Industrial Training Report

Practical Training acquired

At

GENUS POWER
INFRASTRUCTURE
LIMITED
(Jaipur)
Submitted in partial fulfillment for the award of degree of
Bachelors of Technology

Department of Electrical
Engineering

Submitted By:-
Rajat Shrivastava
22UELE6042

Department of Electrical Engineering

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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 the irrespective 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 for saving 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 a coordinated 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 confidence to 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
5.6 Correlation graph for household 1

5.7 Difference graph for household 1

5.8 Correlation graph for household 6

5.9 Difference graph for household 6

5.10 Correlation graph for household 10

5.11 Difference graph for household 10

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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 determine consumption 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 peoplein 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 2 Wire electricity meter is shown in the below figure2.1.

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Some of the limitations faced by the traditional electricity meter are as follows:
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 called electromechanical 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 for encouraging 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 electricity consumption, statistics of the consumption couldn‘t
be changed.
Some basic types of meters are explained as follows: -

Different Types Outline

The whole current passes through the electrolyte. The

Electrolytic Meter
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

Mercury Motor of 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

D.C Watt Hour the temperature coefficient is high. For indication of demand
purposes, a D.C Watt Hour separate time switch is used. Also, it
is a clock-type meter in which Meter voltage variations is less
with the reduced shunt loss.

Magnetic conditions are better improved to control energy

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

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 Lagging power factors in the meter reflect the characteristics of
Poly-Phase Watt the current transformer. Attempts for improving a high degree of
Hour Meter accuracy have 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. A smart 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

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 metering allows an improved management and control over the electricity grid .

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 current level 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 ofdevices 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 ofthe customer. In-fact most of the consumers rely on the
monthly bill they expectfor. 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.

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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 are
discussed 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 order of seasonal moving average part and s is the number of time periods of
seasonal cycle.
Different seasonal ARIMA models are:

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

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ARIMA (1, 0, 1) (0, 1, 1)12: First-order autoregressive term and moving average term
in the non- seasonal part and first-order moving average term in the seasonal part with
seasonal differencing 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
of the period (s), while the non-seasonal ARIMA does it in terms of its past values at lag 1 .
Non-Seasonal ARIMA model: A non-seasonal ARIMA model is represented as ARIMA.

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.

ARIMA (1, 1, 1): A mixed model of autoregressive and moving average

terms of order 1with differencing of order 1.

ARIMA (1, 1, 0): First order autoregressive term with non-seasonal differencing
of order.
ARIMA (0, 1, 1): First order moving average term with non-seasonal
differencing of order1.

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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-economic 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 consumption using
Smart Meters is done. Answers are proposed for a set of research questions
such as

1) Is feedback useful for energy saving and behavioral change?

2) What presentation of feedback is good and effective?

Scientific advice on energy saving instruments for household energy

consumption is provided in. A Smart Metering privacy model is implemented
to measure the privacy that a Smart Meter will provide with and without
involvement of third parties. The advantages of Smart Metering concept are
low metering costs, energy efficiency and easier detection of fraud.

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.

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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. An overview of Smart
Metering installations, implementations, and functionality which is installed in
the Netherlands is given in.
In , 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, 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 , 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.

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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.

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
graphical form. 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 is 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 is needed 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].

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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 and cost.
 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.

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2. Inthe 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. Inthe 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
had beenfollowed 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 ―Spotpriser 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 isconsidered
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 greatimpact on the results.
For reasons of anonymity and privacy, the 16 households are 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.

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1. 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.
2. 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].

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. In statistics, the maximal value of negative correlation is represented
by -1 [29].

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

correlation and cost-consumption correlation, average, standard deviation, and
coefficient of variation for price, cost and energy consumption is done.
3. 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.
4. 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-

23
 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.
5. 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 togetherwith high
consumption.
 For negative correlation, there is a tendency that low prices occur togetherwith low
cost.
 The mentioned correlation in this research helps the reader with much easier
and quicker analysis.

6. 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 allmodels is carried to search for the best fitted model
.
7. The identification of ARIMA model that fits best is finally chosen and graphed
fordifferent cases. The parameters AIC and SBC of the model are quite low and
mode fits the original graph when compared to other models. According to
the standard statistics [24], the mentioned parameter values should be low. The
model 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.

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

24
 5. RESULTS

Question: - What are the methods to measure and analyze the power consumption
of householdapplications 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] hasbeen
performed to answer the question. The presentation of this part of the research question
andits answer is mainly invoking an impressive knowledge for the people ofvarious
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 asfollows.
 Power: Power is defined as the energy consumed per unit time.

Power = e/t

where e is energy consumption in KWht 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 numberof these
quantities. It is formulated as follows:
n

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

25
 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].
n□kn
2
y y y rk □ (yi ) i+k )) / (yi )
□ □
=(
i
(y - □ -
1
- i
1

where rk is lag k autocorrelation

i = 1, 2, 3,---------n
k = 1, 2, 3,---------n
n is total number of terms
yis average of n terms

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
th
where Ci is cumulative cost at i hour
i = 2, 3, 4,-----------n
th
ci is cost at i hour
Ci-1 is previous cumulative cost

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

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

26
 □The product of average price and larger value of cumulative consumption is calledcumulative cost based
on average price. It is formulatedas follows:
ACumE.= p
Where ACum is cumulative cost based on average price
24

ei E=□
□ i1
24

p= 1/24 □
pi
i□1 □Thesum of productofconsumptionandpriceinaparticular houriscalled
cumulative cost based on hourly price. It is formulated as follows:
24

H = □ei .pi
i□1
Where HCum is cumulative cost based on hourly price
i is hour of the day
E is sum of consumption of a day
ei is consumption of particular hour
pi is price on particular hour
pis average price

Based on the systematically analyzed data, the graphs are drawn as follows. The selected graphs are
displayed in the report as the data is interesting.
 Energy consumption graph of some households are shown in the below figures
byconsidering consumption (KWh) on they-axisand time(24 hours) on thex-axis.
 □ Price and cost graph of some households are shown in the below figures by
consideringprice, cost on the y-axis and time(24 hours) on the x-axis.
 Cumulative cost graph of some households are shown in the below figures
byconsidering cumulativecoston 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.

27
 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.

28
 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.

29
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

30
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

31
  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.

32
 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 mostlyloses
controlover consumption.

From figures 5.12 and 5.13 of household 10, the consumption of user is mostly experiencingpositive
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 mcorrelated
with price. This results in receiving negative differences. This kind of user is an inspirationto
rest ofthe users in controlling the energy.

33
ss

household 10

10

34
35
6. DISCUSSION
With all the obtained data from the statistical analysis, the following results areimplied:

A close in□spection 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 ur ing 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 behaviour.

Therefore to study the patterns of households a flattening technique is

proposed to
save the cost of the consumer.

36
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 reductioninenergyconsumption.
 Reduced costs: By reducing energy consumption, Genus Company has been able to save
money onitsenergybills.
 Improved customer service: Smart energy meters have provided Genus Company with
valuable data that can be used to improve customer service. Forexample, the company can
use this data to identify customers who are usingexcessive amounts of energy and provide
them with tips on how to conserveenergy.
 Increased customer satisfaction: Customers have been generally satisfied with the
implementation of smart energy meters. They appreciate the ability to tracktheir 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 usesinformation and
communication technologies to improve the efficiency, reliability, and sustainability of the
power grid. Genus Company believes thatsmart grids have the potential to further reduce
energy consumption and costs.
 `Developing demand-response programs: Demand-response programs encourage customers
to shift their energy usage during peak periods. This canhelp to reduce strain on the power
grid and prevent blackouts. Genus Company is currently developing a demand-response
program that will offer customersincentivesto reducetheir energy usageduring peak periods.
 Investing in renewable energy: Genus Company is committed to reducing itsenvironmental
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.

37
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