Monitor Electricity Consumption Research Articles

Smart Energy Meter For Household Application

On the grounds that these day’s energy meters have many drawbacks like two way communication, actual time tracking, electricity tampered, and many others. To manage up with these dangers, this proposed work is used to monitor electricity consumption at home level. This allows in reducing electricity consumptions and monitors the gadgets ate up. The objective is to make the electrical appliances shrewd and offer consolation to client and to reduce energy intake in web packages. Plan and execution of the task is specifically founded on Arduino UNO controller and IoT generation. If any tampering happens the controller will send the information to the server .This adventure depicts the digitization of burden quality utilization readings over the net. The web site makes use of the THINGSPEAK analytics to analyze the strength utilization to present more targeted description and visualization of the energy utilization information. Wi-Fi unit performs IOT operation by means of sending electricity facts of the load to the webpage which may be accessed thru the channel identification of the tool. The above system in completed to every single home equipment to screen electricity ate up through that specific appliance, with the aid of doing this over consumption this is intake beyond specific limit can be without problems identified and tends to choose a suitable replacement for that unique equipment by means of other via another that consumes less energy or to pick out any fault in it keywords: Low power, electricity meter

Integrated piezoelectric direct current sensor with actuating and sensing elements applicable to two-wire dc appliances: theoretical considerations

An oscillating-type MEMS dc current sensor integrated with piezoelectric actuating and sensing elements was proposed to be utilized for monitoring electricity consumption by a one-wire or two-wire appliance cord. It enabled non-contact and constant measurement and could be applicable to two-wire appliances without using a cord separator. It was found experimentally that the relative change in the maximum value of the output voltage was approximately proportional to the applied dc current. Theoretical models are developed in this work to analyze the relationship between the relative change in the maximum value of the output voltage and the applied dc current. We find that as the applied dc current increases, the oscillating-type sensing system exhibits three response areas: a linear increase area, an abrupt increase area and a decrease area. A linear increase area is defined as the sensing area where the equations on the linear relationship and the sensitivity are derived. In addition, theoretical considerations are outlined on the effect of the displacement of the cantilever during the vibration on the nonlinear response, the restoring forces of the cantilever as the applied current increases, and the factors affecting sensitivity. Theoretical models can be utilized to predict the applicable magnetic forces and piezoelectric output voltage as well as guide the optimization of the sensor design.

Rancang Bangun Alat Kontrol dan Monitoring Konsumsi Listrik Lampu Penerangan Jalan Umum Berbasis Mikrokontroler ATMega 2560

Abstract—Public street lighting (LPJU) is a road infrastructure that is very important for road users at night. The constraints faced by User managers in an area in LPJU management are the problem of maintenance, monitoring, electricity consumption, and limited workforce. In an effort to overcome this problem, this study designed a tool in the LPJU system that can control LPJU ignition according to user needs by combining the timer and light sensor functions, and can monitor electricity consumption by calculating the power consumption or kWh meter and recording data on accumulation of electric power consumption every day at 06.00 WITA automatically stored on the micro SD Card. The accumulated data last recorded for 1 (one) month at the beginning of each month at 08.00 WITA will be sent using SMS services to users periodically. In addition, the user can monitor at any time with the "lapor" SMS format, the monitoring data sent in the form of the last electrical power consumption used by LPJU at that time.

Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate

Nowadays climate change problems have been more and more concerns and urgent in the real world. Especially, the energy power consumption monitoring is a considerate trend having positive effects in decreasing affecting climate change. Non-Intrusive Load Monitoring (NILM) is the best economic solution to solve the electrical consumption monitoring issue. NILM captures the electrical signals from the aggregate energy consumption, feature extraction from these signals and then learning and predicting the switch ON/OFF of appliances used these feature extracted. This paper proposed a NILM framework including data acquisition, data feature extraction, and classification model. The main contribution is to develop a new transient signal in a different aspect. The proposed transient signal is extracted from the active power signal in the low-frequency sampling rate. This transient signal is used to detect the event of household appliances. In household appliances event detection, we applied to Decision Tree and Long Short-Time Memory (LSTM) models. The average accuracies of these models achieved 92.64% and 96.85%, respectively. The computational and result experiments present the solution effectiveness for the accurate transient signal extraction in the electrical input signals.

Design of Electric Quantity Detection System Based On Iot

With the development of social economy, the electricity consumption of residents is increasing year by year. And the problem of electricity safety is still becoming more and more prominent. Especially in college dormitories, due to regulatory loopholes and students use prohibited electrical appliances and other reasons. There is a frequent occurrence of fire in the dormitory. This not only causes certain property losses to students, but also threatens the safety of students. If we can carry out the real-time network monitoring of the dormitory electricity consumption, we can cut off the power in the home in the early stage of the accident. To avoid a similar incident to a certain extent, this project aims at designing a student dormitory electricity monitoring system for this safety problem. By using the STM32 processor and the ESP8266 network communication module, the real-time monitoring of the electricity consumption in the dormitory is completed, so that the school supervision department and the students can grasp the electricity consumption of the dormitory in real time. The automatic alarm can be realized over the electricity limit.

FULL-TARGETED ASPECTS OF MONITORING OF ELECTRICITY LEVELS AT ENTERPRISES OF THE INDIVIDUAL DEPARTMENT OF THE KRIIVORIZHOGY REGION

Purpose. Conduct a cost-target analysis as part of monitoring electricity consumption levels and develop recommendations for a rational strategy for consuming electricity from mining enterprises with underground mining of iron ore.Methodology. The study is based on the use of laws, regularities and categorical apparatus. In the process of scientific research, general scientific methods of research (comparison, generalization, analogy method, structural analysis and synthesis), methods of logical-theoretical analysis, special economic-mathematical methods were used.Findings. The indicators of electricity consumption at iron ore enterprises of the Krivoy Rog region are analyzed. A comparative analysis of the planned and actual energy consumption is carried out. The trend components of electricity consumption indicators at iron ore enterprises of the Krivoy Rog region are highlighted. Dynamics of structural transformation of unit cost of electric energy production at iron ore enterprises of the Krivoy Rog region is determined.Originality. When forming the cost-target component of monitoring the energy consumption levels, it is suggested to use the index methodology in combination with trend models, which will allow to determine the forecast of electricity consumption and realize rational management of power consumption. An index system of structural transformation of the unit cost of electric energy production at mining enterprises of the Krivoy Rog region is proposed.Practical value. Dynamics analysis of the structural transformation of the unit cost of electric power consumption of iron ore enterprises of the Krivoy Rog region is carried out. The system of indices of the unit cost of electricity consumption is defined: the index of the average unit cost of electricity consumption of variable composition; index of unit cost of power consumption of fixed composition; the index of the average unit cost of power consumption of structural shifts. The deterministic component of the time series of electricity consumption for the iron ore enterprises of the Krivoy Rog region was singled out and analyzed. The obtained trend models give grounds for determining the forecast of electricity consumption levels with an appropriate level of reliability. The indicators of electricity consumption levels provide the formation of an information-analytical component of the relevant monitoring. The carried out researches allow to formulate the strategy of electricity consumption at the enterprises of iron ore industry of the Krivoy Rog region. The problems highlighted in the article are closely related to the scientific and practical tasks of rational use and distribution of available energy resources.

Research on Energy Conservation Management Platform of Smart Grid Park

The energy conservation management platform of the park includes five parts: contract energy management, electric power substitution, new energy enterprises, local trading market, real-time monitoring of electricity consumption and enterprise energy efficiency analysis. It can help the enterprises improve the degree of mastery of their own electricity consumption while it can also improve the equipment of enterprises, offer new energy to enterprises, provide the electricity consumption guidance for the enterprises from all aspects, build demand response model, guide users to use the electricity scientifically, reduce the enterprise cost of electricity use, reduce the energy consumption of the enterprise effectively, improve the efficiency of energy use, guarantee the enterprise equipment to be operated safely, reliably, economically, promote energy conservation technology, energy efficiency improving technology, renewable energy technology development and application, and finally, promote the sustainable development of social energy conservation and emissions reduction at the same time.

Методика выбора параметров противоаварийных команд на отключение потребителей в сетях 0,4 кВ

Conventionally, when accidents entailing active power imbalance occur in a power system, the underfrequency load shedding devices disconnect the necessary and sufficient amount of loads at the 10 kV voltage level. These load shedding operations are carried out without the possibility of taking into account the importance of certain consumers at the 0.4 kV voltage level. What is more, in view of using loads highly sensitive to power supply continuity, recent years have seen more stringent requirements being imposed on the reliability of their power supply. On the other hand, the availability of new computerized facilities opens the possibility of improving the reliability of supplying power to them even in the emergency load limitation mode. Specifically, this can be achieved by integrating the existing relay protection and automatic control systems with the automated electricity consumption monitoring and control system in such a way that consumers at the 0.4 kV level are selectively disconnected by commands of the underfrequency shedding control devices installed at the 10 kV voltage level. The article considers issues concerned with elaborating a procedure for selecting the values of the information parameter specifying the duration of command signals for disconnecting 0.4 kV loads produced by the automatic underfrequency load shedding devices at the 10 kV voltage level. It is proposed to perform load shedding operations by using a new method developed at the Moscow Power Engineering Institute National Research University. According to the proposed method, loads operating at the 0.4 kV voltage level are selectively disconnected through artificially creating a voltage sag (interruption) and using it as the underfrequency shedding command signal for disconnecting less critical loads. The article presents the principle of tuning-out from possible events and phenomena in the power supply network internal and external parts taking into account the existing setpoints of the relay protection and automatic control devices, and the influence of motor loads in generating and transmitting the command signal for short-term interruption of voltage via the 10 and 0.4 kV power network. A procedure for selecting the timing tuning parameters of the emergency control command generating and receiving devices.

An intelligent power distribution service architecture using cloud computing and deep learning techniques

Smart management of power consumption for green living is important for sustainable development. Existing approaches could not provide a complete solution for both smart monitoring of electricity consumption, and also intelligent processing of the collected data effectively. This paper presents a cloud-based intelligent power distribution service architecture, where an intelligent electricity box (IEB) is designed using Zigbee and Raspberry Pi, and a standard MQTT (Message Queuing Telemetry Transport) protocol is used to transfer monitored data to the backend Cloud computing infrastructure using open source software packages. The IEB provides cloud services of real-time electricity information checking, power consumption monitoring, and remote control of switches. The current and historical data are stored in HBase and analyzed using Long Short Term Memory (LSTM). Evaluations and practical usage show that our proposed solution is very efficient in terms of availability, performance, and the deep learning based approach has better prediction accuracy than that of both classical SVR based approach and the latest XGBoost approach.

Modelling of household electricity consumption with the aid of computational intelligence methods

ABSTRACTThe installation of smart meters for electricity consumption monitoring is common practice in many countries. Such meters usually provide information for the temporal variation of electricity consumption-related parameters, at an aggregated (household) level. In some cases, such meters may monitor individual appliances, or appliance groups installed in household departments. In the current study, a Computational Intelligence approach is used to analyse and model appliance group electricity consumption and to investigate the best possible computational approaches for improving consumption model performance. For this purpose, meta-features are used, a new feature prioritization method is introduced and a set of selected algorithms is employed. Results indicate an improvement in modelling capacity and an ability to construct models that effectively perform partial electricity consumption disaggregation. Overall, such methods may be used for the support of household electricity consumption modelling and for related demand management.

Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature.

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM's issues and improve the performance of load identification.

Post-occupancy Energy Consumption of BASIX Affected Dwellings in the Sydney Metropolitan Area

Australian residential sector contributes approximately 13 per cent of the total greenhouse gas (GHG) emissions. The New South Wales (NSW) government in Australia has introduced mandatory energy efficiency/ GHG emissions reduction target through the Building Sustainability Index (BASIX) assessment tool, which estimates the operational energy consumption and GHG emissions from new residential developments based on information available at the building design stage. However, post-occupancy energy consumption can be different from the estimated figures at the design stage. This research aims to disentangle the relationships between various attributes that influence energy consumption at the dwelling scale through a post-occupancy residential energy performance analysis model. The model combines multidimensional attributes which include building design features, construction quality, possession of household appliances and their usage, demographic and homeowners’ energy use behaviour. The model allows comparing the estimated energy consumption/ GHG emissions from BASIX assessment with actual energy consumption/ GHG emissions in the real household environment. Data for this study has been collected from a sample of 48 BASIX affected dwellings located in the greater Sydney area which includes energy bill data, real-time monitoring of electricity consumption and indoor environment (temperature and humidity) data. This paper reports findings based on the data collected from late 2015 to mid-2016 from 16 dwellings. The initial results demonstrate discrepancies in energy consumption (therefore GHG emissions), household occupancy and breakdown of energy consumption for various services between BASIX estimates and the collected data. Given that BASIX assessment is based on information available at the design stage, the reported discrepancies are expected. More importantly, the initial results show that BASIX assessments under-estimated energy consumption for active cooling and over-estimated consumption for lighting and ventilation and other household plug loads.

Dilemmas of Robust Analysis of Economic Data Streams*

Data streams (streaming data) consist of continuously observed, non-equally spaced and temporally evolving multidimensional data sequences that challenge our computational and/or inferential capabilities. In economics, data streams are among others related to electricity consumption monitoring, Internet user behavior in exploring, or order book forecasting in high-frequency financial markets. In this paper, we point out and discuss several open problems related to robust data stream analysis and propose three robust and conceptually very simple approaches in this context. We apply the proposals to real data sets related to the activity of investors in the futures contracts market.

Repeating patterns as symbols for long time series representation

A symbolic time series representation using repeating shapes as symbols is proposed.Lower bounding similarity measure properties evaluated on URC datasets.Electricity consumption dataset used to evaluate on very long, seasonal time series.Reconstruction error and dimensionality reduction ability comparable to PAA. Over the past years, many representations for time series were proposed with the main purpose of dimensionality reduction and as a support for various algorithms in the domain of time series data processing. However, most of the transformation algorithms are not directly applicable on streams of data but only on static collections of the data as they are iterative in their nature. In this work we propose a symbolic representation of time series along with a method for transformation of time series data into the proposed representation. As one of the basic requirements for applicable representation is the distance measure which would accurately reflect the true shape of the data, we propose a distance measure operating on the proposed representation and lower bounding the Euclidean distance on the original data. We evaluate properties of the proposed representation and the distance measure on the UCR collection of datasets. As we focus on stream data processing, we evaluate the properties and limitations of the proposed representation on very long time series from the domain of electricity consumption monitoring, simulating the processing of potentially unbound data stream.

Highlights on energy efficiency improvements: a case of a small brewery

The aim of the study is to provide a deep analysis of the potential impact of energy efficiency improvements by focussing on the evaluation of energy consumption and efficiency in various sub-departments at a small brewery – the locus of our research. To thoroughly analyze the specific energy consumption, an analysis of the historical energy consumption data, as well as an electricity consumption monitoring was carried out. The acquired knowledge significantly adds to the existing body of research, particularly by providing detailed data on energy consumption by different types of packaging equipment and through an in-depth analysis of energy efficiency in brewing and, uniquely, by analyzing the barriers to implementation of energy efficiency measures over time. The analysis of the brewery's energy consumption, shows that it significantly exceeds recommended benchmarks, so an in-depth analysis of heat losses in the brew house and energy monitoring in the packaging department were performed to identify the source of the inefficiency and the potential improvements. The aim of the electricity monitoring was also to acquire detailed consumption data to enhance company's stakeholders' awareness and scope for decisions regarding energy efficiency. The results of electricity monitoring show large variation of specific energy consumption for various types of packaging, with much lower specific consumption for packaging in metal barrels than previously reported in literature. As separate measurements were performed for subsections of packaging lines, it is possible to identify causes for under-implementation of energy efficiency and to propose specific improvements for each of the subsections. In addition to technological aspect, also the energy efficiency barriers were considered. Building on the typical energy efficiency barrier studies, a distinctive approach to analyze the barrier dynamics over time is applied. It is concluded that in the case brewery the main perceived barriers have not changed over five-year period, even though some successful energy efficiency interventions have been implemented. The provided results from electricity monitoring in the packaging department of brewery may be further applied for development of individual process, industry and national benchmarks.

Towards Dynamic Demand Response On Efficient Consumer Grouping Algorithmics

The widespread monitoring of electricity consumption due to increasingly pervasive deployment of networked sensors in urban environments has resulted in an unprecedentedly large volume of data being collected. Particularly, with the emerging Smart Grid technologies becoming more ubiquitous, real-time and online analytics for discovering the underlying structure of increasing-dimensional (w.r.t. time) consumer time series data are crucial to convert the massive amount of fine-grained energy information gathered from residential smart meters into appropriate demand response (DR) insights. In this paper we propose READER and OPTIC, that are real-time and online algorithmic pre-processing frameworks respectively, for effective DR in the Smart Grid. READER (OPTIC) helps discover underlying structure from increasing-dimensional consumer consumption time series data in a provably optimal real-time (online) fashion. READER (OPTIC) catalyzes the efficacy of DR programs by systematically and efficiently managing the energy consumption data deluge, at the same time capturing in real-time (online), specific behavior, i.e., households or time instants with similar consumption patterns. The primary feature of READER (OPTIC) is a real-time (online) randomized approximation algorithm for grouping consumers based on their electricity consumption time series data, and provides two crucial benefits: (i) time efficiently tackles high volume, increasing-dimensional time series data and (ii) provides provable worst case grouping performance guarantees. We validate the grouping and DR efficacy of READER and OPTIC via extensive experiments conducted on both, a USC microgrid dataset as well as a synthetically generated dataset.

Design of an Embedded Sensor Network for Application in Energy Monitoring of Commercial and Industrial Facilities

The deployment of energy monitoring systems is well established for industrial facilities and commercial buildings, however these systems are insufficient where large variations in activity level (production variation, one-off events) are common. In such cases, accurate analysis of energy usage requires the deployment of a flexible embedded sensor network that can capture data at the required level of accuracy and granularity and can associate the energy usage data with the context and activity that has caused it. This research proposes an approach and a set of hardware and software tools for the monitoring of energy usage in technical building services and the association of that data with real-time production information in advanced manufacturing facilities.The system developed incorporates an embedded controller with sensors to monitor the physical environment, an integrated energy analyser to monitor electricity consumption and a visualisation interface that associate events in the physical world with the actual energy consumption recorded. The creation of a detailed time-stamped energy profile for the process can then be used in the identification of energy saving opportunities. A case study from a pharmaceutical facility was used to validate the operation of the system.

Home Electricity Consumption Monitoring Enhancement Using Smart Device Status Information

Electricity consumption monitoring to understand energy consumption status and patterns is the first step in reducing the electric cost for a house. Conventional electricity consumption monitoring systems usually rely on specially designed power measuring equipment such as a smart meter or sub-metering device. However, it is not easy to obtain enough information for energy monitoring because the limited number of measuring equipment can be installed due to their costs and privacy issues. Therefore, the status of devices could be usually inferred from limited information using the technology called non-intrusive appliance load monitoring. However, the limited information causes inaccuracy in estimating operation status of the devices. Many efforts have been performed to improve the estimation accuracy. Smart devices including smart appliances have been deployed recently to provide more advanced services at home. Network connectivity is one of the essential functions for the smart devices. When the operation information of those smart devices can be shared with energy monitoring devices, it can improve the energy consumption monitoring. In this paper, a hybrid approach is proposed to improve energy consumption status estimation. Devices' status information is shared via ubiquitous home networks in addition to the conventional energy consumption monitoring. This approach makes the energy consumption inference more simple and accurate for the rest of unknown devices.

Energy Controlling - Analysis and Evaluation of Energy Measuring Equipment for the Purpose of Energy Transparency in Production Plants

This paper addresses the analysis and evaluation of energy measuring devices for the operation in the manufacturing industry. The focus lies on the comparison and scoring of energy meters regarding their performance spectrum against the backdrop of an energy controlling. Based on the fundamentals of electro technical metrology, the main measurement parameters were identified which are necessary for the purpose in the manufacturing industry. Further, capability characteristics for the differentiation of electricity meters were defined. With this, a classification of meters into a basic, a standard, an advanced and a premium class was undertaken. It is shown, that the advanced class is sufficient for the permanent monitoring of electricity consumption in the lights of energy controlling.

Research on Intelligent Power Management System Based on CAN Bus

This paper analyzes the actual situation of the electricity management about student apartments, and design intelligent energy student housing management system based on CAN bus. The system uses the field level, the underlying management level and upper management level three management systems. Field level with a dedicated energy metering chip AD7755 and STM32F103 microcontroller with A/D conversion function as the core, to achieve real-time power measurement; via CAN bus timing or random read live energy data for monitoring electricity consumption of the apartment, investigate abnormal electricity, thus effectively limiting the students to use electrical power to achieve the modernization and automation of power management solutions student apartments.