A Smart Home Energy Monitoring System Based on Internet of Things and Inter Planetary File System for Secure Data Sharing

Energy demand will continue to rise as a result of predicted population growth. In this work, a user-friendly home energy monitoring system based on IoT is described, which is capable of collecting, analyzing, and displaying data. Users register their sensors and devices on the monitoring platform. PostgreSQL and Elasticsearch databases are used to store the resulting measurements. In a smart home, the wireless sensor ACS712 was used to monitor the flow of electricity (current and voltage) for a household device. The user can share data about electricity consumption and costs with a third party via the private IPFS (InterPlanetary File System) network. A third party can download all the energy consumption data for a device or many devices from the platform for 1 day, 3 months, 6 months, and 1 year. The studies on the development of energy-efficient technology for home devices benefit greatly from the gathered data. For security in the system, it is preferred to run Keyrock Idm, Wilma Pep Proxy, and Orion Context Broker in HTTPS mode, and MQTTS is used to retrieve sensor data. The experimental results showed that the energy monitoring system accurately records voltage, current, active power, and the total amount of power used and offers low-cost solutions to the users using household devices in a day.

Development of Wi-Fi Based Home Energy Monitoring System for Green Internet of Things

Green Internet of things （loT） has been heralded as the ＂next big thing＂ waiting to be realized in energy-efficient ubiquitous computing. Green IoT revolves around increased machine-to-machine communications and encompasses energy-efficient wireless embedded sensors and actuators that assist in monitoring and controlling home appliances. Energy efficiency in home applications can be achieved by better monitoring of the specific energy consumption by the appliances. There are many wireless standards that can be adopted for the design of such embedded devices in loT. These communication technologies cater to different requirements and are classified as the short-range and long-range ones. To select the best communication method, this paper surveys various loT communication technologies and discusses the advantages and disadvantages to develop an energy monitoring system. An IoT device based on the Wi-Fi technology system is developed and tested for usage in the home energy monitoring environment. The performance of this system is then evaluated by the measurement of power consumption metrics. In the efficient deep-sleep mode, the system saves up to 0.3 W per cycle with an average power dissipation of less than 0.1 W/s.

Research on Smart Energy Monitoring and Management System Based on Digital Twin Technology

Smart energy monitoring and management system lays a foundation for the application and development of smart energy. However, in recent years, the work efficiency of smart energy development enterprises has generally been low, and there is an urgent need to improve the application efficiency, resilience and sustainability of smart energy monitoring and management system. Digital twin technology provides a data-centric solution to improve smart energy monitoring and management system, bringing an opportunity to transform passive infrastructure assets into data-centric systems. This paper expounds on the concept and key technologies of digital twin, and designs a smart energy monitoring and management system based on digital twin technology, which has dual significance for promoting the development of smart energy field and promoting the application of digital twin.

Energy evolution and structural health monitoring of coal under different failure modes:An experimental study

Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.

Wireless Self-Powered Vibration Sensor System for Intelligent Spindle Monitoring

In recent years,high-end equipment is widely used in industry and the accuracy requirements of the equipment have been risen year by year.During the machining process,the high-end equipment failure may have a great impact on the product quality.It is necessary to monitor the status of equipment and to predict fault diagnosis.At present,most of the condition monitoring devices for mechanical equipment have problems of large size,low precision and low energy utilization.A wireless self-powered intelligent spindle vibration acceleration sensor system based on piezoelectric energy harvesting is proposed.Based on rotor sensing technology,a sensor is made to mount on the tool holder and build the related circuit.Firstly,the energy management module collects the mechanical energy in the environment and converts the piezoelectric vibration energy into electric energy to provide 3.3 Vfor the subsequent circuit.The lithium battery supplies the system with additional power and monitors’the power of the energy storage circuit in real-time.Secondly,a three-axis acceleration sensor is used to collect,analyze and filter a series of signal processing operations of the vibration signal in the environment.The signal is sent to the upper computer by wireless transmission.The host computer outputs the corresponding X,Y,and Z channel waveforms and data under the condition of the spindle speed of 50∼2500 r/min with real-time monitoring.The KEIL5 platform is used to develop the system software.The small-size piezoelectric vibration sensor with high-speed,high-energy utilization,high accuracy,and easy installation is used for spindle monitoring.The experiment results show that the sensor system is available and practical.

RF energy harvesting system for wireless intraocular pressure monitoring

This paper presents an RF energy harvesting system for wireless intraocular pressure monitoring applications.The system consists of an implantable antenna and a rectifier.A new sizing strategy is adopted to optimize the conversion efficiency of the rectifier,and the design principle of an implantable antenna is introduced from material selection and structure design.Results from testing demonstrate that the antenna gain is about-20 dBi and the rectifier's maximum total conversion efficiency which contains match efficiency and rectifying efficiency is 47.18%under the implementation of0.18μm standard CMOS process.The maximum power obtained from the proposed system is 8μW when the power density of electromagnetic wave is lower than the national standard 40μW/cm^2 at915 MHz,which is enough to power the intraocular pressure monitoring system.

Design and Implementation of Monitoring System for Buildin~ 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.

Development of OIST Wave Energy Converter Monitoring System for Maldives Island Experiment

This paper describes experiments with our self-built Wave Energy Convertor(WEC)monitoring system in the Maldives and demonstrates how we developed a horizontal-axis type,half-scale,wave energy converter(WEC)that generates electricity in the coastal breaking wave zone.In order to measure this power generator and turbine’s efficiency,voltage and current were measured by pulling the generator with a 35 cm diameter turbine(half scale).We obtained data showing 400 W peak power in water speed of 3.1 m/s.Consequently,we assembled two sets of WEC,placed them near the shoreline on Kandooma Island in the Maldives in May 2018,and measured the wave energy at the breaking wave zone.A monitoring system was set up in one rack for the two sets of WECs,connected simultaneously.Two outputs of the generators were rectified and connected to power resistors and internal LED displays.The outputs could also be switched to connect to 24 electric double layer capacitors(EDLC),in order to perform a continuous lighting test of external high-power LED lights.The wave power data were continuously saved by an automated data logger and could be transferred from the installation site,to Japan via the Internet.The wave power was measured on Kandooma Island in the Maldives for a long period,and is still ongoing.Examples of the obtained data are shown in this paper.

Advances in self-powered sports monitoring sensors based on triboelectric nanogenerators

The new era of the internet of things brings great opportunities to the field of intelligent sports.The collection and analysis of sports data are becoming more intelligent driven by the widely-distributed sensing network system.Triboelectric nanogenerators(TENGs)can collect and convert energy as selfpowered sensors,overcoming the limitations of external power supply,frequent power replacement and high-cost maintenance.Herein,we introduce the working modes and principles of TENGs,and then summarize the recent advances in self-powered sports monitoring sensors driven by TENGs in sports equipment facilities,wearable equipment and competitive sports specialities.We discuss the existing issues,i.e.,device stability,material sustainability,device design rationality,textile TENG cleanability,sports sensors safety,kinds and manufacturing of sports sensors,and data collection comprehensiveness,and finally,propose the countermeasures.This work has practical significance to the current TENG applications in sports monitoring,and TENG-based sensing technology will have a broad prospect in the field of intelligent sports in the future.

Energy model based sensorless estimation method for operational temperature of braking resistor onboard metro vehicles

Purpose–This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor(BR)onboard the vehicle,which overcomes the vulnerability of having conventional temperature sensor.Design/methodology/approach–In this study,the energy model based sensorless estimation method is developed.By analyzing the structure and the convection dissipation process of the BR onboard the vehicle,the energy-based operational temperature model of the BR and its cooling domain is established.By adopting Newton’s law of cooling and the law of conservation of energy,the energy and temperature dynamic of the BR can be stated.To minimize the use of all kinds of sensors(including both thermal and electrical),a novel regenerative braking power calculation method is proposed,which involves only the voltage of DC traction network and the duty cycle of the chopping circuit;both of them are available for the traction control unit(TCU)of the vehicle.By utilizing a real-time iterative calculation and updating the parameter of the energy model,the operational temperature of the BR can be obtained and monitored in a sensorless manner.Findings–In this study,a sensorless estimation/monitoring method of the operational temperature of BR is proposed.The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR,instead of adding dedicated thermal sensors.The results also validate the effectiveness of the proposal is acceptable for the engineering practical.Originality/value–The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks.The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

Prospect of energy management system for large steel enterprise

The energy management system(EMS),which acts as the heart of the energy management center of a steel enterprise,is a large computer system focused on the concentrative monitor and control of the production and utilization of energy.Although Chinese steel industry was well developed in the latest decade, so far the levels of the comprehensive energy consumption per ton steel among Chinese steel enterprises are remarkably distinct,and the average value of the comprehensive energy consumption per ton steel of them has still been much higher than the value of those in developed countries.This bad situation,in the opinion of the author,partially results from the poor ability for most Chinese steel enterprises to manage the production and utilization of energy.National policies associated to energy-saving and ejection-decreasing call for steel enterprises to build the EMS;and more and more steel enterprises themselves also desire to achieve EMS projects so that they can optimize their energy production and utilization.Baosteel,the largest and most advanced steel enterprise in China,has got plenty of experience in the EMS due to its incessant practice for more than 30 years in the design,construction,application,and revampment of its EMS.In the present article,the features of an advanced EMS is described and discussed based on the design practice of the EMS of Baosteel Zhanjiang Project.An advanced EMS should be an optimized and integrated system,which possesses of the characteristic of high managing efficiency,enough openness in expansion,friendly interfaces, and simple structure.Furthermore,it could support many-sided applications,e.g.,energy related data mineing,energy network combination and co-supply,application of geographic information technology,and other technical researched on energy-saving aspects.It is known that some energy-related indexes of Baosteel have stood on a high level better than those of some worldwide famous steel enterprises.Moreover,it goes without saying that the indexes of Baosteel Zhanjiang will be better than those of present Baosteel.Therefore, one can easily expect that the new EMS of Baosteel Zhanjiang will be much more advanced,which will be more helpful to fulfil systematiclly saving of energy,to elevate the efficiency of energy utilization,to lower the comprehensive energy consumption per ton steel.

BAS-BP控制模型在化工厂空调节能中的应用研究

实施能源控制,降低化工材料企业能源消耗对企业实施转型升级,提升企业市场竞争力具有至关重要的意义。对化工企业厂房空调系统进行了分析,将空调系统能效比作为节能智能控制的目标。采用BAS对BP神经网络初始权值与偏置进行优化,得到了BAS-BP模型,通过该模型对厂房空调系统实施节能智能控制。将该节能智能控制模型应用于某化工材料企业中,通过520次迭代,厂房内温度区域稳定,为24.5℃,同时得到了各影响因素的值。这对化工材料企业厂房空调节能智能控制提供了数据支撑。

Dptimizing Thermal Energy Storage Systems in the Hospitality Industry

Building energy analyses using forecasting optimization strategies are commonly used for predicting TES （thermal energy storage） system performance. These strategies produce perfect optimized cost savings and are not typically realized in the real world, unless a safety factor is applied. Rather than show how to improve the industry＇s ability to accurately model and simulate a true TES system design, this paper will show advanced building information strategies and energy management simulation techniques required to truly achieve the ideal optimized cost savings, determined from the TES energy simulation analysis. This paper uses the hospitality industry as a case study, showing the application of simulation and analytical modeling for an optimized partial TES system. As a result building energy managers can make better decisions through the entire building life cycle from the earliest concept model through operation and maintenance.

Monitoring of Large Size Solar Thermal Systems： Design and Data Analysis

This paper describes how to achieve an efficient design and management of a tele-monitoring system of several solar thermal plants. The system will be able to make an analysis that assures a more efficient management of each plant and of the whole system. In the first part of this study, the features of the monitoring system that allows to monitor the operating parameters and to discover the issues before they actually become dangerous for the plant have been identified. The data collected in the different solar thermal systems realized in Italian jails have been analyzed. The results of these elaborations allowed us both to find out some anomalies of functioning of the plants, and to optimize the management of the whole plant in a more efficient way.

基于BAS-IMOPSO算法的风电系统储能优化配置

风力发电作为一种清洁的可再生能源,在电力系统中应用广泛,但风电出力的随机波动性会对电力系统电能质量产生不利影响。储能系统(energy storage system,ESS)因其灵活的双向调节能力被用于平抑风电接入系统带来的电压与功率波动问题。文中考虑储能接入节点与容量不同对风电出力波动的平抑效果不同,以系统电压偏差、日有功网损和ESS配置容量为目标函数建立多目标储能优化配置模型。采用一种基于天牛须搜索算法的改进多目标粒子群优化(beetle antennae search-improved multi-objective particle swarm optimization,BAS-IMOPSO)算法求解该模型,利用基于信息熵的逼近理想解排序法在Pareto解集中选取最佳储能配置方案。在IEEE 33节点系统中对模型进行仿真验证,结果表明:BAS-IMOPSO算法优化下的ESS降低配电网电压偏差与有功网损的能力较多目标粒子群优化(multi-objective particle swarm optimization,MOPSO)算法显著提高,有效改善了系统的电能质量,具有削峰填谷的作用,验证了BAS-IMOPSO算法的有效性。

Simulation study of energy resolution with changing pixel size for radon monitor based on Topmetal-Ⅱ^- TPC

In this paper, we study how pixel size influences energy resolution for a proposed pixelated detector—a high sensitivity, low cost, and real-time radon monitor based on a Topmetal-Ⅱ^- time projection chamber(TPC). This monitor was designed to improve spatial resolution for detecting radon alpha particles using Topmetal-Ⅱ^- sensors assembled by a 0.35 lm CMOS integrated circuit process.Owing to concerns that small pixel size might have the side effect of worsening energy resolution due to lower signalto-noise ratio, a Geant4-based simulation was used to investigate the dependence of energy resolution on pixel sizes ranging from 60 to 600 lm. A non-monotonic trend in this region shows the combined effect of pixel size and threshold on pixels, analyzed by introducing an empirical expression. Pixel noise contributes 50 keV full-width at half-maximum energy resolution for 400 lm pixel size at 1–4σ threshold that is comparable to the energy resolution caused by energy fluctuations in the TPC ionization process( ~20 keV). The total energy resolution after combining both factors is estimated to be 54 keV for a pixel size of 400 lm at 1–4σ threshold. The analysis presented in this paper would help choosing suitable pixel size for future pixelated detectors.

Assessment of thermal behaviour of thermo-active diaphragm walls based on monitoring data

Thermo-active diaphragm walls have proved their effectiveness in the thermal conditioning of buildings and infrastructures. However, some aspects still need to be investigated in order to tailor methods and tools for an accurate prediction of their energy and structural performance. In this perspective, some issues are addressed that concern the definition of models for the numerical analysis, in particular issues about the modelling of geometry and thermal boundary conditions. Taking advantage of a monitoring programme on a real full-scale structure, this research focuses on the assessment of heat transfer process and thermal response of diaphragm wall and soil mass on the basis of field data. Understanding of the heat transfer process contributes to the definition of the time-dependent thermal boundary conditions at the excavation side. From the analysis of thermal gradients in the wall, the condition at the excavation side is recognised as a major factor that influences the heat transfer process, governing the direction of the heat flux in different seasons of operation of the geothermal system.

IoT-enabled energy efficiency monitoring and analysis method for energy saving in sheet metal forming workshop

Sheet metal forming,as a typical energy-intensive process,consumes massive energy.Due to the significant difference between sheet metal forming and machining,manufacturers still lack an effective method to monitor and analyze the energy efficiency in the sheet metal forming workshop.To this end,an energy efficiency monitoring and analysis(EEMA)method,which is supported by Internet of Things(IoT),is proposed.The characteristics in a forming workshop are first analyzed,and then the architecture of the method is expatiated-detailedly.Energy efficiency indicators at machine level,process level,and workshop level are defined,respectively.Finally,a sheet metal forming workshop for the deformation of panels of forklift was investigated to validate the effectiveness and benefits of the proposed method.With the application of the IoT-enabled method,various energy-saving decisions can be made by the management of the enterprises for energy efficiency improvement and energy consumption reduction(EEIECR)in the sheet metal forming workshop.

Research on remote hydro-generator sets diagnosis system

A type of remote monitoring and diagnosis system is brought forward which based on Matlab Web Server.Firstly,wavelet packet decomposition is introduced to acquire energy features of which reflect hydrogenerator sets performance to be Feature Parameter.Then these Feature Parameters can be adopted as BP Neural Network input variable to realize fault diagnosis.Most of all,it is the first time to adopt Matlab Web Server to hydro-generator sets faults diagnosis field to implement distributed remote monitoring and diagnosis system.Therefore,remote diagnosis application is independent from the OS used on server side.There is no need for software maintenance by clients.And clients can finish remote diagnosis by Web Browser and without installation of Matlab-software.Client users can monitor and diagnose hydro-generator sets by Browser.Finally,further research work is pointed out such as hydro-generator sets fault modeling,accelerating BP Neural Network learning speed and convergence property,improving data transfer speed of Matlab Web Server to meet the needs of real-time diagnosis for hydropower generator sets.

Self-Powered,Long-Durable,and Highly Selective Oil-Solid Triboelectric Nanogenerator for Energy Harvesting and Intelligent Monitoring

Triboelectric nanogenerators(TENGs)have potential to achieve energy harvesting and condition monitoring of oils,the“lifeblood”of industry.However,oil absorption on the solid surfaces is a great challenge for oil-solid TENG(O-TENG).Here,oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties.The designed O-TENG can generate an excellent electricity(with a charge density of 9.1μC m^(−2) and a power density of 1.23 mW m^(−2)),which is an order of magnitude higher than other O-TENGs made from polytetrafluoroethylene and polyimide.It also has a significant durability(30,000 cycles)and can power a digital thermometer for self-powered sensor applications.Further,a superhigh-sensitivity O-TENG monitoring system is successfully developed for real-time detecting particle/water contaminants in oils.The O-TENG can detect particle contaminants at least down to 0.01 wt%and water contaminants down to 100 ppm,which are much better than previous online monitoring methods(particle>0.1 wt%;water>1000 ppm).More interesting,the developed O-TENG can also distinguish water from other contaminants,which means the developed O-TENG has a highly water-selective performance.This work provides an ideal strategy for enhancing the output and durability of TENGs for oil-solid contact and opens new intelligent pathways for oil-solid energy harvesting and oil condition monitoring.