Smart Energy Monitoring System Research Articles

Centralized smart energy monitoring system for legacy home appliances

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants.

A novel coreless current sensing mechanism for two-wire power cord

In the development of smart home products, energy monitoring, and control systems, there is a significant need for precise and reliable electric current measurement. Most domestic appliances, whether residential or commercial, are powered by power cords with two wires. A reliable and accurate current probe would be required to detect current in two-wire power cables. Existing current sensors for two-wire power cables are complex, bulky (they use a magnetic core), and need full access to the power cord. In this paper a coreless, contactless current probe that requires only limited access of the space around the two-wire power cable is proposed. The sensor unit presented here measures the current accurately even for certain types of misalignments of the power cord. This makes the current measurement less sensitive to minor mechanical displacements, radius of the conductors, and the overall insulation thickness of power cord. In the proposed current probe, one-of-a-kind and highly reliable arrangement of magnetic field sensors, are used to measure the magnetic flux density caused by the current passing in the power cord. The current is computed using a derived mathematical formula which is expressed in terms of the voltage outputs of the magnetic sensors. To provide a clear understanding of the measurement process, an analysis using the Finite Element Method (FEM) is performed. The functionality of the proposed current probe is theoretically tested. Considering different sources of errors, an accuracy of less than 1.5% is obtained.

An IoT-based intelligent smart energy monitoring system for solar PV power generation

Abstract As the world’s attention turns to cleaner, more dependable, and sustainable resources, the renewable energy sector is rising quickly. The decline in world energy use and climate change are the two most significant factors nowadays. PV forecasting was essential to enhancing the efficiency of the real-time control system and preventing any undesirable effects. The smart energy management systems of distributed energy resources, the forecasting model of irradiation received from the sun, and therefore PV energy production might mitigate the impact of uncertainty on PV energy generation, improve system dependability, and increase the incursion level of solar power generation. Smart sensors and Internet of Things technologies are essential for monitoring and controlling applications in a broad range of fields. As a result, solar power generation forecasting was essential for microgrid stability and security, as well as solar photovoltaic integration in a strategic approach. This paper examines how to use IoT, a solar photovoltaic system being monitored, and shows the proposed monitoring system is a potentially viable option for smart remote and in-person monitoring of a solar PV system.

Smart Energy Meter using IoT

In recent times, internet of thing based applications are more efficient to provide solution regarding the real time problems. The labor involved in gathering energy utility meter readings. The Internet of Things (IoT) provides an efficient and cost-effective way to wirelessly send energy consumer information as well as monitor power use. In this paper we are presenting smart energy monitoring system. the main objective of our project is reduced manual efforts for measuring electricity by Internet and make the electrical appliance intelligence and provide consolation to consumer and also get reduction in electric bill.

Smart Campus 4.0: Digitalization of University Campus with Assimilation of Industry 4.0 for Innovation and Sustainability

According to the United Nations, global sustainability in terms of social, economic, and environmental issues must be achieved by 2030. SDGs 4 and 9 are related to education and strengthen the attainment of quality education and infrastructure innovation. Resilient infrastructure plays a significant role in strengthening the campus in terms of education, management, placement and environment. These all aspects come under the smart campus. Smart campus 4.0 is the amalgamation of multitude industry 4.0 enabling technologies for delivering smart and innovative facilities with the aspect of sustainability. The previous studies have proved that the sustainable development goals (SDGs) can be achieved with the amalgamation of industry 4.0 enabling technologies in the campus such as cloud computing, artificial intelligence (AI), Internet of things (IoT), edge/fog computing, blockchain, robot process automation (RPA), drones, augmented reality (AR), virtual reality (VR), big data, digital twin, and metaverse. The main objective of this study to provide the detailed discussion of all industry 4.0 enabling technologies in single research related to smart campus. The findings observed are IoT-Based Drone system is intended to ground patrolling, and a cloud server to develop a smart campus energy monitoring system. AI for campus placement prediction model; cloud and Edge computing architecture to build an intelligent air-quality monitoring system. The novelty of the study, it has discussed all industry 4.0 enabling technologies for a smart campus with challenges, recommendations, and future directions.

Smart Energy Monitoring and Controlling System

In recent times internet of thing based applications are more efficient to provide solution regarding the real time problems. The labor involved in gathering energy utility meter readings. The Internet of Things (IoT) provides an efficient and cost-effective way to wirelessly send energy consumer information as well as monitor power use. In this paper we presenting smart energy monitoring system. the main objective of our project is reduced manual efforts for measuring electricity by Internet and make the electrical appliance intelligence and provide consolation to consumer and also get reduction in electric bill

Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System

Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

IOT Based Smart Energy Monitoring and Billing System

Electricity is one of the fundamental necessities of human beings, which is commonly used for domestic, industrial, and agriculture purposes. The present system of energy metering as well as billing in India uses electromechanical and somewhere digital meter. it consumes more time and labor. One of the prime reasons is the traditional billing system which is inaccurate. The smart energy meter gives real power consumption as well as accurate billing. In the project, we will measure the energy being consumed in the house and will generate its bill automatically using telemetric communication. This will help in reducing the consumption of energy in the house as the owner will continuously be notified about the number of units that are being consumed. The main objective is to generate the bill automatically by checking the electricity unit’s consumption of the house to reduce the manual labor. The calculations will be performed controller and the bill will be updated on to the clouds by using a network of Internet of Things. The bill amount can be checked by the user and suppler anywhere remotely.

Machine Learning in Smart Home Energy Monitoring System

In order to solve the insufficiency of the existing smart home energy monitor ing system in autonomous adaptability, a smart home energy monitoring system based on machine learning and embedded technology is proposed. The system uses a gatewa y to collect sensor data, and then uses a cloud computing platform running Hadoop and machine learning algorithms to learn and identify user behaviours to achieve autonom ous decision-making capabilities. Through the analysis of examples, it can be seen that the solution greatly improves the humanization of the smart home system.

Development of a Smart Energy Monitoring System

The preservation of energy and the fight against global climate change are both benefited by computationally intelligent energy forecasting techniques for effective energy management at the consumer/producer side. Worldwide energy production and consumption are quite high, necessitating intelligent approaches with practicable implementation potential for effective energy management. The development of smart energy monitoring systems has become an increasingly important area of research in recent years. With the growing concern over climate change and the need to reduce greenhouse gas emissions, it is essential to find new ways to conserve energy and promote sustainable practices. One way to achieve this goal is by using smart energy monitoring systems that enable users to monitor and control their energy consumption. In this article, we will discuss the development of a smart energy monitoring system, its components, benefits, and challenges.

Smart Energy Management and Monitoring System for Public Lighting System based on Photovoltaic and Storage Systems

The public lighting system was being a vital part all over the world for many years. Indeed, as the number of population and urbanism grow, this system is becoming a primordial part of our life comfort. Therefore, it is used extensively leading to an increased important amount of electrical energy being consumed. This paper proposes an idea of an intelligent energy management system (EMS) for public lightening system consumption based on an autonomous hybrid sources of electrical energy. It is composed of a PV system installed at the top of the lightening system and a battery storage system, where an intelligent energy management system is used to ensure the charging of the battery during the daylight with the option of supporting the PV system production in case of low period irradiation during the day. Whereas, during the night, the battery fully powers the lightening system. In the same for the protection of the battery system and its related lifespan, the proposed (EMS) takes into accounts this issue. Furthermore, this system has the ability of recognizing accurately the basic data on the length of the day, the sunrise time and sunset time automatically based on the location of the used public lightening system which is defined by its longitude and latitude. The proposed EMS is built around a special electronics card including display screen of main information with the insertion of special sensors such as current and voltage sensors, which were built by the authors. The proposed system has been tested in a known location where the obtained results prove the validity of the proposed management system for ensuring the operation of the public lightening system.

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

Making climate public: energy monitoring and smart grids as political participation

This article presents the findings of ethnographic research in the UK with a network of engineers, activists, and citizens involved in developing smart energy monitoring systems and community smart grids. The paper explores how everyday uses of data, material evidence, and sensory information on material and thermodynamic processes that appear in such projects, are opening up new spaces for public participation in climate change politics. Here, familiar discursive and deliberative forms of democratic participation are supplemented by what I term material diagnostics�a practice of public participation that revolves around a collective effort to unpack and rethink infrastructures as sites of climate action. Building on these findings, the paper suggests that everyday digitally informed experiments with urban infrastructures have the potential to extend the kinds of political subjectivities and participatory politics that are possible, as governments seek to transition to a net-zero future.

Making climate public: energy monitoring and smart grids as political participation

This article presents the findings of ethnographic research in the UK with a network of engineers, activists, and citizens involved in developing smart energy monitoring systems and community smart grids. The paper explores how everyday uses of data, material evidence, and sensory information on material and thermodynamic processes that appear in such projects, are opening up new spaces for public participation in climate change politics. Here, familiar discursive and deliberative forms of democratic participation are supplemented by what I term material diagnostics�a practice of public participation that revolves around a collective effort to unpack and rethink infrastructures as sites of climate action. Building on these findings, the paper suggests that everyday digitally informed experiments with urban infrastructures have the potential to extend the kinds of political subjectivities and participatory politics that are possible, as governments seek to transition to a net-zero future.

Retraction: An Efficient Power Theft Detection Using Mean-Shift Clustering and Deep Learning in Smart Grid (IOP Conf. Ser.: Mater. Sci. Eng. 983 012003)

This article has been retracted by IOP Publishing following an allegation that this article contains text overlap from multiple unreferenced sources [1, 2, 3]. IOP Publishing has investigated and agree the article constitutes plagiarism. IOP Publishing also expresses concern regarding a number of nonsensical phrases used in the article, which suggests the article may have been created at least partly by artificial intelligence or translation software.If the authors wish to contest this retraction they are advised to contact researchintegrity@ioppublishing.org.1. Jeyaranjani J, Devaraj D, "Machine Learning Algorithm for Efficient Power Theft Detection using Smart Meter Data" in International Journal of Engineering & Technology, vol. 7.3, pp. 900-904, 2018, https://www.sciencepubco.com/index.php/ijet/article/view/19585 2. Aswini. R. and Keerthiha. V., "IoT Based Smart Energy Theft Detection and Monitoring System for Smart Home," 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), 2020, pp. 1-6, https://ieeexplore.ieee.org/document/9262411 3. Hasan M, Toma RN, Nahid AA, Islam MM and Kim JM (2019) "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach" in Energies 2019, 12(17), 3310; https://doi.org/10.3390/en12173310 Retraction published: 09 November 2021

Performance Analysis of Smart Energy Monitoring Systems in Real-time

The use of smart energy monitoring systems in buildings offers significant potential for energy saving. The introduction of a new kind of energy monitoring system is essential in the consumer-side in order to meet the demands of energy conservation. In this paper, four energy monitoring systems based on advanced wireless technology are proposed. The design and implementation of the smart real-time energy monitoring systems is analyzed with respect to their performance. The proposed systems utilize advanced wireless technologies like the Zigbee module, Internet of Things (IoT), Android Mobile Apps, and cloud computing for interfacing the information between the meters and the consumer end. A digital power meter is installed in the main panel, interfaced with a communication gateway and parameters such as voltage, current, power, power factor, and the harmonics of household appliances are measured in real-time. These systems provide power consumption details to the consumer and allow interacting with them by giving instant data such as live power tracking, identification of abnormal patterns of energy usage, energy bill estimation, and energy usage information.

Smart Energy Metering System using Android Mobile Application

In recent years, smart-devices became very popular. This paper presents the smart energy monitoring system using android application. This system is efficient and convenient to achieve the visualization of real time values that are received from the energy meters via NodeMCU module. This application enables the user to control their power usages in the house by knowing about the consumption of power at any time. In this the analog data is converted to digital data with the help of inbuilt NodeMCU and passes it to the Arduino controller. Android based mobile phone itself transmits and receives data using Arduino IDE module, power supply and NodeMCU.

Automatic Detection of Manufacturing Equipment Cycles Using Time Series

Abstract Manufacturing industry companies are increasingly interested in using less energy in order to enhance competitiveness and reduce environmental impact. To implement technologies and make decisions that lead to less energy demand, energy/power data are required. All too often, however, energy data are either not available, or available but too aggregated to be useful, or in a form that makes information difficult to access. Attention herein is focused on this last point. As a step toward greater energy information transparency and smart energy-monitoring systems, this paper introduces a novel, robust time series-based approach to automatically detect and analyze the electrical power cycles of manufacturing equipment. A new pattern recognition algorithm including a power peak clustering method is applied to a large real-life sensor data set of various machine tools. With the help of synthetic time series, it is shown that the accuracy of the cycle detection of nearly 100% is realistic, depending on the degree of measurement noise and the measurement sampling rate. Moreover, this paper elucidates how statistical load profiling of manufacturing equipment cycles as well as statistical deviation analyses can be of value for automatic sensor and process fault detection.

Employing electricity-consumption monitoring systems and integrative time-series analysis models: A case study in Bogor, Indonesia

The Paris Agreement calls for maintaining a global temperature less than 2°C above the pre-industrial level and pursuing efforts to limit the temperature increase even further to 1.5°C. To realize this objective and promote a low-carbon society, and because energy production and use is the largest source of global greenhouse-gas (GHG) emissions, it is important to efficiently manage energy demand and supply systems. This, in turn, requires theoretical and practical research and innovation in smart energy monitoring technologies, the identification of appropriate methods for detailed time-series analysis, and the application of these technologies at urban and national scales. Further, because developing countries contribute increasing shares of domestic energy consumption, it is important to consider the application of such innovations in these areas. Motivated by the mandates set out in global agreements on climate change and low-carbon societies, this paper focuses on the development of a smart energy monitoring system (SEMS) and its deployment in households and public and commercial sectors in Bogor, Indonesia. An electricity demand prediction model is developed for each device using the Auto-Regression eXogenous model. The real-time SEMS data and time-series clustering to explore similarities in electricity consumption patterns between monitored units, such as residential, public, and commercial buildings, in Bogor is, then, used. These clusters are evaluated using peak demand and Ramadan term characteristics. The resulting energy-prediction models can be used for low-carbon planning.

A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation

With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.