Industrial Training Report

practical training taken

At

GENUS POWER
INFRASTRUCTURE LIMITED
(Jaipur)
Submitted in partial fulfillment for the award of degree of
Bachelor of Technology In
Department of Electrical Engineering

Submitted By:-
Rajat Shrivastava
22UELE6042
B.Tech. IV Year

Department of electrical engineering

MBM University, Jodhpur

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 ABSTRACT
In recent years, the demand for electricity has increased in households with the use of
different appliances. This raises a concern to many developed and developing nations with
the demand in immediate increase of electricity. There is a need for consumers or people to
track their daily power usage in houses. In Sweden, scarcity of energy resources is faced
during the day. So, the responsibility of humans to save and control these resources is
also important. This research work focuses on Smart Metering data for distributing
electricity smartly and efficiently to the consumers. The main drawback of previously
used traditional meters is that they do not provide information to the consumers, which
is accomplished with the help of Smart Meter. A Smart Meter helps consumers to know
the information of consumption of electricity for appliances in their respective houses.
The aim of this research work is to measure and analyze power consumption using Smart
Meter data by conducting case studies on various households. In addition to saving
electricity, Smart Meter data illustrates the behavior of consumers in using devices. As
power consumption is increasing day by day there should be more focus on
understanding consumption patterns i.e., measurement and analysis of consumption
over time is required. In the case of developing nations, the technology of employing
smart electricity meters is still unaware to many common people and electricity utilities.
So, there is a large necessity for saving energy by installing these meters. Lowering the
energy expenditure by understanding the behavior of consumers and its correlation with
electricity spot prices motivated to perform this research. The methodology followed to
analyze the outcome of this study is exhibited with the help of a case analysis, ARIMA
model using XLSTAT tool and a flattening technique. Based on price evaluation results
provided in the research, hypothesis is attained to change the behavior of consumers
when they have better control on their habits. This research contributes to measuring the
Smart Meter power consumption data in various households and interpretation of the
data for hourly measurement could cause consumers to switch consumption to off-peak
periods. With the results provided in this research, users can change their behavior when
they have better control over their habits. As a result, power consumption patterns of
Smart electricity distribution are studied and analyzed, thereby leading to an innovative
idea forsaving the limited resource of electrical energy.

Keywords: Advanced Meter Infrastructure, Power consumption patterns, Smart Meters,

Smart Metering, ARIMA models.

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 ACKNOWLEDGEMENT
We would like to thank our supervisor Mr. Kamlesh Kumar GARG for his guidance and encouragement
provided throughout the period of our report. His patience and valuable time eased us to move our thesis in
acoordinated manner.
Furthermore, we would like to express our gratitude to the electricity company who provided real-time
data. Lastly, we would like to thank our parents and friends for their moral support which. Boosted our
confidenceto pass the milestones in the journey of our education.

(Rajat Shrivastava)

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 TABLE OF CONTENTS

ABSTRACT
ACKNOWLEDGEMENT
LIST OF ABBREVIATIONS
1. INTRODUCTION ....................................................................................................................... 1
2. BACKGROUND AND RELATED WORK ...................................................................3

2.1. Evolution of electricity meters from the past

2.2. Smart Grid

2.3. Smart meter

2.4. Power consumption

2.5. Study of people’s behavior

2.6. ARIMA model

2.7. Survey of related Work

3. DESIGN AND IMPLEMENTATION……………………………………..

4. CASE STUDY 15 ....................................................................................................................... 5

RESULTS.............................................................................................................................. 17 6
DISCUSSION ......................................................................................................................... 49

5. CONCLUSION AND FUTURE WORK .................................................................................... 50

REFERENCES ............................................................................................................................. 52

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 LIST OF ABBREVIATIONS

AC ( Alternate Current)
ACF ( Autocorrelation Function)
AIC (Akaike Information Criterion)
AICC (Akaike Information Criterion Corrected)
AMI (Advanced Meter Infrastructure)
AMR ( Advanced Meter Reading)
ANN (Artificial Neural Network)
ARIMA (Autoregressive Integrated Moving Average)
ARMAX (Autoregressive Moving Average with Exogenous Inputs)
CMCC (China Mobile Communication Corporation)
CPP (Critical Peak Pricing )
DAP (Day Ahead Pricing )
FPE (Final Prediction Error
GARCH (Generalized Autoregressive Conditional Heteroscedastic)
HEMS (Home Energy Management System)
HAN (Home Area Network)
IHD ( In-Home Display)
MAPE ( Mean Absolute Percentage Error)
MaxAE (Maximum Absolute Error)
MaxAPE (Maximum Absolute Percentage Error)
MSE (Mean Square Error)
NRMSE (Normalized Root Mean Square Error)
OLTP ( Online Transaction Processing Systems)
PNNL (Pacific Northwest National Laboratory)
PACF (Partial Autocorrelation Function)
RTP (Real Time Pricing)
SBC (Schwarz criterion)
SCADA (Supervisory Control and Data Acquisition System)
SEMS (Smart Energy Management System)
SSE (Sum of Squared Errors of Prediction)
TAM (Technology Acceptance Model)
TOUP ( Time of Use Pricing)
WIMAX (Worldwide Interoperability for Microwave Access)
WN variance (White Noise variance)
XML ( Extensible Markup Language)

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 List Of Figure

Figure no. Figure name

2.1 Traditional meter
2.2 Smart meter
2.4 Smart Meter measuring electrical appliances in a electrical appliances
in a household [19]

3.1 Flow of Research Methodology

5.1 Energy Consumption, Price, Cost and Cumulative cost of household 3

on JUNE 6
5.2 Energy consumption, , Price, Cost and Cumulative cost of household
3 on JUNE 9
5.3 Energy Consumption, Price, Cost and Cumulative cost of household 6
on JUNE 6
5.4 Energy Consumption, Price, Cost and Cumulative cost of household 6
on JUNE 14
5.5 Peak energy consumptions of different households
Correlation graph for household 1
5.6

5.7 Difference graph for household 1

Correlation graph for household 6
5.8

Difference graph for household 6

5.9

Correlation graph for household 10

5.10

Difference graph for household 10

5.11

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1. INTRODUCTION
In the early phase of household technology, delivery of electricity is completely depended on
traditional energy meters. These meters play a key role in measuring the consumption of electrical energy in
individual households. The usage of these meters has been slowly declining with the advancement in
technology as rapid changes have been made to encounter the problems occurred by the traditional
meters. The major problem arises when habitants are unaware of their daily behavior. Monthly feedback
given to the consumers is not sufficient as the consumers will not have knowledge on how much energy
does the individual.
appliances consume. To overcome the problems of traditional electricity meters, Smart Meters have been
upgraded and developed. With the use of Smart Meter data, energy alerts will be provided to the
consumers based on hourly utilization of energy. The primary objective of the Smart Meters is to reduce
the energy consumption in the households. Our thesis utilizes real time Smart Meter data sets obtained from
a Swedish electricity company. A case study is performed on hourly measurement data of 16 households to
determineconsumption patterns.
With its growing attention in the market the behavior of the consumers can be studied and analyzed.
The energy consumption patterns can be facilitated in improving the behavior of users. The electricity
market can be restructured with the installation of these meters, as it not only preserves energy, but also reduces
carbon dioxide emissions.
Trust and credibility of these meters is established only when the consumers have positive quality of
experience.
Timely consumption of consumers can be reduced as Smart Meters are connected to online billing.

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2. BACKGROUND AND RELATED WORK

a. Evolution of Electricity Meters from the Past

In early years, electricity is available only to a specific section of affluent society. The advancement in techno-
logy over time encouraged meeting the demands of common people in all parts of the world. The history
of electricity meter is well connected involving researchers from past. The general usage of electricity in the
early 1870‘s is only confined to telegraphs and arc lamps. With the invention of the electric bulb by Thomas
Elva Edison, the power energy market became widely opened to the public in the year 1879. Oliver B.
Shallenberger introduced his AC ampere hour meter in the year 1888. Eventually, the progressive
development in metering technology leads in enlightening the lives of many common people .

• Traditional Electricity Meters and its types

The electrical devices that can detect and display energy in the form of readings are termed as the
electricity meter. Traditional meters are used since the late 19 century.
They exchange data between electronic devices in a computerized environment for both electricity
production and distribution. In most of the traditional electricity meter aluminum discs are used to find the
usage of power [28]. Today ‘s electricity meter is digitally operated but still has some limitations. A simple
1 Phase 2Wire electricity meter is shown in the below figure2.1.

Figure2.1 Traditional meter

Some of the limitations faced by the traditional electricity meter [28] are as follows:
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• Meters are unreliable in nature as consumer has to anticipate for the monthly electricity bill.
• The process of measurement is supported by a specific mechanical structure and hence they are
calledelectromechanical meters.
• In order to perform meter readings, a great number of inspectors have to be employed-ed.
• Payment processing is expensive and time consuming.
• □ New type of tariffs on hourly basis cannot be introduced with the corresponding meters
forencouraging the consumer.
• □ Development of meter software applications and supportive network infrastructure is complicated.

Besides the above-mentioned limitations, there are also several other elements creating app between the
consumer and distributor because of installation of traditional Meters are of distinct types. Even though
timely development of electricity meters helps. The consumer to gain knowledge with respect to
electricityconsumption, statistics of the consumption couldn’t be changed.
Some basic types of meters are explained as follows: -

Different Types Outline

Electrolytic Meter The whole current passes through the electrolyte. The major
drawback is mechanical considerations and adoption by limited.
localities.

Brush-shifting device is used to vary the current load and

Commutator Meter commutator ‘so small diameter facilitates in insulation
attention. The major drawbacks are inadequate load
characteristics, maintenance cost and lack of proper insulation.

There is a satisfactory performance with the introduction of

Mercury Motor this meter. The adoption of rotor made a prominent role in
supplying the calibration. The momentary short circuit is
reduced or even prevented.

This meter model is developed for heavy current circuits where the
D.C Watt Hour temperature coefficient is high. For indication of demand purposes, a DC
Watt Hour separate time switch is used. Also, it is a clock-type meter inwhich
Meter voltage variations is less with the reduced shunt loss.

Magnetic conditions are better improved to control energy

Single Phase meter consumption and a considerable improvement in performance is also
done. Meter inspection is easily assessed as the construction of this
Induction model has accessibility of simplifying assembly.

Lagging power factors in the meter reflect the characteristics of the

Poly-Phase Watt current transformer. Attempts for improving a high degree of
Hour Meter accuracyhave been built to avoid troublesome corrections.
Interaction effects,calibration and increase in the effects of shunt
loss are the greatest drawback of this model.

Table. -1. Various electricity meters

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2.1. Smart Grid
Smart Grid is the modern development in electricity grid. Recent electrical grids are
becoming weak with respect to the electrical load variation of appliances inside the home. The
increase in population is also the indication of electrical grids becoming more fragile. The
higher the population, the more load on the grid.
Improving the efficiency of grid by remotely controlling and increasing reliability,
measuring the consumptions in a communication that is supported by delivering data (real-time)
to consumers, supplier and vice versa is termed as Smart Grid [31]. Automated sensors are used
in Smart Grids. These sensors are responsible in sending back the measured data to utilities and
have the capability to relocate power failures and avoid heating of power lines. It employs the
feature of self-healing operation. Literally, the concept of Smart Meter is commenced from the
idea of Smart Grid. A carbon emission reduction of 5% is expected by 2030, annually by its
installations and it can show a greater impact on environmental changes [31]. For a sustainable
development and establishment of new grid infrastructure, Smart Grids are recommended for
many countries.
2.2. Smart Meter
Smart Meter is an environmentally friendly energy meter that is used for measuring the
electrical energy in terms of KWh (Kilowatt - hours). It is simply a device that affords a
direct benefit to the consumers who want to save money on their electricity bill. They belong
to a division of Advanced Meter Infrastructure and are responsible for sending meter
readings automatically to the energy supplier. A simple picture of a Smart Meter is shown
below.

Figure 2.2 Smart Meter [20]

Accurate meter reading will be provided with the inclusion of firm benefits from the Smart
Meter. They record the consumption on the basis of hourly or less than hourly intervals. ASmart
Meter has non-volatile data storage, remote connect or disconnect capability, tamper detection
and two-way communication facilities. They perform remote reporting of the collected data to the
central meter. This central meter monitors the functionality of the Smart Meter. From an
operational perspective, use of Smart Metering allows an improved management and control over
the electricity grid.:

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 Some of the benefits of Smart Meters are as follows
• Low operational cost.
• Time saving to the consumers and utility companies for reporting the meter reading back to the energy providers.
• Online electricity bill payment is allowed.
• Power consumption can be greatly reduced during the high peaks with an intimation plan

• Smart Meter senses all the consumption generated inside the residents. Meter readings give a
❖ broader understanding to the energy utilities so that overall energy usage customs of the habitants can be
altered. Finally, all the information that is generated by Smart Meter will increase help in noble generation.

2.3. Power Consumption

The total amount of power consumed in an individual household is referred as power consumption.
The consumption of power is an important aspect of electricity supply. People should be aware
of preserving energy for future use. With daily usage of electricity, the energy patterns have been
slowly varying. This variation of consumption patterns can be caused by weather conditions or
unnecessary utilization of power by inhabitants such as increase of appliances in respective
households and careless attitude in utilization for example not switching OFF the lights or
television when not watching it. These factors may show greater impacts on end user.
As the power supplied by energy companies is vast, most of the people are neglecting energy and its
savings. The importance of consumption is declining in the mindset of utilities. The energy utilities
should play a major role in advancing the Smart Meter technology and should make people
participate in reducing energy consequences by creating awareness about the impact of their
currentlevel of consumption.

2.4. Study of People’s Behavior

People‘s behavior is termed as behavior of consumer on appliance consumption in a household.
If the consumption of the customer is high then we can empathize that their usage of devices is
also high, which means cost is directly proportional to the product of number of uses and the
corresponding durations. It is important for energy companies in reaching the anticipation of the
customer. In-fact most of the consumers rely on the monthly bill they expect for. They usually do
not know which appliances are consuming more energy and how they can manage their
consumption better. These factors play an important role in influencing the behaviors of the
customer.

The better understanding of the people ‘s behaviors is only achieved through analyzing how
they use their energy. The consumers should be influenced in a smart way while accessing their
appliances [2]. An illustration of a Smart Meter installed in a household while measuring the
appliances is shown in figure 2.3.

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 Figure 2.4 Smart Meter measuring electrical appliances in a household [19]

The above figure expresses the daily activities of household appliances measured by a Smart Meter
in a home. Smart Meter is installed outside the house and its hourly consumption data is measured
for lowering consumer electricity bills. This measurement facility converts simple home to a smart
home.

2.5. ARIMA model

The acronym of ARIMA stands for Autoregressive Integrated Moving Average. ARIMA model is a
standard linear time series model that accepts the present values and predicts the future values in the
series. It is represented as ARIMA (p, d, q) where parameter p is referred as the order for auto-
regression, parameter d is the order for non-seasonal difference and q is the order for the moving
average. The ARIMA model accepts time series data as input (combination of past values) and
predicts future values as output. Predicting the future values guides in applying many
applications such as demand estimations, stock prices estimation, economic estimations and sales
representations[23].
There are two types of ARIMA processes, seasonal and non-seasonal ones, which arediscussed in
detail below.
Seasonal ARIMA model: Seasonality is a regular pattern of changes that repeats overs time
periods. A seasonal ARIMA model is expressed as ARIMA (p, d, q) (P, D, Q)s where P is
the order of seasonal auto regressive part, D is the order of seasonal differencing part, Q is the
orderof seasonal moving average part and s is the number of time periods of seasonal cycle [30].
Different seasonal ARIMA models are:

ARIMA (1, 0, 0) (0, 1, 0)12: First-order autoregressive term in non-seasonal part and seasonal
differencing of order 1.

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ARIMA (1, 0, 1) (0, 1, 1)12: First-order autoregressive term and moving average term in thenon-
seasonal part and first-order moving average term in the seasonal part with seasonaldifferencing of
order 1.

ARIMA (0, 1, 1) (0, 1, 1)12: First-order moving average term, differencing term in the non-
seasonal part and first-order moving average term with seasonal differencing.

ARIMA (2, 0, 1) (2, 1, 0)12 : Second-order autoregressive term, first-order moving average
term in non seasonal part and second-order autoregressive term in seasonal term with seasonal
differencing of order 1.

It is not recommended to use more than one order of seasonal differencing or more than two
orders of total differencing [30].
Seasonal ARIMA presents the series in terms of its past values at lag equal to the length ofthe
period (s), while the non-seasonal ARIMA does it in terms of its past values at lag 1 [34].
Non-SeasonalARIMA model: A non-seasonal ARIMA model is represented as ARIMA.

(p,d, q) model where p is number of autoregressive terms, d is number of non-seasonal differences and q
is moving average term [23].

Different non-seasonal ARIMA models are:

ARIMA (2, 1, 1): An ARIMA model with autoregressive term of order 2 and movingaverage termof
order 1 with differencing of order 1.

where d is the differencing term

a (1) is first order autoregressive coefficient a(2) is second order autoregressive coefficientc(1) is
first order moving average coefficient y(t□1), y(t□2) are series in previous values
e(t) and e(t□1) are residuals at period t and (t□1)

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ARIMA (1, 1, 1): A mixed model of autoregressive and moving average terms of
order 1with differencing of order 1.
Y(t) = d+ a(1).y(t□1)– e(t) – c(1).e(t□1)

where a(1) is first order autoregressive coefficient

d is differencing term
c(1) is first order moving average coefficient
y(t□1) is series in previous values
e(t) and e(t□1)are residuals at period t and t□1

ARIMA (1, 1, 0): First order autoregressive term with non seasonal differencing of order
1.

Y(t) = d + a(1).y(t□1)

where a(1) is first order autoregressive coefficient

d is differencing term
y(t□1) is series in previous values

ARIMA (0, 1, 1): First order moving average term with non seasonal differencing of
order1.
Y(t) = d – e(t) – c(1).e(t□1)

where c(1) is first order moving average coefficient

d is differencing term
e(t) and e(t□1)are residuals at period t and t□1

2.6. Survey of Related Work

The survey is split into four parts, namely socio-economical issues, technological issues,
cases and prediction. As we started with literature survey in the initial stage of
research, thedivision of cases is chosen to answer research questions in an organized
manner.

Socio-economical issues: The value of customer satisfaction in communication market is

trusted with the services provided by service provider. In [2], the author explains
people‘s behavior towards the Smart Metering system and states the services such as
viewing electric consumption in real time, viewing the effect of turning electrical
appliances on and off, making estimation of the next bill, or receiving messages
directly from the grid operator. The consumption patterns during night and weekends
are projected in the paper. A survey is conducted in different countries over different
households and user‘s feedback is obtained so that people become motivated to be
energy-conscious. A socio-technical review to promote sustainable energy
consumptionusing Smart Meters is done. Answers are proposed for a set of research
questions suchas 1) Is feedback useful for energy saving and behavioral change? 2)
What presentationof feedback is good and effective? Scientific advice on energy
saving instruments for household energy consumption is provided in [3]. A Smart
Metering privacy model isimplemented to measure the privacy that a Smart Meter
will provide with and without involvement of third parties [4]. The advantages of
Smart Metering concept are low metering costs, energy efficiency and easier detection
of fraud [7].
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A quantitative survey was conducted among various households and results of this
survey were presented in paper [9]. The mapping of consumer ‘s perception with
household appliances is done. A theoretical framework named TAM is proposed
for household perception of Smart Appliances. Mean scores and standard
deviations for perceived usefulness, perceived ease of use, attitude and intention to
use, safety, control and comfort are tabulated.

Technological Issues:

The connection between meter and the household appliances is carried out in different
ways. The connection can be dedicated line, wireless connection, web-based
communication and power-line communication between the appliances in home and
the meter [1]. The secured scenario can be maintained by connecting the meter to the
data center. When Smart Meters are connected with mobile phones, the actual power
consumption of a device when
it is
switched ON/OFF or plugged in/out is observed [5]. An overview of Smart Metering
installations, implementations, and functionality which is installed in the Netherlands
is given in [6].
In [8], Smart Metering involves installation of one or several Smart Meters by
continuously
monitoring and sending feedback of data to the customer. Consumers, by making use of
Smart Meters, will get safe, secure and affordable energy, and a reduction of carbon
emissions is possible.
In [13], the architecture of Smart Energy Management System was developed to
control the
transmission capacity and rate generation for the aggregated load conditions of the
Smart Appliances. Energy prices, consumption and cost of consumption under
different demand conditions i.e. on-peak, mid-peak and off-peak values are tabulated.
The energy cost of each appliance is shown in pictorial form.

In [14], the importance of Smart Meter in the market with respect to the customer and
business organization has been reviewed. Functionalities and benefits of Smart Meters
compared to mechanical meters are explained. The authors are curious to find out the
hypothesis to the proposed questions in this particular research paper. To make energy
efficient society, the customer must be aware of the energy consumed. So, different
feedbacks are proposed in this paper to save energy and improve energy efficiency.
In [38], the monitoring of Smart Meters in Hungary is discussed. The meter has two-
way
communication capability for tariff-based operation and remote control. The
communication tools of the meter such as Zigbee, WIMAX and Home Area Network
supporting the energy meter is addressed. Energy Management System with high
level application possibility has been proposed.

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Cases:
In [10], consumption patterns are analyzed in two households and an office in the UK,
where real time reporting is done using web. The need for Smart Meters, benefits
and how to monitor the power is detailed. Experimental setup is designed in three
household premises. The experiment setup contains a section of equipment and
software. Graphs are observed on a 24 hour cycle online for weekday, Sunday,
before and after the change of appliance. The analysis is also done for heating water,
turning on central heating and printing from a laser printer. Direct feedback is
suggested to identify the appliances of high burn. The aim of influencing consumer
habits has been achieved by indicating where the savings are possible.
In [36], a thorough analysis of 15-minute residential meter data of 50 houses were used
to derive several target applications such as identifying demand response
potentials, abnormal load behaviors and fault diagnosis. In [11], the processing of
Smart Meter data with the aid of Supervisory Control and Data Acquisition System,
billing and weather data is focused. The data collected by the researchers at Pacific
Northwest National Laboratory was used. The load profile of two households with
highest and lowest energy consumption over 15 minutes during the month of April is
plotted. The impact of temperature on the power consumption of a household is
demonstrated.
A Smart Metering development system for a Korean residential environment is
explained
and system monitoring of other countries is reviewed in [15]. A pilot demonstration
with the developed system is conducted in 77 different sized households located in
two different cities. The study is focused on verifying the effectiveness of In-Home
Display which is an essential component of Korean Smart Metering system. Many ideas
such as Advanced Meter Infrastructure, Smart Grid and Smart Metering system have
been proposed. The results interpreted convey that people living in small houses are
more sensitive to price-related information. The daily power consumption comparison
graphs of two cities before and after using In-Home Display are demonstrated. The
impact of temperature on daily power consumption is observed.
Prediction:

In [12], price prediction is done on the basis of Home Energy Management System.The
experiment evaluated results in saving 22.2% of electricity expenditure daily.

Types of pricing models such as Real Time Pricing, Day Ahead Pricing, Time of Use
Pricing and
Critical Peak Pricing are specified. Client interface data model for the energy
consumption is
constructed using XML.
The graph for actual price and predicted price, maximum power utilization i.e. peak
hours are also compared and observed.
Test bed is designed to evaluate the Home Energy Management System.

16
In [21], simulation model presents a generated load profile for household to construct
flat tariffs. The impact of Smart appliances and variable prices on electricity bills of
a household is investigated. Field tests are carried out to estimate the bill saving and
other cost estimation. The operations of household appliances are shifted so that
users can reduce cost. The load curves for working days, Saturday and Sunday are
demonstrated. Comparison of load curves for flat tariff and time-based tariff is shown.
The results of the paper show how variable pricing will affect consumer behavior
under realistic environment conditions.
In [22], an ARIMA approach to forecast short term electricity prices to improve
accuracy by
forecasting errors is proposed in the paper. Based on the historical data obtained from
California power market, ARIMA model is implemented on daily average prices.
Forecasting curves after single and double error adjustments are shown in graphicalform.
Statistical results such as mean, variance, Mean Square Error, Maximum Absolute Error
for forecasting price of California and after twice error adjustments are tabulated.
In [26], spot electricity price forecasting has been done using European Energy
Exchange data. ARIMA (3, 0, 3) (1, 1, 1) is founded to be the best fitting model for the
experiment.
From the results, Maximum Absolute Percentage Error and Mean Absolute Percentage
Error of the model are rounded.
In [27], results from Spain and California markets are presented in this paper. The
differences of both the market have been observed by applying ARIMA model.
Time series analysis is explained with steps from identification to forecasting of the
model. The outcome of the Spanish market is 5 hours to predict future prices and 2
hours isneeded for California market predictions.
Monthly energy data forecasting approach of Provincial Electricity Authority of
Thailand is provided to decompose trend cycles and seasonal patterns. The
decomposition technique is used for time series forecasting, while correlation
coefficients and mean absolute percentage errors are computed to measure fitting
accuracy [36].

In [37], seasonal ARIMA model (2, 0, 1) (2, 1, 0) is used for forecasting the mobile traffic.
Analysis is performed based on the real time data obtained from CMCC. NRMSE is
calculated for determining and acceptance of forecast errors.

The papers which impacted our research addressed people‘s behavior towards Smart
Metering system [2]; benefits of Smart Meter compared to mechanical meters and
feedback to save energy on improving energy efficiency [14]; consumption patterns
on a 24 hour cycle are analyzed in two households in the
U.K [10]; a Korean residential environment of a Smart Metering system [15];
implementation of ARIMA model on time series analysis [27]; and the use of seasonal
ARIMA (2, 0, 1) (2, 1, 0) model which is analyzed with real time data [37].

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 3. SIGN AND IMPLEMENTATION
❖ Aim and Objectives
The main aim of this research is to measure and analyze power consumption using
Smart Meter data by conducting a case study on various households. The related
objectives are as follows:

Objectives
• To analyze the data on an hourly basis to understand the potential that much line-
grainedmeasurements can have on control of electricity consumption.
• To understand how to move demands in time so that the overall power consumption
becomes less varying and costly.
• To change people ‘s mind a bit more intelligent during the day for better
distribution of energy consumption.
• To select a good prediction model for predicting 24 hours ahead consumption
andcost.
• To flatten power distribution graph when abnormal electricity changes occur.

❖ Research Methodology

1. The research methodology involved in our research using case study and stages that
are followed for answering the research questions are as below:
In the first stage of the research, we have to perform a literature review related to
Smart Meters. The data which is measured using Smart Meters is obtained from an
energy provider.
The results which are obtained from data are plotted in the form of graphs and
observations are done regarding the consumption, price-cost, cumulative cost of the
household and further statistical analysis.
Particularly, in this stage the results are statistically summarized from the arrived data.
The flow of research methodology is shown in below figure.

18
2. In the second stage of the research, a prediction model is selected. Model matching
should be done after model selection, which is followed by validation. Different
household energy consumption and cost patterns can be modeled using ARIMA.
Various data sets are processed to obtain price-consumption correlations for observing
behavior of households using superposition.

3. In the third stage of research, a method of flattening consumption patterns is identified

and developed, aiming at flattening daily patterns and attempting to change the attitude
of consumers. Finally, conclusions are drawn from the analysis.

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4. CASE STUDY
An analysis of data involving a method of research is called case study. Some steps hadbeen
followed in the case study of our research. The steps are as follows:
1. In the first step of our study, the Smart Meter data is obtained from a local energy
provider that prefers to remain secret. This data was received by our supervisor.
2. This received data is passed to our team by supervisor for evaluating and analysing the
results. Smart Meter data is a validated one as it is obtained from an energy utility
provider.
3. The received data contains two sets of Microsoft Excel sheets.
a) The first excel sheet contain price values from days 1 to 30 of the month of April 2012.
The name specified for this sheet is ―Spot riser April 2012‖. We considered price values of
area 4, south of Sweden, which is named as
―SE4‖ in the sheet. The hourly price is varying for each day, i.e. each day has 24 different
prices.
b) The second sheet contains Smart Meter consumption data values of 16 households.
The 24-hourly consumption values per day and household are included in the Excel
sheet. The data in the sheet ranges from day 30 to day 1 of the month of April. We
considered time from 1:00 hour to 23:00 hour of the current day in the sheet as it is,
and 00:00 hour of next day which is considered as 24:00 hour of present day. This data
is carefully observed and noted in a separate Excel sheet. A careful observation is
needed when moving the data from one sheet to another, as skipping of data has a
great impact on the results. For reasons of anonymity and privacy, the 16
householdsare referred by a number from 1 to 16.
4. After reading the data, we need to interpret the real Smart Meter data in a new Excel
sheet. In the formulated sheet we arranged time (1:00 to 24:00 hour), price, energy
consumption and calculation of cost, cumulative cost, cumulative consumption, lag
1autocorrelations of price, cost, energy consumption and correlations of price-cost,
price-consumption and cost-consumption is determined.
5. Based on the results obtained, graphs are plotted for time versus consumption time
versus price, time versus cost and time versus cumulative cost. The time is plotted
on the x-axis and consumption is plotted on the y-axis for time- consumption
graph; time on the x-axis and price on the y-axis for time-price graph;time on the
x- axis and cost on the y-axis for time-cost graph; and lastly for time-
cumulative cost, time is plotted on the x-axis and cumulative cost is plotted on the
y-axis.
6. Correlation: It is a statistical measure of how two variables move in relation with
each other. It ranges between +1 and -1 [29]. They are of two types.

Positive correlation: A relationship between two variables which move in the same
direction i.e. as one variable decreases, the other variable also decreases and when one
variable increases the other variable increases is called positive correlation. In statistics,
the maximal value of positive correlation is represented by +1 [29].

20
Negative Correlation: A relationship between two variables which move in the
opposite direction i.e. as one variable decreases, the other variable increases and as one
variable increases, the other variable decreases is called negative correlation. Instatistics,
the maximal value of negative correlation is represented by -1 [29].

In addition to that computation of price-cost correlation, price-consumptioncorrelation

and cost-consumption correlation, average, standard deviation, and coefficient of
variation for price, cost and energy consumption is done.
7. The above computer steps are repeated for all days in a month per single household.
Similar graphs are generated for all 16 households from the results obtained.
8. The complete analysis can only be achieved with the help of another classified sheet.
So, we named those sheets as analysis succeeding with a number representing the
household.
In this determined sheet, we calculated real cumulative cost, cumulative cost
based on average price, difference of real cumulative cost and cumulative cost
based on average price. The correlation of price with consumption is done as
positive correlation is expected to yield high cumulative cost. A comparison of
cumulative based on hourly price with those based on average price is computed
because this reveals when hourly prices are disadvantageous/advantageous for
consumers. Two graphs are plotted, one with the number of the day on the x-axis
and price-consumption correlation on the y-axis represented as correlation graph,
another with difference between real cumulative cost and cumulative cost based
on average price on the y-axis and the number of the day on the x-axis
representing difference graph.
9. The maximum values of data such as price, peak power consumption and cost are
documented independently in sheets of ―Microsoft PowerPoint‖ for better
understanding.
Cross correlation: Cross correlation between consumption and price is a measure
to know how effective hourly charging was for the customer. It is a measure for
interdependencies.

▪ +100%: price and consumption follow each other either up/down

▪ □0: price and consumption are approximately independent
▪ -100%: price up implies consumption down and vice versa

• For positive correlation, there is a tendency that low prices occur together with
highconsumption.
• For negative correlation, there is a tendency that low prices occur together with
lowcost.
▪ The mentioned correlation in this research helps the reader with much easier and
quicker analysis.

21
10. A model search has been done, and the XLSTAT tool [40] is found to fit the
combination of model selection and prediction. Various ARIMA models are
tested by taking consumption data as a reference of one household and
comparison of all models is carried to search for the best fitted model
.
11. The identification of ARIMA model that fits best is finally chosen and graphed
for different cases. The parameters AIC and SBC of the model are quite low
andmode fits the original graph when compared to other models. According
to the standard statistics [24], the mentioned parameter values should be low.
Themodel with lowest AIC and SBC has the tendency to exhibit good results.
Moreover, different observation characteristics of the model also displayed
various impeccable outcomes. As adequacy, efficiency and accuracy strongly
reflects the nature of the model motivated us to use this model in our research
work.

12. Finally, flattening of the consumption pattern is observed.

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 5. RESULTS

Question: - What are the methods to measure and analyze the power consumption
of household applications in a real-time environment?
ANS: -The research question is split into two complementing directions of research as

follows: Methods to Measure:

There are multiple traditional ways of assessing the meter and its data. The better way
of
assessing the meter is implemented by using Smart electricity meters rather than opting
for traditional meters. The ancient and next generation methods of measuring energy are
explained in background work of this thesis paper. A literature review [2] has been
performed to answer the question. The presentation of this part of the research question
and its answer is mainly invoking an impressive knowledge for the people of
various developed and underdeveloped nations regarding the fundamental changes
among electricity market.
From the literature survey and final suggestions of research, it was found that Smart
Meter is the efficient meter to measure power consumption of household in real-time
environment.

Analysis:

We acquired real time hourly power consumption data of 16 households and prices for
April month from an electricity provider in the form of excel sheets. Time, price, cost,
consumption, cumulative consumption, cumulative cost are tabulated in excel sheet.
Lag-1 autocorrelation, price-cost correlation, price-consumption correlation, cost-
consumption correlation, average, standard deviation and coefficient of variationof
price, cost and
consumption are calculated. Each and every factor specified above is tabulated for easy
understanding. The data is arranged for all the 16 households and graphs are drawn for
consumption, price, cost and cumulative cost. The parameters that are required are
defined as follows.
• Power: Power is defined as the energy consumed per unit time.

Power = e/t

where e is energy consumption in KWh t is time

Cost (c): Cost is calculated as the product of price and energy consumption. The unit of
measurement is in monetary units.
c = p.e

where e is energy consumption in KWhp is price in monetary units/KWh

• Average (m): It is defined as sum of different quantities divided by the total number of
thesequantities. It is formulated as follows:
n

m = 1/n.□Xi
Where n is total number of terms

23
 Xi is value of each individual element
i = 1, 2, 3,----------n

• Standard Deviation (s): A measure of dispersion of a set of data from its mean iscalled
standard deviation. Standard deviation is calculated as square root of the
variance [16]. It is formulated as follows:

s= 1/(n□1). □(Xi□X)2
i□1
Where n is total number of terms
th
Xi is value of i terms
Xis mean
i = 1, 2, 3,---------n
□ Autocorrelation: A mathematical representation of the degree of similarity between a
given time series and a lagged version of itself over successive time intervals is
termed as autocorrelation [17].

Cumulative consumption (Ei): The cumulative consumption is the sum of hourly

consumptions during the first i hours of the day. The first value of cumulative
consumption is taken as it is from consumption.

Ei = e i + Ei-1
where Ei is cumulative consumption
i = 2, 3, 4, ---------- n
ei is consumption
Ei-1 is previous cumulative consumption
Cumulative cost (Ci): The cumulative cost is the sum of hourly costs during the first
i hours of the day. The first value of cumulative cost is taken as it is from cost.
Ci = ci + Ci-1
where Ci is cumulative cost at i hour
i = 2, 3, 4, ----------- n
ci is cost at i hour
Ci-1 is previous cumulative cost

□ Coefficient of variation: The ratio of standard deviation to average is called coefficient of

variation. It is formulated as follows:
Coefficient of variation = (Standard deviation / Average)

•
in the below figures byconsideringcumulativecoston the y-axis and time(24 hours) on
thex-axis.
Three graphs for consumption, price-cost and cumulative cost on a particular day of a fhirosutsehold,
which are interesting for some specific reasons, are shown below. This is the
step of evaluating multiple graphs for further references.

24
 Cumulative

Figure 5.1 Energy Consumption, Price, Cost and Cumulative cost of household 3 on JUNE 6

The energy consumption shown in the above figure 5.1 regarding household 3 declines from hour
8:00 to 12:00 and increases sharply from 12:00 to 13:00. The cumulative cost graph is almost linearly
increasing from 1:00 to 24:00 hours. The user should be smart enough to playa safer role in utilizing
energy efficiently by avoiding spikes.
From figure 5.2 regarding household 5, high energy consumption when compared to other
households are recorded at 17:00 hour on JUNE9. The price on JUNE 9 is much less varying. So the
price graph is observed as flat. As the user is consuming much energy, thecost factor is high even
though the price is low. From the cumulative cost graph, we infer that the curve is not increasing
linearly but becomes much steeper at high consumption hours.

25
 Figure 5.2 Energy Consumption, Price, Cost and Cumulative cost of household 5 on
JUNE 9

From the below figure 5.3 regarding household 6, complete flatten energy consumption is
observed with very low consumption on JUNE 6. As consumption is very low, cost is low as
well, as it is a product of price and consumption. Cumulative cost increases almost linearly from
1:00 to 24:00hours as the cost is not constant overtime.

From the below figure 5.4 regarding household 7, consumption shift between 0 and 1 is observed.
This behavior is different from the remaining households. This unusual behavior indicates the type
of meter that only counts multiples of kw. Here the user is cautioned about the power. The
cumulative cost is not increasing linearly but following a stepwise pattern.

26
Figure 5.3 Energy Consumption, Price, Cost and Cumulative cost ofhousehold 6 on JUNE6

Figure 5.4 Energy Consumption, Price, Cost and Cumulative cost ofhousehold 7 on JUNE 14

27
The graphs for energy consumption, price-cost and cumulative cost which are generated above can
help the users to study their daily electricity usage. As it is difficult to compare time plots, the
necessity of understanding the correlation is evaluated. The parameters obtained in the
correlation data sheet is defined in chapter 4. For the convenience of readers, we could only
present a specific set of household patterns, revealing somehow interestingbehaviors.
After analysis, we found that it would be quite interesting to compare the peak consumption
of all households during a particular hour in June 2023 on a single graph.

Figure 5.5 Peak energy consumptions of different households

By comparing consumption values of all 30 days in 16 households, high consumption for
each household is selected and plotted on a single graph as a reference. From the figure 5.5,
household 5 consumed maximum power when compared to other households.
Correlation of 16 Households:

The steps followed to calculate and analyse correlation are as follows:

• The price-consumption correlation, real cumulative cost, cumulative cost based on
average price and difference between real cumulative cost and cumulative cost basedon
average price is calculated for 30 days of all the 16 households.
• Two graphs are generated for each household (correlation and difference graph).
• Correlation graphs are plotted with the correlation on y-axis and number of days onx-axis.
• Difference graphs between real cumulative cost and cumulative cost based on average price
on the y-axis and number of days on the x-axis is plotted.
• By analyzing all the graphs and data we came to know that on days during which the
correlation between price and consumption is positive, the real cumulative cost is higher
than the cumulative cost based on average price, whereas days during which the
correlation between price and consumption is negative implies the real cumulative cost is
less than the cumulative cost based on average price.
• Real cumulative cost i.e. cumulative cost obtained by hourly price is denoted byHCum and
cumulative cost based on average price during the day is denoted by

28
 • ACum. A consumer with hourly pricing benefits if HCum is less than ACum and
loses if HCum is greater than ACum.
• Depending on the sign of the cross correlation between HCum and ACum,
we arriveat the following cases. Bad indicates hourly pricing leads to higher cost than
average
pricing. Good indicates hourly pricing leads to less cost than average pricing.
Positive HCum > ACum (Bad) Negative
HCum < ACum (Good)
Disappearing HCum □ ACum (Neutral)

The correlation of 16 households is executed, and some of the mostinteresting results are
displayed below.

forhousehold 1

From the figures 5.6 and 5.7 of household 1, we observe that days of negative correlation
imply that HCum is less than ACum, which means savings with hourly pricing. For this user,this happens on
the majorityofdays.

29
 Figure 5.9 Difference graph for household 3

From figures 5.8 and 5.9 of household 3, the consumption of user is partially negative and
partially positively correlated with the price. Maximum negative difference is observed on June
18, on which the user experiences a moderate step-down of cost.
From figures 5.10 and 5.11 of household 6, the consumption of user is positively correlated
with price. As HCum is greater than ACum results in positive difference, the user mostly loses
controlover consumption.

From figures 5.12 and 5.13 of household 10, the consumption of user is mostly experiencing positive
correlation with price where he can be advised to change his patterns.
From figures 5.14 and 5.15 of household 13, the consumption of user is negatively correlated
with price. This results in receiving negative differences. This kind of user is an inspiration to rest
ofthe users in controlling the energy.

30
ss

household 10

10

31
32
6. DISCUSSION
With all the obtained data from the statistical analysis, the following results are implied:

A close in□section is done from the obtained data and results confirms that the usage the
electricity by most of the households is high during the beginning, middle of month and low
d u r in g the end of the month. Nevertheless, this strongly
□
reflects
the real characteristics of people ‘s attitude towards electricity.
Graphs are generated to state the performance and analysis of 16 households for
consumption, price-cost and cumulative cost. The patterns of graphical analysis suggest
consumers serve a competitive nature among them. With computation.
of price-consumption correlation results, we can observe that more negative the correlation, the
lower will be the cost while as more the positive correlation higher
will be the cost if hourly pricing was applied.
Several models were observed, and a precise selection is done on the basis of
goodness of fit statistics. An idea and a methodology of using seasonal ARIMA for predicting□
future electricity prices and cost have been proposed. Resource
utilization
can be effectively improved with the observation of distributed statistics. The unstable behaviour of
users is noticed and compared from the prediction analysis. graphs are
shown in the analysis. The graphs generated for this can bring people to adopt changesrequested
in the paper.
The correlations in the superposition can make people to imitate and defend to control their future
statistics. As it is hard to find a typical user, it is also difficult.
predict a typical user ‘s behavior.
Therefore, to study the patterns of households a flattening technique is

proposed to
save the cost of the consumer.

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7. CONCLUSION AND FUTURE WORK

The implementation of smart energy meters in Genus Company has led to several positive
outcomes, including:
• Increased energy efficiency: Smart energy meters have enabled Genus Company to identify
and address energy inefficiencies, resulting in a reduction in energy consumption.
• Reduced costs: By reducing energy consumption, Genus Company has been able to save money
on its energy bills.
• Improved customer service: Smart energy meters have provided Genus Company with
valuable data that can be used to improve customer service. For example, the company can
use this data to identify customers who are using excessive amounts of energy and provide
them with tips on how to conserve energy.
• Increased customer satisfaction: Customers have been generally satisfied with the
implementation of smart energy meters. They appreciate the ability to track their energy usage
and receive real-time feedback on their consumption habits. Genus Company plans to
continue to invest in smart energy technologies. The company is currently exploring the
following opportunities:
• implementing smart grids: Smart grids are a type of electrical grid that uses information and
communication technologies to improve the efficiency, reliability, and sustainability of the
power grid. Genus Company believes that smart grids have the potential to further reduce
energyconsumption and costs.
• `Developing demand-response programs: Demand-response programs encourage customers
to shift their energy usage during peak periods. This can help to reduce strain on the power
grid and prevent blackouts. Genus Company is currently developing a demand-response program
thatwill offer customers incentives to reduce their energy usage during peak periods.
• Investing in renewable energy: Genus Company is committed to reducing its environmental
impact. The company is currently investing in renewable energy sources, such as solar and wind
power. Genus Company believes that renewable energy is essential to a sustainable future.

34
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