Easy Mobile Meter Reading for Non-smart Meters: Comparison of AWS

Rekognition and Google Cloud Vision Approaches

Maria Spichkova, Johan van Zyl, Siddharth Sachdev, Ashish Bhardwaj and Nirav Desai

School of Science, RMIT University, Melbourne, Australia

Keywords:

Software Engineering, Computer Vision, Google Cloud Vision, AWS Rekognition.

Abstract:

Electricity and gas meter reading is a time consuming task, which is done manually in most cases. There

are some approaches proposing use of smart meters that report their readings automatically. However, this

solution is expensive and requires (1) replacement of the existing meters, even when they are functional and

new, and (2) large changes of the whole system dealing with the meter readings. This paper presents results

of a project on automation of the meter reading process for the standard (non-smart) meters using computer

vision techniques, focusing on the comparison of two computer vision techniques, Google Cloud Vision and

AWS Rekognition.

1 INTRODUCTION

There are many approaches proposing smart devices

for several types of utilities, see e.g., Depuru et al.

(2011); Benzi et al. (2011); Zheng et al. (2013). Smart

meters can record energy consumption and automat-

ically send the corresponding data to the electricity

supplier for monitoring and billing purposes. This

solution is deﬁnitely useful and has many beneﬁts,

from increasing the sustainability to offering poten-

tial beneﬁts to householders. However, implementing

it in a large scale in real life is expensive, e.g., the

costs of the transition program for Australia were es-

timated to be a total cost of $ 1.6 billions. Many cus-

tomers prefer not to upgrade their non-smart meters

to a smart version, when the costs of this upgrade are

out of their pocket. For example, in Australia, differ-

ent energy providers may have different approaches

to how they charge their customers for this change –

either as a lump sum that is added to the ﬁrst bill af-

ter the upgrade or a higher monthly fee – but in all

cases the costs are beared by the customers. Also, the

use of smart meters raised privacy concerns from the

consumers’ side: as they typically record energy con-

sumption on the hourly basis or even more frequently,

and report it to the system at least daily, this informa-

tion might be used to identify whether the residences

are at home or not, etc.

Therefore, many countries delay the transition to

the smart meter systems or purpose a partial transi-

tion. Thus, another solution is required for this case,

as to do the electricity and gas meter reading com-

pletely manually is extremely time consuming. We

proposed a solution for non-smart meters, which is

based on computer vision approaches. This solution

provides an easy way for customers to upload meter

readings to their system.

The project was conducted in collaboration with

Energy Australia, which is an electricity and gas

retailing private company in Australia. It supplies

electricity and natural gas to more than 2.6 million

residential and business customers throughout Aus-

tralia. Our goal was to provide a convenient alter-

native method for their current meter reading updat-

ing system. The current solution from Energy Aus-

tralia involves consumers using updating their utility

reading through using an online portal. This method

is inconvenient for consumers as consumers need to

provide intricate entry details. Consumers are also

required to calculate their utility reading from their

meter. The proposed solution is to use a mobile appli-

cation for capturing readings, a cloud-system to man-

age readings and a blockchain technology, see Zheng

et al. (2018); Michael et al. (2018); Swan (2015), to

store reading securely.

Contributions: This paper presents (1) the archi-

tecture and implementation details of the proposed so-

lution, as well as (2) the comparison of two computer

vision technologies, Google Cloud Vision

and Ama-

https://cloud.google.com/vision

Spichkova, M., van Zyl, J., Sachdev, S., Bhardwaj, A. and Desai, N.

Easy Mobile Meter Reading for Non-smart Meters: Comparison of AWS Rekognition and Google Cloud Vision Approaches.

DOI: 10.5220/0007762301790188

In Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019), pages 179-188

ISBN: 978-989-758-375-9

179

zon Web Services (AWS) Rekognition

, applied for

recognition in utility meter readings. As the majority

of the currently used meters have digital displays (the

old versions were of dial type) we focused on this type

of displays as well as on digit recognition analysis.

The system was elaborated within a research

project under the initiative Research embedded in

teaching, proposed at the RMIT University, see

Spichkova and Simic (2017); Simic et al. (2016). To

encourage curiosity of Bachelor and Master students

to the research in Software Engineering, we suggested

to include research and analysis components in the

projects as a bonus task. Short research projects

have been sponsored by industrial partners and fo-

cused on the topics related to the project to conduct

within semester. These have to be conducted after the

semester end, focusing on research prospective and

deeper analysis of the semester task, see for example

Spichkova et al. (2018); Sun et al. (2018); Spichkova

(2018); Christianto et al. (2018); Clunne-Kiely et al.

(2017).

Outline: The rest of the paper is organised as fol-

lows. Section 2 introduces related work. The method-

ology we applied to compare AWS Rekognition and

Google Cloud Vision technologies, as well as the re-

sults of the conducted study are discussed in Sec-

tion 3. The proposed and implemented system is pre-

sented in Section 4. Finally, Section 5 summarises the

paper and proposed future work directions.

2 RELATED WORK

Many research works on elaboration of auto-

mated/remote meter reading were conducted even ap-

prox. 20 years ago. There are also a number of cor-

responding patents. For example, Kelley et al. (2000)

patented an automated meter reading (AMR) system

with distributed architecture that collects, loads, and

manages system-wide data collected from energy me-

ters and routes the data automatically to upstream

business systems.

Nap et al. (2001) patented an automatic me-

ter reading data communication system that has

an integrated digital encoder and two-way wireless

transceiver that is attachable to a wide variety of util-

ity meters for meter data collection and information

management. Many other systems with similar ideas

were patented Jenney et al. (1999); Knight and Banks

(1998); Ehrke et al. (2003), but the research area is

still very active, see e.g., Grady et al. (2016); Winter

(2017).

https://aws.amazon.com/rekognition

However, the majority of works in this area last

years focus on the following aspects:

• Application of the data mining and data analytics

techniques on the meter reading data.

Thus, Rathod and Garg (2016) presented an elec-

tricity consumption analysis for consumers using

data mining techniques applied to meter reading

data. Xiao et al. (2013) proposed an approaches

to recognise energy theft based on the analysis of

meter data.

• Design of smart energy meter for the smart grid,

where a smart greed is a next generation power

grid having a two-way ﬂow of electricity and in-

formation, see Yan et al. (2013b) for more details

on smart grids.

Zheng et al. (2013) presented an overview of typi-

cal smart meter’s aspects and functions wrt. smart

grid aspects.

Kuzlu et al. (2014) analysed communication net-

work requirements for smart grid applications.

Yaacoub and Abu-Dayya (2014) proposed an ap-

proach on automatic meter reading in the smart

grid using contention based random access over

the free cellular spectrum.

Arif et al. (2013) conducted a study on design and

development of smart energy meter for the smart

grid.

• Privacy and security aspects of smart meters are

studied especially intensively over the last years,

as the privacy and security concerns provide one

of the biggest obstacles for the (potential) users of

smart meters.

Yan et al. (2013a) proposed a security protocol for

advanced metering infrastructure in smart grid.

Sankar et al. (2013) proposed a theoretical frame-

work to analyse privacy aspects of smart meters.

Albert and Rajagopal (2013) and Beckel et al.

(2014) discussed what the consumption patterns

derived using the smart meters might say about

the consumers.

Chen et al. (2013) presented an approach for non-

intrusive occupancy monitoring using smart me-

ters, having a goal to implement energy-efﬁciency

optimizations based on the information of home’s

occupancy. Other approaches for occupancy de-

tection from electricity consumption data were

proposed by Kleiminger et al. (2013); Yang et al.

(2014); Masoudifar et al. (2014); Chen et al.

(2018) and Tang et al. (2015).

Tan et al. (2013) proposed a solution to increase

the smart meter privacy through energy harvest-

ing and storage devices.

Eibl and Engel (2015) analysed the inﬂuence of

data granularity on smart meter privacy as well

ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering

180

(a) (b) (c)

Figure 1: Blurring effect: (a) 30BLUR, (b) 60BLUR, (c) 90BLUR.

(a) (b) (c)

Figure 2: Gamma correction effect: (a) 0.25GAMMA, (b) 1.5GAMMA, (c) 3.0GAMMA.

as what granularity should be used to prevent the

interference of personal data from load proﬁles

by using non-intrusive appliance load monitoring

methods. Another approach for preventing occu-

pancy detection from smart meters was proposes

by Chen et al. (2014, 2015).

Eibl et al. (2015) elaborated a set of use cases

for Smart Metering, formulated in a way that is

suitable for the development of privacy enhancing

technologies.

Eibl et al. (2018) also presented a study on holiday

detection from energy consumption data based on

low-resolution smart meter data.

Burkhart et al. (2018) conducted a study where

swimming pools were detected through their ﬁlter

pumps in load data with the 15-minute granularity

prescribed by the European Union for smart me-

ters, which demonstrates how vulnerable the pri-

vate information might be through access to the

meter readings data.

3 AWS REKOGNITION VS.

GOOGLE CLOUD VISION

To implement the proposed system, the were select-

ing between two computer vision technologies, AWS

Rekognition vs. Google Cloud Vision. In the below

sections we present the methodology of the compari-

son as well as the details of the conducted study.

3.1 Methodology

Reading utility meters involves several challenges for

application of computer vision technologies: reﬂec-

tion from the meters’ glass, clipped digits, additional

text on the meter that does not belong to the actual

meter reading, blur, noise, as well as cases, where a

meter has digital representation style for some read-

ings but dial representation for other.

Images for the evaluation data set were selected

based on their “uniqueness” – images with unique

meters or images with unique lighting. A total of 30

images were selected. This set of images were dupli-

cated and modiﬁed with various effects in order to test

the limitations of the different technologies. These ef-

fects are:

• Scaling: The data set was scaled in steps of 0.1

ranging from a scale of 0.1 to 0.9 (10% to 90%)

of the original data set.

• Blurring: Blurring was done in steps of 10 from

10 to 90 with an open source blur algorithm that

is based on the normalised box ﬁlter, see OpenCV

(2018). The algorithm uses a normalised box ﬁl-

ter, the numeral value adjusts the kernel size. Fig-

ures 1(a)–1(c) present examples of blurring appli-

cation with 30BLUR, 60BLUR, and 90BLUR, re-

spectively.

Easy Mobile Meter Reading for Non-smart Meters: Comparison of AWS Rekognition and Google Cloud Vision Approaches

181

10%

20%

30%

40%

50%

60%

Original

0.8 Scale

0.6 Scale

0.4 Scale

0.2 Scale

3.00 Gamma

2.50 Gamma

2.00 Gamma

1.50 Gamma

0.75 Gamma

0.25 Gamma

0.08 SP

0.16 SP

20 Blur

40 Blur

60 Blur

80 Blur

Average

GCV Accuracy(%)

Figure 3: Google Cloud Vision (GCV) Accuracy.

10%

20%

30%

40%

50%

60%

Original

0.8 Scale

0.6 Scale

0.4 Scale

0.2 Scale

3.00 Gamma

2.50 Gamma

2.00 Gamma

1.50 Gamma

0.75 Gamma

0.25 Gamma

0.08 SP

0.16 SP

20 Blur

40 Blur

60 Blur

80 Blur

Average

AWS R ekog nit ion Accuracy(%)

Figure 4: AWS Recognition Accuracy.

• Gamma: The gamma algorithm was used with

an open source lookup table algorithm OpenCV

(2018). The gamma correction to simulate

different lightning conditions. Figures 2(a)–

2(c) present examples of gamma algorithm ap-

plication with 0.25GAMMA, 1.5GAMMA, and

3.0GAMMA, respectively.

• Noise: The noise algorithm is based upon the

salt and pepper noise algorithm that adds sharp

and sudden disturbances in the image in the form

of sparsely occurring white and black pixels, see

Gonzalez and Woods (2001). This algorithm was

included to further test the performance of the

various technologies as noise arguably emulates

“dirt” on meters.

We calculated the accuracy of recognition calcu-

lated as the following simple formula (we measure the

accuracy in percents, where 100% means a totally ac-

curate recognition):

Accuracy =

CorrectResults

Total

∗ 100 (1)

where

CorrectResults is the number of results that match

with the original readings completely,

Total presents the total number of images in data set.

In our study, we had 30 images in each of the data

sets.

3.2 Results of the Study

The results of the conducted study are summarised on

Figures 3 and 4 for Google Cloud Vision and AWS

Rekognition, respectively. The bar Original presents

the recognition results for the original data set. For

this case, Google Cloud Vision has performed slightly

better than AWS Rekognition having a 3% higher ac-

curacy.

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182

Figure 5: Solution Architecture.

Scale Data Set: There is a variation of 10% in the

accuracy of the two models. AWS Rekognition has

an overall higher efﬁciency than Google Cloud Vision

with the former performing 10% better than the latter

in every iteration. As the value of scaling is increased,

accuracy is also increasing.

Gamma Dataset: The variation between the two,

in this case, is almost negligible, as both provide an

accuracy of approx. 40%. SP Dataset: AWS Rekogni-

tion outperforms Google Cloud Vision with over 20%

margin in accuracy. As the value of SP increases, so

does the accuracy.

Blur Dataset: This dataset proved to be a chal-

lenge for both the models, with AWS Rekognition

reaching a top accuracy of 50% whereas the Google

Cloud Vision only reached around 37% when blur

level is 10. It dropped down to almost 0% when

it reached around 40% blur in Google Cloud Vision

and 90% blur in the case of AWS Rekognition. Even

with higher blurred images, AWS Rekognition is able

to detect some readings, unlike Google Cloud Vision

where accuracy is 0%.

Thus, on average, AWS Rekognition was able to

perform approx. 7% better than Google Cloud Vision

when same data set was provided.

4 PROPOSED SYSTEM

Figure 5 presents the solution architecture for the

proposed system, where computer vision approaches

are applied to capture meter readings using mobile

phones. These readings should then be passed on to

the core system to update consumer utility-charges

accordingly. Consumers should then be able to

view their renewed charges and usages in an internet

browser. Thus, the mobile application is used to cap-

ture, upload and store an image of the meter to the

system.

The system will then analyse this image to iden-

tify meter readings and return the readings’ values

back to the user for conﬁrmation. Once the user has

conﬁrmed the meter reading, it will be stored on a

blockchain.

The proposed system has two core components

providing interfaces for two user types:

• an Android application developed for customers;

the application was built using React Native,

which provides cross-platform compatibility be-

tween Android and iOS platforms (thus, develop-

ment of an iOS version of the app will be less

time-consuming);

• a Web application developed using ReactJS for

admin users to audit the meter readings.

Mobile application and web application acts as a

clients and call back-end APIs (application program-

ming interfaces) running of Spring Boot. which

is deployed on Amazon Web Services Elastic

Beanstalk Services (2018). AWS Elastic Beanstalk

reduces complexity without restricting choice or con-

trol, as it automatically handles the details of capacity

provisioning, load balancing, scaling, and application

health monitoring.

An example of a Web application page is pre-

sented in Figures 6. Figure 7 presents an examples

of the mobile application pages.

Spring Boot APIs are secured using JSON Web

Token OAuth 2.0 security. The back-end uses Post-

greSQL and Hyperledger Blockchain

to store data.

Amazon Web Services (AWS) Rekognition is used

to get the meter reading from the meter image. The

choice of the computer vision technology is justiﬁed

by the study presented in Section 3.

https://www.hyperledger.org

Easy Mobile Meter Reading for Non-smart Meters: Comparison of AWS Rekognition and Google Cloud Vision Approaches

183

Figure 6: Web Application (Admin): Meter reading results.

Figure 7: Mobile Application (Customer View): Capturing

an image of a meter.

When a customer using the mobile application clicks

an image of the meter (the application uses viewﬁnder

technology as shown in Figure 7), a Spring Boot API

will be called to ﬁlter out the meter readings from the

image and to forward the result to AWS Rekognition,

which returns all the text at the Spring Boot level. Fig-

ure 8 presents an algorithm we elaborated to ﬁlter out

all irrelevant data and return only the relevant results

back to the mobile application. The API takes the im-

age URL and the storage bucket (S3) name from the

client and returns the meter reading. Firstly, image

is fetched from the URL and the bucket name, then

the image is passed to the AWS Rekognition library,

which is applied to identify all the text on the image.

The algorithm further ﬁlters out all irrelevant text by

considering the user’s last meter reading or the initial

meter reading, which was added to the system when

the corresponding account was created. If the algo-

rithm unable to return the scanned meter reading, it

simply returns the last meter reading to the user, so

that user has to change only the minimal number of

digits.

If the customer is satisﬁed with the image recogni-

tion results, the customer submits the meter reading,

thus, another API will be called which stores the im-

mutable data into Blockchain and mutable data into

PostgreSQL database. The administrator can use the

Web application to audit the meter readings at any

time. Web application also calls Spring Boot APIs

to get all customer details and their meter readings.

The blockchain also contains an interface from

which the cloud-system can interact with. The cloud-

system provides a portal for administrators, where

they can review customer meter readings through dis-

playing previously uploaded images along with their

respective geo-location coordinates. These features

ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering

184

Figure 8: Results Reﬁnement Algorithm for Image Recognition.

provide Energy Australia with a manual method of

detecting falsiﬁed readings.

The blockchain component consists of three

nodes, see Figure 9: Customer Node, EA (Energy

Australia) Node and Orderer Node; deployed using

docker containers on three individual EC2 instances

running on Ubuntu 16.04 Xenial Xerus. The peers

are part of the Fabric and represent the node on the

blockchain. Each Node has its own version of the

Ledger using LevelDB. Each node also consists of

MSP (Membership service provide) docker container

used to provide signatures and certiﬁcates to new join-

ing entities. Node.js is used on all the instances to

expose the APIs for backend to interact with the Net-

work.

When an update is made to the meter reading by a

customer, it is sent by the customer node to the chan-

nel for veriﬁcation. The EA node in this case acts

as an endorser to verify the validity of the transac-

tion. The requested transaction is executed on the en-

dorsers’ version of the ledger. Once it is successful,

the transaction for meter reading update is signed and

sent back to the customer node. This signed transac-

tion is then sent to Orderer. Orderer will verify the en-

dorsed signature and wait for the next block to come

up. Once a block is available it will update the me-

ter reading and attach this block to the ledger. The

block is then sent to all the nodes for inclusion in the

Ledger.

Docker

containers were used to launch the in-

stances on to AWS EC2 instances. In this case, a

docker container consist of six docker images: for

Customer, for EA (Energy Australia), for Orderer, for

Chaincode, for EAMSP (Energy Australia Member-

ship Service Provider) and for Customer Membership

Service Provider. The Chaincode docker consists of

the channel on which the nodes are interacting and

the latest version of Chaincode installed and instan-

tiated. A simple web page is hosted to display the

amount of transaction that have been committed to

the ledger along with other network speciﬁcations. A

shell bash script was written for each AWS EC2 in-

stance to quickly generate all the artefacts required

for Blockchain, to quickly setup and tear down the

network for testing and development and ﬁnally for

deployment.

5 CONCLUSIONS

In this paper, we presented the core results of a re-

search project conducted in collaboration with Energy

https://www.docker.com

Easy Mobile Meter Reading for Non-smart Meters: Comparison of AWS Rekognition and Google Cloud Vision Approaches

185

Figure 9: Blockchain Architecture.

Australia, an Australian electricity and gas retailing

company. The goal of the project was to provide a

convenient alternative method for their current me-

ter reading updating system focusing on non-smart

meters. We implemented the proposed system as a

cloud-based solution that applies

• computer-vision technology to identify the meter

readings automatically,

• blockchain technology to store the meter reading

securely.

We conducted a study to compare two computer vi-

sion technologies, Google Cloud Vision and AWS

Rekognition, applied for recognition in utility meter

readings. The study demonstrated that AWS Rekog-

nition provides better results for our application do-

main. Thus, AWS Rekognition was applied within

the proposed system.

The developed system has two interfaces:

• the customer interface: a mobile application for

automated capturing meter readings and manag-

ing the account details, such as customer’s ad-

dress, contact details, as well as the core details

on the electricity and gas meters belonging to the

customer;

• the administrator interface: a web application for

management customers’ accounts, details on the

electricity and gas meters (including geo-location

of the meters), as well as the stored images of the

meter readings.

Future Work: As a possible future work direction

we consider extending the proposed system to allow

incorporation of data from smart meters. This exten-

sion would provide a better overview of customers’

data for further management and analysis, covering

the data collected from both old (non-smart) and new

(smart) meters.

Another direction of our future work is to investi-

gate further computer vision technologies, as the av-

erage accuracy values of Google Cloud Vision and

AWS Rekognition applied for recognition in utility

meter readings were not high. We consider to conduct

a study to analyse the following technologies, also

applied for recognition in utility meter readings: an

open-source Tensorﬂow technique Abadi et al. (2016,

2017) and a commercial solution Anyline.

ACKNOWLEDGEMENTS

We would like to thank Shine Solutions Group Pty Ltd

for sponsoring this project under the research grant

RE-03615. We also would like to thank Energy Aus-

tralia for collaboration in this project. We also would

like to thank the experts from the Shine Solutions

Group, especially Aaron Brown and Alan Young for

numerous discussions as well as their valuable advice

and feedback.

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