Monitor Electricity Consumption Research Articles

Design of Local Micro-Grids to Solve the Electricity Shortage in Iraq Cities

Iraqi cities suffer from a shortage of electric power due to poor production and deterioration of transmission and distribution lines. There is no prospect currently for improving the grid, despite the government's promises. So it has become necessary to find alternatives, at least at the local level. This research, presented a successful alternative, which applied all over the world, which is the local microgrid. Also, it’s developed a design for this microgrid that suits the conditions of Iraq and supports the integration of clean energy produced by the consumer. The results indicate the success of investing in this microgrid by small investors, foreign companies or local administrations of the cities that want to develop their cities in isolation from the problems of the country's national electrical grid. This solution reduce the pressure on the national grid on one hand and improve the electricity service for the consumer on the other hand, by providing the pivotal key to the solution, which is replacing the blind system of paying wages (for electricity service) currently followed in Iraq with the billing system followed in all developed countries of the world. This solution provides the possibility of deploying the economic solar PV system (without batteries) in Iraqi houses to reduce the bills and also allows personal monitoring of electricity consumption so that the consumer can reduce loads during peak times to reduce bills as well.

IoT Sensor Network Electricity Consumption Behaviour Using ClusterAnalysis Algorithm for Network Environment

The Internet of Things (IoT) sensor network plays a crucial role in monitoring electricity consumption behavior by collecting real-time data from various connected devices. Utilizing cluster analysis algorithms, researchers can effectively segment and analyze consumption patterns within a network environment. By grouping similar usage behaviors, these algorithms reveal insights into energy efficiency, peak usage times, and anomalies in consumption. The paper presents a novel methodology for analyzing and classifying users' electricity consumption behavior, utilizing the Clustering Behavior Analysis Weighted Classification (CBAWC) algorithm. By leveraging cluster analysis techniques and weighted classification, the proposed approach allows for the segmentation of consumers into distinct clusters based on their consumption patterns. Through the application of CBAWC, the study provides insights into the diverse behaviors exhibited by consumers, ranging from moderate to high consumption levels, varied peak hours, and peak days. The classification results demonstrate the effectiveness of the algorithm in accurately assigning users to their respective clusters, enabling stakeholders to better understand consumption trends and tailor energy management strategies accordingly. Through the application of CBAWC, consumers are segmented into distinct clusters based on their consumption patterns. For example, clusters exhibit varying average daily consumption levels, with Cluster 1 consuming around 300 kWh, Cluster 2 consuming approximately 450 kWh, and Cluster 3 consuming about 280 kWh on average. Additionally, clusters display different peak hours per day and peak days per month. The classification results demonstrate the algorithm's effectiveness, with high accuracy scores ranging from 0.86 to 1.00 across different user groups.

SCADA Implementation on Solar Panel Electricity Consumption Monitoring and Regulation System

In an effort to motivate students and understand application of SCADA to the Solar Panel system which is one source potential non - fossil energy to be used as solution to meeting sustainable energy needs.​ Where in its operation , efficiency and reliability Solar Panel system is still a major problemthat must be overcome . Utilization of Supervisory Control and Data Acquisition (SCADA) in management Solar Panel Systems (PLTS) have become significant solution​ to improve loading efficiency . The purpose of this research is to create a prototypes solar panel practicum as a learning media to motivate student in understanding how solar panels work and can apply the process of monitoring and regulating solar panel system with SCADA in real-time. The equipment used​ includes Solar Panels, Solar Charger Controller (SCC), Inverter, Battery , PLC Omron CP1E-NA20DR-A and CP1W AD041, ACS712 Current Sensor , ZMPT101B Voltage Sensor, DHT22 Temperature and Humidity Sensor and Light Sensor (LDR) as well as CX- Programmer software Ver. 9.5 and CX -Supervidor Ver.4.0. The method used is Research and Development method as the basic for design and manufacture of solar panel practicum tools and the application of SCADA to the monitoring system and electricity usage of solar panels . From the results of tests conducted with the application of SCADA on solar panels can monitor Voltage , Current , Power, Temperature , Light and load usage regulator electric solar panels based on electrical energy consumption, as well as the condition of electrical equipment connected to the solar panel system with an average error values : Voltage 0.084%, Current 0.155%, Power 0.107% and Temperature 45 0C and Light 658.7 Lux.

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.

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.

Advanced tools for electricity consumption monitoring

In this work, the power monitoring system developed at the University of León is presented. This system includes both traditional and advance monitoring tools based on statistical and data mining techniques. These techniques exploit data in order to search electrical patterns, detect anomalies, predict future consumption, optimize power, etc. Some results obtained from the monitoring system during year 2010 are presented.

"CAPABILITIES OF NB-IoT TECHNOLOGY FOR SOLVING TASKS OF MONITORING, ACCOUNTING AND COLLECTING DATA ON ELECTRICITY CONSUMPTION "

"Problems with accounting, collection and correct readings of electricity meters are of great importance when preparing and invoicing for electricity, especially in neighborhoods with private households, where the process of monitoring electricity consumption is not always provided for various reasons beyond the control of the consumer of services. The development of modern information technologies, including mobile communications using the technology of ΝΒ-ΙοΤ, allows us to solve problems related to improving the efficiency of monitoring, accounting, and data collection of electricity consumption in microdistricts not only quickly and efficiently, but also increase the management efficiency of the process of collecting meter readings, as well as reducing the costs of the Service Provider for transport services, personnel and other items of expenditure. The article presents an analysis of various brands of metering devices used in the Urker microdistrict and the use of metering devices with the support of technology ΝΒ-ΙοΤ. A simulation model of access to metering devices using the technology of ΝΒ-ΙοΤ is shown. The issues related to the causes and problems of electricity theft are touched upon. The article will be useful to students, doctoral students, engineers, anyone who is related to automation and control of technological processes and productions, as well as anyone who deals with the problems of implementing the ASUE, IoT technology. "

Non-intrusive condition monitoring based on event detection and functional data clustering

Implementing monitoring electricity consumption strategies in industrial environments provides improvements in both the maintenance process and energy efficiency. The contribution of this work is an industry-oriented non-intrusive load monitoring approach based on an unsupervised algorithm, encompassing a method of event detection, functional data clustering, and condition monitoring. With this method, multiple devices can be monitored by only one electric meter in industrial environments, enhancing the early detection of anomalies and energy inefficiencies. The proposed approach presents a robust event detection to deal with different industrial contexts due to its intuitive parameters, which allows adapting the detection to be more sensitive or to filter out higher noise variations. Unlike other feature-based clusterings, the proposed functional data clustering enables high-precision identification of transient state patterns, characterizing specific shapes for each pattern to properly cluster them, and provides higher reliability during load detection. Thus, this load detection segments the power consumption to extract transient states and identify which load acts on each event based on functional data clusters. By detecting when loads start to consume, the proposed energy disaggregation extracts the frequency spectrum of each device from the aggregate current consumption, which is used in a condition monitoring strategy to track the spectrum behavior of each device. In this way, the condition of multiple loads can be monitored using a single electric meter, whose information can be relevant to accurately schedule maintenance interventions and detect anomalies or inefficiencies early. The proposed approach was validated in three industrial contexts: The load detection accuracy was verified in an industrial testbed, giving a precision higher than 99% for monitoring five devices. The second and third industrial scenarios validate the accuracy of the proposed condition monitoring method. The last scenario was carried out at Bilbao airport to track the condition of multiple conveyor belts of a baggage handling system located at check-in. As a result, the degradation trend of three sets of conveyor belts was monitored.

Design of Realtime Electricity Consumption Monitoring System Using Campus Web

In this study, an electricity consumption monitoring system that integrated with the campus web was developed. The system was designed using the Waterfall method approach. A realtime electricity consumption monitoring has been developed and has been implemented and can be accessed. ISO 50001 is a globally accepted standard for managing energy, providing technical and corporate management strategies to improve energy efficiency, reduce costs, and enhance environmental performance. Web technology that internet media can access can facilitate the implementation of transparent monitoring of electrical energy consumption.

Managing Electricity Consumption on Campus: The Effect of Online Learning from Home

This paper analyzes the effect of Online Learning from Home (OLFH) during the COVID-19 pandemic on electricity consumption and the quality of the learning process quality. This study identifies the reduction of electricity consumption on campus during OLFH, which can be utilized to maintain the quality of the learning process. The electricity consumption data was analyzed from the direct measurement using the electricity consumption real-time monitoring system. Again, the data analyzed of the online learning process quality was obtained from the student survey via Google Forms. The results of monitoring electricity consumption before and during OLFH showed that the most significant decrease in electricity consumption occurred in classroom buildings, with a decrease value of 68.74%. Meanwhile, the lowest reduction of electricity consumption occurred in the rectorate building, with its reduced value of 19.02%. A student survey showed that most respondents were satisfied with the transition from face-to-face learning to OLFH during the COVID-19 pandemic. However, this study also identified negative aspects of OLFH. Reducing electricity consumption during OLFH will cost saving for the university. The cost saving can be used to implement programs to address the deficiencies of OLFH and improve its infrastructure so that the quality of the learning process is maintained.

Electrical Control and Monitoring Home System With ACS712 Sensor & Relay Module via Blynk

Electrical load control and monitoring system is a device designed to control and monitor electricity consumption. The process of monitoring electric loads uses a power sensor model ACS712 which is connected to a relay module to supply electric loads and stop using electric loads if power consumption exceeds a predetermined capacity of 1000 watts so that energy control can be carried out. The microcontroller used is the NodeMCU esp8266 microcontroller which functions as the main control system that will be linked to the blynk application with the aim of being able to monitor it in real time in the future. By conducting an experiment assembling the ACS712 sensor module with NodeMCU esp8266 which produces a monitoring feature for reading current, power, energy and peak power measurements from the IoT blynk application through the ACS712 sensor. The results of this study are a control and monitoring of household electricity measurements through the blynk application to control the use of electrical energy loads from household electronic devices in the form of readings of current, power, energy, and stopping the supply of electric power to loads connected to the ACS712 sensor and relay. module. The Blynk IoT application, the ACS712 sensor is almost close to the measuring instrument or clamp meter used so that it is able to properly and correctly monitor electricity usage in household loads

Pre-SEMMS: A Design of Prepaid Smart Energy Meter Monitoring System for Household Uses Based on Internet of Things

The use of prepaid smart electricity services from the State Electricity Company (SEC) for the general public in Indonesia has increased. It is because prepaid electricity has several advantages compared to postpaid electricity. One of them is that it is becoming easier for customers to manage and control their daily electricity usage. Customers can also estimate their total electricity consumption each month. However, customers still have to manually view the information on the electricity meter to find data and information on prepaid electricity. It can make it difficult for customers if the electricity meter is placed outside the house, which is quite far away. Customers must also constantly monitor the use of electricity. This article discusses a prepaid smart electricity consumption monitoring design using Internet of Things technology. The design is carried out without changing the standard prepaid electricity meter system from the SEC. However, a KWH meter tool has been developed that has been combined with Internet of Things technology and can calculate and monitor electricity usage. The results show that with this system, customers can more easily find out and monitor their daily electricity consumption.

Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems

In smart grids, homes are equipped with smart meters (SMs) to monitor electricity consumption and report fine-grained readings to electric utility companies for billing and energy management. However, malicious consumers tamper with their SMs to report low readings to reduce their bills. This problem, known as electricity fraud, causes tremendous financial losses to electric utility companies worldwide and threatens the power grid’s stability. To detect electricity fraud, several methods have been proposed in the literature. Among the existing methods, the data-driven methods achieve state-of-art performance. Therefore, in this paper, we study the main existing data-driven electricity fraud detection methods, with emphasis on their pros and cons. We study supervised methods, including wide and deep neural networks and multi-data-source deep learning models, and unsupervised methods, including clustering. Then, we investigate how to preserve the consumers’ privacy, using encryption and federated learning, while enabling electricity fraud detection because it has been shown that fine-grained readings can reveal sensitive information about the consumers’ activities. After that, we investigate how to design robust electricity fraud detectors against adversarial attacks using ensemble learning and model distillation because they enable malicious consumers to evade detection while stealing electricity. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance electricity fraud detection.

Will NILM Technology Replace Multi-Meter Telemetry Systems for Monitoring Electricity Consumption?

The estimation of electric power utilization, its baseload, and its heating, light, ventilation, and air-conditioning (HVAC) power component, which represents a very large portion of electricity usage in commercial facilities, are important for energy consumption controls and planning. Non-intrusive load monitoring (NILM) is the analytical method used to monitor the energy and disaggregate total electrical usage into appliance-related signals as an alternative to installing multiple electricity meters in the building. However, despite considerable progress, there are a limited number of tools dedicated to the problem of reliable and complete energy disaggregation. This paper presents an experiment consisting in designing an electrical system with electrical energy receivers, and then starting NILM disaggregation using machine learning algorithms (MLA). The quality of this disaggregation was assessed using dedicated indicators. Subsequently, the quality of these MLA was also verified using the available BLUED data source. The results show that the proposed method guarantees non-intrusive load disaggregation but still requires further research and testing. Measurement data have been published as open research data and listed in the literature section repository.

ОПТИМИЗАЦИЯ СИСТЕМЫ УПРАВЛЕНИЯ ПРОГРАММНО-АППАРАТНОГО КОМПЛЕКСА ДЛЯ УЧЕТА ЭЛЕКТРОЭНЕРГИИ

The presented article explores the topic of optimizing the control system software and hardware complex for electricity metering. The control system plays a crucial role in accurately measuring and monitoring electricity consumption. The main objective of this research is to identify and implement optimization techniques that enhance the performance and efficiency of the control system. Aim. Based on the analysis of existing electrical complexes, to develop an architecture, simulation digital models, effective control al-gorithms and methods of operation of an electrical complex based on three-phase digital measuring trans-formers with a control system of domestic production for electricity metering in high-voltage AC networks, ensuring operability and quality of functioning in various modes under various external influences. Mate-rials and methods. The scientific tasks have been solved using the general theory of electrical complexes and systems, the control systems theory, methods of physical, mathematical, simulation and computer modeling of components of electrical complexes and systems. Results. The article reflects the development of the general theory of electrical complexes, including digital combined measuring transformers and a control system in the form of a stand-alone integration module, in terms of optimizing system connections of components of electrical power metering complexes. Conclusion. The main practical result of the con-ducted research is the production of a prototype of an electrical complex based on prototypes of adaptive digital current and voltage transformers and an electrical complex control system for electricity metering based on a universal platform for measuring transformers. Such a construction of a digital three-phase measuring transformer and a universal terminal block (unification module) make it relatively easy to de-velop similar devices with other current and voltage ranges based on the developed prototype. In existing combined measuring transformers, such a combination of primary converters is not used.

Voltage, Current, Power Consumption Monitoring: A IOT Based System

Electricity is a basic human necessity that is extensively employed for home, industrial, and agricultural purposes. In this way, energy waste leads nations to lose revenue. Solutions based on technology, such as The Internet of Things (IOT) connects the physical and digital worlds, This IOT application ,In this scenario, manages and/or analyze energy consumption.. Furthermore, the advancement of micro and Nano-electronics has made it possible to the creation of connectivity modules like the XBEE that enables the rapid deployment of a wireless sensor network effectively, using the least amount of energy possible employed for responsibilities of monitoring and control. By developing a hardware and software solution, the given prototype takes advantage of the previously indicated features. It uses a scalable and modular platform with XBEE technology and a developed protocol for data exchange between the modules that make up the system to allow remote monitoring of electricity consumption in a home. The prototype's accuracy is shown when compared to readings acquired with a regular electric bill, according to the conclusions.

Electricity Consumption Measurement System Using Current Transformer Sensor TA12-100

Electricity consumption measurement system with an embedded system device with Arduino-based technology combined with a current transformer sensor TA12-100 which is connected to the load of electricity consumption. Arduino microcontroller which functions as the main control system that will be connected to the computer, with the aim of future loads so that they can be monitored in real time. By experimenting with the assembly of the Arduino sensor module combined with the current transformer sensor TA12-100, it is continued by comparing the current measurements obtained from the TA12-100 sensor readings to the ampere meter pliers. The results of this study obtained a measurement data for house electrical energy consumption and the percentage error from the comparison of the TA12-100 sensor readings with the readings from the ampere meter measuring instrument. By conducting this research, it is possible to obtain data on what percentage of error is obtained from the use of the TA12-100 sensor, so that later from the experimental results, the TA12-100 sensor is able to properly and correctly monitor electricity consumption at house loads.

Auditron: An IoT-Based Smart Audit System for Monitoring Electricity and Water Consumption

In today’s scenario, water and electricity consumption are at an all-time high. We use these commodities on a daily basis but never bother to analyze and track their usage. The average electricity consumption of a household in Bangalore is around 300 kWh per month. The average water consumption is 150 liters per person every day. Considering a family of 3 people, this amounts to around 4,500 liters of water per person every month. These consumptions amounts are very concerning especially in an ever-growing city like Bangalore. Due to the growing population, the water demand has drastically increased. In order to regulate and manage the usage of these resources, we are proposing a simple device of an Audit System Internet of Things (IoT) Technology to help users manage and track their water and electricity usage with ease, thus saving the resources and their expenditure.

IoT System for the Continuous Electrical and Environmental Monitoring into Mexican Social Housing Evaluated under Tropical Climate Conditions

This work presented the design, development, and implementation of a low-cost IoT system for real-time monitoring of electrical consumption and environment parameters inside social housing. The IoT monitoring system is composed of a set of remote measurement nodes that wirelessly sense and transmit records of the air temperature and relative humidity inside the house as well as the voltage, current, and power consumed by several electrical devices. A server coordinator composed of a Raspberry Pi was used for the interaction with the sensors through the Message Queue Telemetry Transport (MQTT) protocol and allowed the measured data packaging, fragmentation, transfer, storage, and cloud upload for remote multiuser visualization. The system was experimentally evaluated in an inhabited single-story house under tropical climate conditions. Operation tests indicate a successful performance of the protocols implemented for remote visualization and cloud storage. Moreover, an analysis of measured data allows the feasibility of identifying the occupants’ energy consumption patterns and their relationship with the search for comfortable environmental conditions. Thus, the proposed friendly framework is a promising alternative to energy management. Its implementation in embedded Linux-based systems provides the flexibility to integrate control strategies based on artificial intelligence.

A machine learning approach based on neural networks for energy diagnosis of telecommunication sites

Multi-layer-perceptron-feedforward neural-networks (MLPFFNNs) are proposed to monitor electricity consumption of generic telecommunication sites, thus addressing well-known energy saving and environmental issues. The proposed prediction tool helps infer telecommunication site efficiency, thus enabling undertaking suitable energy intelligence actions (monitoring, control and diagnosis, supervision) as a function of a large number of data and information. A trial-and-error analysis was performed to find the most accurate neural network for such a prediction task. Physical knowledge and intrinsic behavioral features of telecommunication sites indicated that 2 electricity consumptions- and 4 weather-related variables shall populate the input layer. The number of hidden neurons, along with training and test splitting percentages, were selected as the variables to refine within the networks development task. Statistical assessment of accuracy was conducted via joint evaluation of root mean square error and coefficient of determination. The precision and generalization of the best MLPFFNN were successfully assessed on a relevant sites dataset. Prediction percentage errors set to 5.65% and 5.77% for monthly and weekly assessment, respectively, thus confirming the suitability of the proposed modelling approach to meet regulatory standards (i.e., ISO 50001), requiring reliable and continuous monitoring and diagnosis of large fleets of industrial buildings, such as central offices and datacenters.