Topic: BusinessDecision Making

Last updated: February 12, 2019

ESTIMATING ENERGY CONSUMPTION BASED ON SMART ENERGY METER USING WIRELESS NETWORK K.Deepa. St.

Joseph College Of Engineering,Chennai119 ABSTRACT This paper presents a Smart power metering system or issue is captivated by countless profits. In India and other countries prove that smart metering is technically practicable. Main issues are the real value of the payback, the outlay involved, the distribution of overheads and gross settlement of smart metering between markets parties involved. The fundamental necessities of human beings is electricity which is commonly used for domestic, industrial and agricultural purposes. Accurate prediction/forecasts of energy demands(load) is crucials.

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The progress in technology about electrical distribution network is a non-stop process. To reduce the power consumption in a house or at individual site remains to be a difficult problem. This is done using Smart Energy Meter (SME) , a common form of smart grid technology, are digital meters that replace the old analog meters used in homes to record electrical usage. SEM is an electric device having energy meter chip for measuring the electrical energy consumed and a wireless protocol for communication. It presents a automatic metering and billing system. This meter can work as either post-paid or pre-paid.

It avoids the human intervention, provides accurate billing and minimize the power consumption in a house. In EB Server Section, Easily we will monitor the home section data and control the load using Smart Metering system we can avoid Wrong Power Usages and etc. we can view the values remotely using the IOT in the web server and can be controlled. I. INTRODUCTION: Internet of Things (IoT) is an interconnection of several devices, networks, technologies and human resources to achieve a common goal. There is a variety of IoT based applications that are being used in different sectors and have succeeded in providing huge benefits to the user. Data Analytics has a significant role to play in the growth and success of IoT applications and investments.

Electricity has now become a part of our daily life and one cannot think of a world without electricity. It is an important part of homes & industries. Almost all the devices at homes, businesses and industries are running because of electricity . In developing countries like India, power theft is one of the most prevalentissues which not only cause economic losses but also irregular supply of electricity. So, it has become important for the government to manage electricity and estimate it properly. With the evolution of IoT, the conventional use of energy meter can be made as smart energy. A smart meter is a new kind of electricity meter that can digitally send meter readings to your energy supplier for more accurate energy bills. Smart meters come with in home displays, so consumer can better understand their energy usage on daily basis.

The energy consumption will be daily monitored and on comparison with historical datas, the energy theft is identified. The proposed system will reduce the human intervention. Experiments are carried out using available datasets and results are expected to a increase in accuracy when compared to other methodologies.

This also helps provider and consumer to have a transparent and balanced energy billing system.This methodology can be extended further II. EXISTING SYSTEM : In Existing System, the possibility to use the network of SMs as a sensors network for the grid monitoring has been explored and validated through a dedicated experimental set-up. In fact, SMs of the next generation, compared to devices already installed in some countries provide several parameters, like active and reactive power, line frequency, voltage dips and total harmonic distortion, which can be used by DSO to monitor the status of the network. A network composed of 48 SMs has been deployed over the LV grid of A2A in the city of Brescia, Italy.

Each SM is connected to the MDC by means of a performing broadband power line communication network. The results of the monitoring, performed over 2 months, highlight the potential capabilities of a large scale monitoring system based on the use of a network of second generation SMs. Using the SM network has been verified that the voltage is below the 5 % of the nominal value only the 3 % of the time in section of the distribution grid under analysis, despite the large presence of distributed energy resources. In addition, the SMsnetwork identifies inversion of the energy flow in part of the distribution grid due to an excess of PV energy production compared to customers consumption. we can view the values remotely using the IOT in the web server and can be controlled. III.

PROBLEM STATEMENT: The Electricity Board (EB) have got used to manual process. Going to each and every house and generating the bill is a laborious task and requires lot of time(Fig 1). If the consumer is not available , the billing process will be pending and human operator again needs to revisit. It becomes difficult especially in rainy season. If the consumer did not pay the bill, the operator needs to go their houses to disconnect the power supply. This is time consuming and difficult to handle. To avoid human intervention in billing process, automatic billing is done to reduce the manpower and power consumption(Fig 2).

Fig.1. Monthly (4-week) energy consumption plots of Fig.2. Daily energy consumption plots of Three normal and abnormal household electricity use households Fig 3: Piecewise Regression Model Fig 4:Comparison Of ARMA&ARIMA:IV. LITERATURE SURVEY: Kasun Amarasinghe., Daniel L. Marino and Milos Manic1Investigates the effectiveness of using Convolutional NeuralNetworks (CNN) for performing energy load forecasting at individual building level.

Methodology uses convolutions on historical loads. Weather data to find the effectiveness of deep learning algorithms in load forecasting. Yi Wang, Qixin Chen.

, Tao Hong and Chongqing Kang2 It conducts an application-oriented review of smart meter data analytics. It follows three stages of analytics, namely, descriptive, predictive and prescriptive analytics. To provide a complete picture and deep insights into this area of big data issue, developments of machine learning, novel business model, energy system transition, and data privacy and Security. Ramyar Rashed Mohassel., Alan Fung., Farah Mohammadi and Kaamran Raahemifar3Advanced Metering Infrastructure provides a needs improvement in the areas of communication, data analysis and control schemes. Smart home system with RaspberryPi and NodeMCU as the backend that not only serves as home automation and merely a switch replacement, but to also record and report important things to the owner of the house.

Further smart home development with better security and more functions.V. Preethi and G. Harish4 SME helps user in identifying the usages between authorized and unauthorized users which helps in controlling the power theft. Communication between user/household and substation is done using Zigbee. GSM network is used for sending SMS to the local authorities. Yasirli Amri and Mukhammad Andri Setiawan5 Used to calculate the power that has been used by electronic devices so thathomeowners can save money in electricity bills. This greatly helps the homeowner to monitor the house even when he is not at home.

Smart home system with RaspberryPi and NodeMCU as the backend that not only serves as home automation and merely a switch replacement, but to also record and report important things to the owner of the house. Further smart home development with better security and more functions. Maninderpal Singh and Er.Varun Sanduja6IOT technology the government person can find the dishonest user, it can make the assignment of the agents impracticable to steal the electricity, the power theft. This analysis work has been implemented to find the dishonest use. Energy meter communicate with raspberry pi through GPIO pins. GPIO pins fetch the effective data from energy meter and it send effective data to the raspberry pi, then connect wifi module with raspberry pi. The implementation smart meter automatically cut electricity when any one tried to theft and it also monitor the electricity consumption through smart phone and smart meter that sends status if any fault occurred in transmission line.

Darshan Iyer N and Dr. K A Radhakrishna Rao7 Ease of accessing information for consumer from energy meter through IoT. Theft detection at consumer end in real time.

LCD displays energy consumption units and temperature. Disconnection of service from remote server. IoT and PLC based meter reading system is designed to continuously monitor the meter reading and service provider can disconnect the power source whenever the customer does not pay the monthly bill and also it eliminates the human involvement, delivers effective meter reading, prevent the billing mistake. IoT energy meter consumption is accessed using Wi-Fi and it will help consumers to avoid unwanted use ofelectricity.

The performance of the system can be enhanced by connecting all household electrical appliances to IoT. Pooja D Talwar , Prof. S B Kulkarni8-To generate bill automatically by checking the electricity unit’s consumption in a house and in a way to reduce the manual labor. The calculations are performed automatically and the bill is updated on the internet by using a network of Internet of Things. The bill amount can be checked by the owner anywhere globally.

Wifi ESP8266 is a low cost chip and microcontroller. Displaying the information about the energy consumed in terms of units, about the bill and if any theft occurs that will be displayed in the website. The main improvement for the future is going to make energy meter readings, tampering identification techniques, and connection and disconnection and also the pre information providing to the users all is going to happen on wifi internet. Mr. Rakeshkumar D. Modi , Mr.

Rakesh P. Sukhadia9 -The design can be eliminate the man power involvement to maintain the electricity. The consumers of electricity need to pay as per the utilization of electricity on schedule, somehow consumers fail to pay, the transmission of electricity can be turned off from the distant server automatically. Energy meter provides provision to the consumers that they can monitor the energy consumption in units by using web page. The smart electricity energy meter consists Microcontroller, LCD display, theft detection unit, temperature sensor, PLC (Power Line Communication)modem and Wi-Fi module. Theft detection and supplier can disconnect service to the consumers in the event of meter tempering or un authorized use of electricity.

It eliminates the man power required for meter reading.Manasi Giridhar and S.Kayalvizhi10 Monitoring the systems in smart grid and collect the information of electricity uses then establish communication with the consumers which can be useful for providers as well as consumer by using message queue telemetry transport protocol. In additionally it provides real time pricing and monitored usage information to the consumers. Current and voltage sensors are used for measuring the power consumption .

Those readings are sent to the consumer as well as Electricity Board by using ZigBee and message Queue Telemetry Transport (MQTT) protocol. MQTT is a network protocol which is used transfer data between publisher and subscriber. It is a publisher and subscriber protocol. IoT energy meter consumption is accessed using Wi-Fi and it will help consumers to avoid unwanted use of electricity. The performance of the system can be enhanced by connecting all household electrical appliances to IoT. Ning Lu.,Pengwei Du Xinxin Guo and Frank L.

Greitzer11 Defining each individual load profile .In home energy management systems (HEMS), daily energy consumption patterns can be an important variable to monitoring and triggering customer actions. A 15-minute meter and weather data set collected by researchers at Pacific Northwest National Laboratory (PNNL) . For customers, however, daily monitoring for security and energy consumption(Fig 2) is better than monthly monitoring because customers have exclusive information about their electricity usage. Focuses on studying the data correlation among multiple data sources from both the distribution and transmission power grids. Energy consumption (Fig 1) varies daily due to weather, occupancy, and different customer consumption patterns, daily information alone is of little value to a utility unless an abnormality in monthly energy consumption is detected.Xiufeng Liu.

, Lukasz Golab and Ihab F. Ilyas12 SMAS, our smart meter analytics system, which demonstrates the actionable insight that consumers and utilities can obtain from smart meter data . SMAS, our smart meter analytics system, which demonstrates the actionable insight that consumers and utilities can obtain from smart meter data . The slope (Fig 3) of the 90th percentile line corresponding to high temperature is the cooling gradient, and the slope of the line corresponding to low temperature is the heating gradient. Furthermore, the height of the 10th percentile lines at their lowest point is the base load, which corresponds to load due to appliances that are always on, such as a refrigerator. Savita Pawar ,Dr.

B. F. Momin13The smart meters main functionality is measuring, capturing and transforming data (Table 1) related to usage or consumption of electricity, gas or water and events such as meter status and power quality . Smart meters and smart grid will be the dominant part of Internet of Thing (IoT) which integrate various views of peoples’ requirements and services to satisfy them and therefore many analytical challenges arises such as real time analytics. Dr.P.Mathiyalagan.

,Ms.A.Shanmugapriya.,Geethu.

A.V14Electricity consumption data of a smart meter are used at a sampling rate of one minute. The streaming data is loaded into HDFS in a hive table, which is further exported into R in order to perform predictive analysis and load profile analysis. The data are aggregated into daily, weekly, monthly and quarterly series, and used ARMA and ARIMA (Fig 4)model to predict the future electricity consumption. Arunesh Kumar Singh., Ibraheem.

, S. Khatoon., Md. Muazzam and D. K. Chaturvedi15 it can be inferred that demand forecasting techniques based on soft computing methodsare gaining major advantages for effective use.

There is also a clear move towards hybrid methods, which combine two or more techniques such as regression, multiple regression, exponential smoothing, iterative reweighted leastsquares, adaptive load forecasting, stochastic time seriesautoregressive, ARMA model, ARIMA model etc. This paper presents a review of electricity demand forecasting techniques. Table 1 :Techniques and tools and its Application in Smart Meter Data Analytics Techniques and Tools Applications SOM Clustering load curve , Load forecasting, Load Profiling SVM Electricity theft detection and Appliance type recognition. FL Intelligent support system, Automated decision making. BN and HMM Appliance identification, load disaggregation, supply demand analysis. V. RESEARCH METHODOLOGY The proposed system has two section namely, Home Section and Electrical Base Station. The communication between these two is done by wireless network.

It monitors the load and calculates the power consumed exactly by the user at a given time. Energy utilized and the corresponding current, voltage, power and amount will be displayed on LCD continuously and communicated to the base station. An SMS containing monthly bill along with due date is sent to respective meter owner using GSM. IOT Gateway Device with wifi is used to transit the power consumed to the Electricity Board website.

In case of bill not paid , the automatic power cut is done through RelayDriver Circuit. The system can act as either pre- paid or post-paid.In pre-paid mode , the power consumption(in watts) with respect to time that corresponding amount is deducted from the total amount and is displayed on the LCD continuously.

If the recharged amount reduces to half of the total amount a buzzer is activated and continuously alerts the consumer.In post-paid mode, it simply displays the power consumption and the corresponding amount on LCD continuously. Fig 5: Implementation diagram VI. CONCLUSION: The Electrical distribution network is a non-stop process in today technology. An advanced metering road and rail network put forward the leeway for auxiliary energy allied services such as demand side management and consciousness of virtual power plants. The potential of smart metering relies profoundly on the policy and decisiveness of the legislative bodies mixed up. Energy savings and an improved security of supply are the major drivers and deems in smart metering as huge targets of a nation.

The Smart metering system will monitor the consumed power in particular home and transmitted via IOT Gateway Device. It avoids human intervention in billing process , Lack of details in meter cards. Delay in meter reading by assessors, Failure to upload meter readings on main server even if done promptly and assessors wrongly notifying the date of assessment in the meter cards VII. REFERENCE: 1. Kasun Amarasinghe, Milos Manic” Deep Neural Networks for Energy Load Forecasting”, : 2017 POWER SUPPLY (SMPS) ENERGY METER MICRO CONTROLLER ERELAY IOT GATEWAY DEVICE SMS LCD DISPLGSM NETWORK (COMMUNICATION BASE MOBIIEEE 26th International Symposium on Industrial Electronics (ISIE).

2. Yi Wang , Qixin Chen “Review of Smart Meter Data Analytics: applications, Methodologies, and Challenges”,:IEEETransactions on Smart Grid ( Early Access ) in 22nd march 2018. 3. Ramyar Rashed Mohassel, Alan S. Fung”A survey on AdvancedMetering Infrastructure”, Published in: 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). 4.

Mofeed Turky Rashid” Design and Implementation of Smart Electrical Power Meter System”,Iraq J. Electrical and Electronic Engineering Vol.10 No.1 , 2014. 5.

Yasirli Amri and Mukhammad Andri Setiawan”Improving Smart Home Concept with the Internet of Things Concept Using RaspberryPi and NodeMCU”, Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering,2018. 6. Vani.H, S. M. Varun Kumar, Chetan Sastry, Shrinidhi.W,”Minimizing Electricity Theft by Internet of Things, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), Volume VII, Issue V, May 2018 7. Darshan Iyer N, Dr.

K A Radhakrishna Rao”IoT Based Electricity Energy Meter Reading, Theft Detection and Disconnection using PLC modem and Power optimization”, International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering(An ISO 3297: 2007 Certified Organization) Issue 7, July 2015. 8. Pooja D Talwar “Iot based energy meter reading”, International Journal of Recent Trends in Engineering & Research (IJRTER)Volume 02, Issue 06; June – 2016 9.

Mr. Rakeshkumar D. Modi “A review on iot based smart electricity energy meter”, International Journal For Technological Research In Engineering Volume 4, Issue 1, September-2016.

10. Arati Kurde “A review on iot based smart electricity energy meter”, National Conference on “Internet of Things: Towards a Smart Future” & “Recent Trends in Electronics&Communication”(IOTTSF-2016).11. Ning Lu” Smart Meter Data Analysis” Conference: Conference: Transmission and Distribution Conference and Exposition (T&D), 2012 IEEE PES. 12. Xiufeng Liu “SMAS: A Smart Meter Data Analytics System “Conference: IICDE,in april 2014. 13. Savita Pawar ; B.

F. Momin “Smart Electricity Meter Data Analytics: A Brief Review Published 2017 in 2017 IEEE Region 10 Symposium (TENSYMP). 14. P. Mathiyalagan ; A. Shanmugapriya ; A.

V. Geethu ” Smart Meter Data Analytics using R and Hadoop”, : 2017 IEEE International Conference on (EIT).

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