveal that family firms' ECM is significantly triggered by energy taxes, but also by the owners' gender diversity. By linking the managerial perspective including employees' engagement and PES with the technical discipline of ECM, this study provides several theoretical and practical implications.

Privacy-Preserving Electricity Trading for Connected Microgrids

The electricity market is evolving from the traditional unidirectional model into a bidirectional one in which households also generate and sell energy. This new scenario requires technology able to manage the available energy and guarantee that all the participants pay or are paid appropriately. Unfortunately, fine-grained monitoring of energy production and consumption makes it possible to infer sensitive information about confidential aspects of people’s private life. In this paper, we propose a system designed for privacy-preserving electricity trading in a connected microgrid. The system guarantees that at the end of a billing period, the distribution system operator can compute the quantity to be charged or paid to each household while being unable to trace its consumption details.