Real-time Energy Consumption Monitoring Research Articles

Potential Role of green hydrogen as an energy carrier in smart energy system communities

Smart energy systems refer to the use of advanced technologies and systems to optimize the generation, distribution, and consumption of energy. The main goal of such a concept is to create an intelligent energy infrastructure based mostly on sustainable solutions namely renewable generations. Notwithstanding, renewable energy resources by their nature are unprogrammable. The main challenge is to balance properly the demand and supply curve To do so, various interventions should be employed to improve the reliability of the system (namely: real-time energy consumption monitoring to optimize energy efficiency and integration of energy storage systems). The final outcome is significant energy saving as well as cost reduction and cutting carbon footprint.Hydrogen is mostly refers to as ‘future fuel’ due to its marvellous properties. It is an energy carrier that is characterized by water and heat as byproducts of combustion. Furthermore, it can be used in a variety of applications, including transportation, power generation, and industrial processes. It can be used in fuel cells to power electric vehicles or blended directly with natural gas to reduce GHG emissions.The current work investigates the potential role of Hydrogen inside a smart energy system on a community scale. Various contributions are defined for Hydrogen inside a community featuring power to gas, power to vehicles or blending into NG. The layout is composed of hybrid electric, thermal and cooling power generation which is integrated with storage systems. At the end of the simulation, various scenarios are compared to each other in terms of energy performance, economic indicators and environmental impacts to carry out the best suitable option.

The Development of Real-Time Energy Consumption Monitoring using IoT

Energy shortage is a global challenge with significant implications for economies, societies, and the environment, including the Philippines. Promoting energy conservation in households is an effective approach to address this issue. In the Municipality of Midsayap, North Cotabato, Philippines, unmonitored energy consumption leads to excessive energy usage in households. To address this problem, this paper aims to research, build, test and implement a Real-Time Energy Consumption Monitoring (RECM) device using IoT technology. The RECM device, equipped with an SCT013 current sensor, enables real-time 24/7 monitoring of energy consumption. The monitored data is displayed in graphical and numerical formats using the Thing Speak cloud storage service. The RECM device was deployed in households, and a survey was conducted to evaluate its functionality and effectiveness. The results indicate that the design of the RECM device is a highly useful and efficient tool for real-time energy consumption monitoring. This paper provides circuit diagrams, wiring diagrams, and the list of materials used to develop the Real-Time Energy Consumption Monitoring (RECM) device using IoT.”

Comprehensive Energy Modeling Framework for Multi-Powertrain Bus Transit Systems

With the increasing emphasis on sustainable transportation solutions, there is an urgent need for a system that can accurately assess vehicle-specific energy consumption in real-time. However, the current modeling frameworks have a significant gap in that they are not demand- and congestion-responsive, nor are they capable of modeling the system-wide energy consumption of bus fleets at the vehicle-level, encompassing all three types of powertrains—conventional, hybrid, and electric. To address this gap, we present a comprehensive modeling framework that takes into account passenger loading, considers the dynamics of the system including acceleration, and is capable of predicting the real-time energy usage of each bus in a transit network. Using the Pioneer Valley Transit Authority system as a case study, we calibrated existing powertrain-specific models for conventional diesel, hybrid, and battery electric buses. Our demand- and congestion-responsive energy consumption modeling framework fills this critical gap and offers significant potential for future sustainable transportation planning. Relying on detailed trajectories computed from one month of bus location data, along with passenger counts and road grade, our framework is a novel contribution in that it provides spatiotemporal energy predictions for an entire bus system. As it is readily calibrated with potential improvements in accuracy given more data, this framework can be applied by transit agencies for the purposes of real-time energy consumption monitoring, fleet electrification planning, equity analyses, and carbon emission reduction strategies.

Energy Meter Billing and Monitoring System using IoT

With the increasing demand for energy and the growing concern for sustainable development, energy conservation has become a necessity. It is essential to ensure that electricity is being consumed efficiently and not wasted, which can lead to a significant increase in energy bills. Moreover, the traditional energy billing methods are prone to errors and are not user-friendly, resulting in inaccurate bills and customer dissatisfaction. In light of these issues, a system that is based on the Internet of Things (IoT) has been proposed and analyzed for energy meter billing and monitoring. The proposed system employs cutting-edge technology to automate the energy billing process and provides real-time monitoring of energy consumption, which helps customers to keep track of their energy usage and reduce their electricity bills. One of the most significant advantages of the IoT-based energy meter billing and monitoring system is that it is highly scalable and can be easily customized to meet the specific needs of each customer. It provides a secure and reliable platform for data communication between the energy meter and the web server, which ensures accurate billing and eliminates the need for manual meter reading. Furthermore, the proposed system is also environmentally friendly as it promotes energy conservation and reduces the carbon footprint. With the help of real-time monitoring, customers can identify areas where energy is being wasted and take corrective actions, such as turning off appliances when not in use, which helps in reducing energy consumption and conserving resources

Long Term Energy Savings Through User Behavior Modeling in Smart Homes

The Internet of Things (IoT) has enabled real-time monitoring of energy consumption in smart homes through sensors embedded in the surrounding environment. In the post-pandemic world, domestic energy management has gained importance due to increased work-from-home consumption, making data collection in a smart home a relevant IoT application with many potential energy savings. However, this information is difficult for most users to understand, and existing monitoring systems’ savings results degrade over time. To address these challenges, this study presents a novel approach for domestic energy consumption, production, and comfort perception using color-based dashboards enhanced for user feedback interaction. The approach includes the management of in-home appliances and comfort levels according to user preferences to attain long-term energy savings. The approach includes multiple appealing strategies such as 3D representation, mobile connectivity, utility integration, and dynamic information, to increase long-term engagement and provides quantitative data on energy savings achieved for one year, where the average energy consumption was reduced by 19%. It was found that the approach sustained user engagement over time, with users actively participating in energy conservation efforts. A community survey with 208 participants was also developed and studied where 69% of the enquired considered our approach more attractive than existing market solutions, and 79% considered it more useful than existing solutions. Regarding the real-time information presented on our approach, 81% of the participants strongly or totally agree that it can change users’ behaviors.

Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm.

Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strategies to achieve safe, intelligent, and visualized energy management. First, the theory of Convolutional Neural Network (CNN) is discussed. Besides, an intelligent energy-saving model based on CNN is designed to ameliorate the variable energy consumption, load, and power consumption of the CDC data center. Then, the core idea of the policy gradient (PG) algorithm is introduced. In addition, a CDC task scheduling model is designed based on the PG algorithm, aiming at the uncertainty and volatility of the CDC scheduling tasks. Finally, the performance of different neural network models in the training process is analyzed from the perspective of total energy consumption and load optimization of the CDC center. At the same time, simulation is performed on the CDC task scheduling model based on the PG algorithm to analyze the task scheduling demand. The results demonstrate that the energy consumption of the CNN algorithm in the CDC energy-saving model is better than that of the Elman algorithm and the ecoCloud algorithm. Besides, the CNN algorithm reduces the number of virtual machine migrations in the CDC energy-saving model by 9.30% compared with the Elman algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm performs the best in task scheduling of the cloud data center, and the average response time of the DDPG algorithm is 141. In contrast, the Deep Q Network algorithm performs poorly. This paper proves that Deep Reinforcement Learning (DRL) and neural networks can reduce the energy consumption of CDC and improve the completion time of CDC tasks, offering a research reference for CDC resource scheduling.

PETS: P2P Energy Trading Scheduling Scheme for Electric Vehicles in Smart Grid Systems

Due to the lack of improper access control policies and decentralized access controllers, security and privacy-aware peer-to-peer (P2P) energy trading among electric vehicles (EVs) and the smart grid is challenging. Most of the solutions reported in the literature for P2P energy trading are based upon centralized controllers having various security flaws resulting in their limited applicabilities in real-world scenarios. To handle these issues, in this paper, we propose a P2P energy trading scheduling scheme called as P2P Energy Trading Scheduling <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(PETS)</i> using blockchain technology. PETS is based on real-time energy consumption monitoring for balancing the energy gap between service providers (SPs), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> smart grids and service consumers, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> EVs. In PETS, the Stackelberg game theory-based 1-leader multiple-followers scheme is proposed to depict the interactions between EVs and the SP. The selection of the leader among all SPs is made using a second-price reverse auction. As per the announced energy price by the leader, EVs manage energy consumption by minimizing their energy bills. In PETS, on the leader’s side, we propose the Genetic algorithm to maximize its profit. In contrast, on the followers’ side, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , EVs, we use the Stackelberg Equilibrium to minimize their energy bills. Simulation results demonstrate that the proposed PETS scheme outperforms the existing state-of-the-art schemes using various performance evaluation metrics. Specifically, it reduces the peak-to-average ratio (PAR) by 12.5% of EVs’ energy load in comparison to the existing state-of-the-art scheme.

Design and implementation of prepaid power billing system in smart grid environment

&lt;span&gt;This paper presents the real-time monitoring of energy consumption utilizing the global system for mobile communication (GSM) via wireless protocols. The power supply company has the authority to change or alter the tariff rates. The method of payment is prepaid, so there will be options for the customer to request the number of units assigned to the particular meter against the money. The customer can monitor the number of units consumed with the help of GSM. If the units are ten percent of the total units, then the system will give a warning message so that the customer can recharge it before cutting off the supply. A GSM-based wireless module has remote access to the usage of electricity. Simulation is done on Proteus while the hardware is implemented using the microcontroller Arduino Uno.&lt;/span&gt;

Real-time monitoring of energy consumption of high-rise residential construction based on BIM building model

In order to overcome the low accuracy of high-rise residential construction energy consumption monitoring, this paper proposes a new BIM building model-based real-time monitoring method of high-rise residential construction energy consumption. This method first analyses the BIM building model; then uses BIM related application software to build a three-dimensional building information model; and then calculates the energy consumption of the construction life cycle, including the preparation stage, the construction maintenance stage, the operation stage and the demolition disposal stage. Finally, the monitoring performance is compared and analysed through an example. The experimental results show that compared with the traditional high-rise residential construction energy consumption monitoring method, the error between the monitoring results of high-rise residential construction energy consumption and the actual energy consumption value is less, 377.94 (kW · h), which proves that the monitoring results based on BIM are more accurate.

Real-Time Monitoring Of Energy Consumption Of High-Rise Residential Construction Based On Bim Building Model

In order to overcome the low accuracy of high-rise residential construction energy consumption monitoring, this paper proposes a new BIM building model-based real-time monitoring method of high-rise residential construction energy consumption. This method first analyses the BIM building model; then uses BIM related application software to build a three-dimensional building information model; and then calculates the energy consumption of the construction life cycle, including the preparation stage, the construction maintenance stage, the operation stage and the demolition disposal stage. Finally, the monitoring performance is compared and analysed through an example. The experimental results show that compared with the traditional high-rise residential construction energy consumption monitoring method, the error between the monitoring results of high-rise residential construction energy consumption and the actual energy consumption value is less, 377.94 (kW · h), which proves that the monitoring results based on BIM are more accurate.

Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid

It is expected that peer to peer energy trading will constitute a significant share of research in upcoming generation power systems due to the rising demand of energy in smart microgrids. However, the on-demand use of energy is considered a big challenge to achieve the optimal cost for households. This paper proposes a blockchain-based predictive energy trading platform to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources. The proposed blockchain-based platform consists of two modules; blockchain-based energy trading and smart contract enabled predictive analytics modules. The blockchain module allows peers with real-time energy consumption monitoring, easy energy trading control, reward model, and unchangeable energy trading transaction logs. The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy consumption. This paper uses real energy consumption data acquired from the Jeju province energy department, the Republic of Korea. This study aims to achieve optimal power flow and energy crowdsourcing, supporting energy trading among the consumer and prosumer. Energy trading is based on day-ahead, real-time control, and scheduling of distributed energy resources to meet the smart grid’s load demand. Moreover, we use data mining techniques to perform time-series analysis to extract and analyze underlying patterns from the historical energy consumption data. The time-series analysis supports energy management to devise better future decisions to plan and manage energy resources effectively. To evaluate the proposed predictive model’s performance, we have used several statistical measures, such as mean square error and root mean square error on various machine learning models, namely recurrent neural networks and alike. Moreover, we also evaluate the blockchain platform’s effectiveness through hyperledger calliper in terms of latency, throughput, and resource utilization. Based on the experimental results, the proposed model is effectively used for energy crowdsourcing between the prosumer and consumer to attain service quality.

Intelligent Monitoring System for Thermal Energy Consumption of Buildings under the IoT Technology

In view of the poor accuracy of single value of heat energy consumption and the weak real-time monitoring of energy consumption in buildings, an intelligent monitoring system for thermal energy consumption of buildings under the internet of things (IoT) technology is designed. The overall structure of the intelligent monitoring system for thermal energy consumption of buildings is constructed. The DSP integrated signal processor is used for data acquisition and real-time information processing of thermal energy of buildings. A wireless intelligent gateway for building thermal energy consumption monitoring is designed by using internet of things technology, a wireless sensor network model is constructed. The VME bus is used as information transmission channel to realise intelligent monitoring for thermal energy consumption. The test results show that the real-time monitoring accuracy of the system is better than that of the traditional method and it has a good application prospect in all aspects.

Bluetooth Based Home Control and Real-Time Energy Consumption Monitoring System through Smartphone

Energy consumption is one of the most important, for it is necessary to control the use of home lighting as needed. Monitoring is important, in order to know the quality of electricity supply in the system, identifying disruption events, and to calculate the amount of electrical energy consumption periodically. To achieve the objectives mentioned above, two schemes for home control and monitoring the electrical quantities in real-time have been developed. The first scheme presents Bluetooth based home control. It uses an HC-05 Bluetooth module and Bluetooth Controller mobile application for switching on / off the appliances. Relay and LEDs are used as loads to demonstrate the working of the system. This system is based on Arduino Uno microcontroller board. Arduino Integrated Development Environment (IDE) is used for developing the necessary software. The second scheme uses the current sensor ACS712-20A for monitoring of voltage, current, and energy consumption. The measurement results include voltage, current, power, and the amount of energy consumption that is displayed by LCD 16*2.

Managing university landscape and infrastructure towards green and sustainable campus

Landscape and infrastructure are two main basic aspects which play a significant role for any universities in achieving environmental, social-behavioural, or aesthetic outcomes. This paper describes the setting and infrastructure management at the Universitas Indonesia (UI) as a continuous effort towards green and sustainable campus. Some developments and improvements have been made during the last three years, such as forest city maintenance, development of pedestrian boulevard, and installation of road marks and traffics signs. In addition, infrastructures have been improved by replacing conventional lamps with LED type ones, development of real-time energy consumption monitoring system, green chilling system, solar photo-voltaic development, solar cooling absorption chiller system, air conditioning with VRF/VRV system, and preparation for green certified building. These improvements on campus setting and landscape are reflected on the UI GreenMetric 2017 results which ranked at #23 worldwide with the score of 957 out of 1500 total score for “setting and infrastructure” category. With regards to these achievements, some challenges and strong commitment in sustainability policy need to be implemented in the forthcoming years to maintain the UI’s vision to be a “world class sustainable university”.

Simulation-based machine shop operations scheduling system for energy cost reduction

Owing to the ever increasing requirements in sustainability, manufacturing firms are trying to reduce their energy consumption and cost. In this paper, we propose a simulation-based machine shop operations scheduling system for minimizing the energy cost without sacrificing the productivity. The proposed system consists of two major functions: (1) real-time energy consumption monitoring (through power meters, a database server, and mobile applications) and (2) simulation-based machine shop operations scheduling (through a machine shop operations simulator). First, the real-time energy consumption monitoring function is developed to collect energy consumption data and provide real-time energy consumption status monitoring/electrical load abnormality warnings. Second, the simulation-based machine shop operations scheduling function is devised to estimate the energy consumptions and cost of CNC machines. In addition, an additive regression algorithm is developed to formulate energy consumption models for each individual machine as simulation inputs. The proposed system is implemented at a manufacturing company located in Tucson, Arizona state of USA. The experiment results reveal the effectiveness of the proposed system in achieving energy cost savings without sacrificing the productivity under various scenarios of machine shop operations.

Multi-level awareness of energy used in production processes

To ensure green manufacturing, the energy consumption of production processes should be transparent and minimized. Also, to achieve the desired level of energy consumption awareness and efficiency improvements, energy use should be measured in more detail and linked to production data. In this scenario, real-time monitoring of energy consumption represents an essential step to increasing energy awareness, efficiency and the support of energy-aware production processes. This paper seeks to provide a way to achieve multi-level awareness of the energy used during production processes. The multi-level awareness of energy consumption means identifying the amount of energy used, CO2 emitted, and the cost of the energy used at operation, product, and order level. This multi-level awareness is achieved by integrating energy usage data with production data at the operational level. Furthermore, energy sources need to be considered to define the amount of CO2 that is emitted from the production process for each product. A pilot study was carried out to integrate electrical energy data, production data and scheduling data in real time to achieve the multi-level awareness of energy used in production. The results show that integrating energy with production data enables factories to provide specific energy consumption information for decision makers at the factory level, as well as for the consumers and the regulators. This integration of energy and production data is achieved efficiently when there is a high level of standardization of production processes and the availability of detailed energy usage data.

Design and Implementation of Monitoring System for Building Energy Consumption

This paper analyzes the shortcomings of ECOTECT simulation software, puts forward the real-time energy consumption monitoring software. According to the structural characteristics of large public buildings, the paper proposes the key technology development and the functions of the software platform, and provides RS485 communication code. The research results in this paper have some practical value on energy consumption of large-scale construction monitoring.

Energy operation management for Smart city using 3D building energy information modeling

During the last decade, global interest according to energy efficiency and climate change has dramatically increased. In particular, the building sector plays an important role, as it accounts for significant percentages of total energy consumption. So numerous high energy efficiency devices have been developed and applied to building. But it is also important that such high energy efficiency equipment is operated through efficient management. In this paper, through establishing a Building Energy Management System (BEMS) in the test-beds, the energy consumption and environmental data are measured and gathered at a total operation center. The proposed energy saving technologies would integrate real-time energy consumption monitoring with Building Energy Information Modeling (BEIM). To find an optimal energy-saving method, a building energy simulation was used with Genetic Algorithms. The Heating Ventilating and Air-Conditioning (HVAC) control schedules were sought. The energy-saving effect of this method is about 9.44%. HVAC control schedules can be applied to building, using a function to control facilities in buildings. This energy-saving effect will be added to the general saving effects for BEMS. It is expected that the proposed energy optimization tool using BEIM will be applied to the micro-energy grid system for smart city in the near future.

Energy Management System for Domestic Electrical Appliances

A variety of energy management systems are currently available for domestic domain, and many are concerned with real-time energy consumption monitoring and display of statistical and real time data of energy consumption. Although these systems play a crucial role in providing a detailed picture of energy consumption in home environment and contribute to influencing energy consumption behavior, households are required to then take appropriate measures to reduce energy consumption. Some energy management systems provide energy saving tips but they do not take into account households’ profiles and energy consumption of home appliances. To generate an effective and real time appliance level advice on energy consumption, the system must be able to cope with a large volume of data. The proposed system addresses this issue by taking into account household profiles and energy consumption of domestic electrical appliances. The system also uses an approach based on functional data services to deal with the challenge of processing a large volume of data in real time.