Identifying Design Criteria for Visualizing Household

Identifying Design Criteria for Visualizing Household Electricity Consumption: A User-Centred Survey Latha Karthigaa Murugesan

a Department

a,∗

a

, Rashina Hoda , Zoran Salcic

a

of Electrical and Computer Engineering, The University of Auckland, New Zealand

Abstract Household energy consumption accounts for up to one-third of a country's yearly energy consumption. Home automation, energy visualisation and energy feedback are likely to impact energy eciency by: notifying the energy consumers with their up-to-date energy consumption; advising more energy ecient behaviours; and actively changing/modifying user actions automatically or manually for reducing energy wastage.

However, eliciting the design criteria for

designing eective visualization of the household energy consumption remains non-trivial. It is likely that the design criteria would be more eective when the requirements are elicited directly from the energy consumers/users themselves. Hence, a user-centred survey is designed to identify and understand both the functional and non-functional design requirements. A total of 137 energy users over 18 years of age participated in the survey. The key functional design criteria identied are aggregated real-time energy data and disaggregated energy data and the main non-functional design criteria are portability and scalability. Also, these design criteria are compared with the results of a systematic literature review and new sets of design criteria are proposed, which would help in developing an eective, easy-to-understand visualization which would be more likely to be used and promote positive behavioural changes.

Keywords:

Energy consumption, User centred design, visualization, SLR

1. Introduction The need to visualize, analyze and understand energy consumption in relation to household energy patterns plays a vital role in energy consumption reduction [8].

A home which is automated to inform the users about energy

consumption is well equipped to achieve that aim.

The automated feedback

in such a home must be easy to read, more likely to be used and also promote positive behavioural changes [5] which start reecting on the users' energy

∗ Corresponding

author

[email protected] (Latha Karthigaa Murugesan), [email protected] (Rashina Hoda), [email protected] (Zoran Salcic) Email addresses:

Preprint submitted to Elsevier: Energy and Buildings

October 9, 2016

2

consumption.

To be easily read and more likely to be used it is highly im-

portant that the design must be user-centred [1]. Designing such ecient and usable systems is non-trivial and demands establishing a clear set of design criteria based on a deep understanding of user requirements. User-centred design (UCD) is a process in which the end-users of the product or service are involved in the various stages of the software design and development.

For example,

the end-users are involved in collecting the software requirements, designing the software, validating the assumptions made by the designers, etc. Some of the important advantages of UCD are (a) increased user satisfaction, (b) reduced redesign costs, and (c) improved performance [11]. The rst step in a software development process is to identify the project requirements, both functional and non-functional.

UCD can be applied to elicit the design decisions or de-

sign criteria (i.e., functional and non-functional requirements) by means of user centred surveys and focus groups from representative user groups. This article presents the results of an online user-centred survey completed by 137 energy users over 18 years of age to obtain the design criteria for visualizing household energy consumption. We asked the users to ll out a survey by responding to a set of carefully designed questions, aiming at understanding: (a) The suitable platform for developing the software application for visualizing household energy consumption, e.g., mobile phones, laptops, tablets, etc.

(b) The user-

preferred-metrics to be used for visualizing the household energy feedback, e.g., feedback with energy consumption (kWh) and price (in $) as metrics. (c) Their primary reasons for saving energy, thereby improving the visualization to tap into user motivations.

(d) The need for smart electricity tips and the most

suitable medium and methods for conveying the notications. The rest of the paper is organized as follows: Section 2 details the related work in this research area. Section 3 illustrates the survey method followed by results and discussion in sections 4 and 5, respectively. Section 6 presents the conclusion and possible future work.

2. Related Works Household energy consumption and the corresponding user behaviours are being investigated by several researchers worldwide with the aim of understanding the energy usage patterns and to encourage more energy-ecient behaviours [10]. Energy consumption monitoring is a human-related task which demands user-centred approaches for ecient functioning [2]. One of the earliest works in this eld is a 2004 survey [16], which was used to assess the users' preferences for energy feedback and had provided information about household energy consumption. Another study [15] was conducted to understand consumer awareness of energy consumption at home and to determine the requirements for visualizing household energy consumption. They discovered the following nonfunctional requirements, privacy, readability and accessibility and that there had to be a balance between the readability and aesthetics in energy consumption feedback.

Dario et.al.

[5] conducted a user survey to understand what

feedback is perceived by users as easier to understand, more likely to be used

3

and more eective in promoting the behaviour changes. The survey identied that clear real-time electricity feedback results in maximum energy savings. Although several studies concentrated on user-centred survey, very few studies focus on identifying both the functional and non-functional requirements of the visualization software to be developed. This user-centred survey is designed to identify a combination of functional and nonfunctional requirements (i.e., design criteria), which would help in developing an eective software solution.

3. Survey Method

3.1. Objective

The primary objective of our web-based anonymous survey is to identify the design criteria (i.e., functional and non-functional requirements) for visualising the household electricity consumption and to help the residents in reducing their domestic energy consumption. The main categories of design criteria to be elicited by the online survey are: (a) preferred device to view visualization, (b) preferred software platform, (c) preferred type of visualization, (d) preferred metrics for visualization, (e) preferred medium to receive the tips/notications, etc. This online survey would help in designing an eective and ecient software application for domestic electricity consumers.

3.2. Participants

The targeted participants of this survey are the residents of New Zealand who are aged 18 years and above. The participants for the survey were invited through various sources, Facebook events and E-mail groups. Also, the advertisement was made available in several NZ Facebook groups, such as NZ groups and was made available in public places such as the church, notice boards in super markets, etc. to draw the attention of prospective participants. A total of 137 people participated in the survey, resulting in a response rate of 1.32% (around 10,350 participants were invited through Facebook and E-mail groups). The survey had been open to public during Dec 2014  Feb 2015.

3.2

4

Participants

Table 1: Questions in User Centred Survey Section Demographic Information

Questions 1. What is your gender? 2. Which category below includes your age? 3. What is the highest level of education you have completed?

Energy Usage

4. Number of rooms in your household (including kitchen, living room, bed room, bathroom) 5. How many people currently live in your household? 6. Where do you spend most of your time? 7. Who pays your energy bill? 8. Do you use gas for household water heating or cooking?

Current System and Motivation

9. On which one of the following devices do you MOSTLY use the Internet? 10. How do you CURRENTLY access your household electricity bill? 11. What information would you read from your electricity bill? 12. Do you attempt to save your household electricity consumption? Briey explain why in the text box below. 13. What is/are the most important reasons for you to save energy?

User Preferences

14. Select all the devices which you prefer for viewing your household electricity consumption. (Select as many as applicable) 15. How often would you like to get the feedback on your household energy consumption? 16. What is/are the preferred metrics (units) for measuring your household electricity consumption? (Select as many as possible) 17. Select the MOST preferred medium to receive the smart electricity conservation tips. 18. Please rate the parameters on the left on how it would motivate you in conserving household electricity consumption. (The more stars you give, the more you are motivated) 19. What are the extra information do you need to know from your electricity bill?

3.3

5

Design, Materials and Procedure

3.3. Design, Materials and Procedure The online survey was created using a free online tool,

eSurv 1 .

Various

online tools such as Google Forms and Survey Monkey were considered before choosing eSurv. The advantages of eSurv over other tools are that it is free and it supports dierent types of questions as well as ability to rate options using stars, and it also supports receiving unlimited responses. The survey contained 17 closed questions and two open ended questions (questions #13 and #19 in Table 1). The questions posted were related to four categories:

•

Demographic information (e.g. age, gender, education)

•

Energy usage information information (e.g.

number of people in home,

bill payer, etc.)

•

Current system and motivation (e.g.

information from electricity bill,

devices used to view the energy information)

•

User preferences (e.g. preferred metrics ($, kWh) to view energy information and frequency of energy information to be delivered)

There are three questions for demographics, ve for energy usage, ve for current system and motivation, and six for user preferences.

3.4. Pilot Testing To validate the survey, we performed two steps. Firstly, the survey questions were validated against the objective of the survey.

The survey was initially

designed by one of the authors and was cross-veried by the other authors and was also reviewed by one of the domain experts in power and energy. The second step was to analyze the eectiveness of the survey. This was done by recruiting three participants (aged above 18 years) to complete the survey and examine the ease of completing it. The average time required by the pilot participants to complete the survey was 10.5 minutes. A reection questionnaire was given to these three participants and the survey was modied with the recommendations provided by the pilot users. Some of the changes made to the survey after the pilot testing were: (a) some of the jargons were replaced or explained, (b) new options were added to one of the questions, and (c) some of the keywords such as

currently, mostly, most

were highlighted in capital letters in some of the

questions.

4. Results The results are expressed under four main categories as mentioned above. They are (a) demographic information, (b) energy usage information, (c) existing visualization, and (d) preferred type of feedback.

1 https://eSurv.org

4.1

Participants' Demographic Information

6

4.1. Participants' Demographic Information As mentioned above, there were 137 participants who responded to the online survey, out of which, 55.47% (76 participants) were male and 43.79% (60 participants) were female and 1 participant did not provide their gender information. The majority of the respondents were between the ages 21 and 29 (58.39% - 80 participants), followed by ages 30  39 (24.82% - 34 participants), ages 40  49 (6.57% - 9 participants), ages 50  59 (5.11% - 7 participants), ages 18  20 (3.65% - 5 participants) and, above 60 (1.46% - 2 participants) as specied above in Figure 1a.

Figure 1: (a) Participants' Age (b) Number of people in household Similarly, the majority of the participants had completed post-graduation (49.64% - 69 participants), followed by participants who had completed graduation (27.74% - 38 participants), doctorate (10.22% - 14 participants), diploma or certicate (4.38% - 6 participants) and, high school (3.65% - 5 participants). Only few participants had attended school (4.38% - 6 participants) as shown in Figure 2a.

Figure 2: (a) Participants' educational qualications (b) Participants' preferred device for using internet

4.2

7

Energy Usage Information

4.2. Energy Usage Information The energy usage questions target to understand number of people at home, number of rooms in the home, types of energy sources used, etc.

The rst

question was to identify the average number of rooms in NZ household. The rooms include kitchen, living room, bed room(s), and bath room(s). As per the survey results, 33.58% (46 participants) people said that there are 4  5 rooms in their household, followed by more than 5 rooms (27.01% - 37 participants), 2  3 rooms (27.01% - 37 participants), and 1 room (12.41% - 7 participants). Hence, the average number of rooms per household is 3.9927 or approximately 4 rooms. The graph in Figure 1b shows the number of people per household in NZ. The average size of the surveyed households is 3.781 persons per household. The majority of people live in household with 2  3 people (44.53% - 61 participants). To understand the need for remote appliance control, the participants were asked to specify where they spend most of their time. Most of the participants (59.85% - 82 participants) said that they spent most of their time at work, while 32.85% (45 participants) of the participants said that they spend most of their time at home. The next question was asked to understand whether the participants used other energy sources such as gas for water heating, cooking or any other domestic purposes other than electricity.

33.58% (46 participants) of the participants

said that they used gas for water heating or cooking, whereas the remaining participants (66.42% - 91 participants) said that they did not use gas. The average time required for the participants to complete the survey was approximately 9.2 minutes.

4.3. Current System and Motivation The technical details of the to-be-developed software include the visualizations that are currently used by the participants to view their household electricity consumption.

The rst question asked the participants to list the de-

vices that they use mostly for browsing the internet. Figure 2b shows that the majority selected laptop (38.69% - 53 participants), followed by mobile phone (32.12% - 44 participants). A signicant number of selections were also received for personal computers (18.98% - 26 participants) and tablets/iPads (5.11% 7 participants) as shown in Figure 2b.

One participant specied that it was

dicult for him/her to decide which was mostly used. The next question was about the current way of accessing the electricity bill. A maximum number of responses were received for the option, No bill, as house owner pays the bill (32.85% - 45 participants).

An interesting observation

here is that only 2.19% (3 participants) said that they use the smartphone application provided by their energy service provider to view their electricity bill.

The others said that they use paper bill delivered in post (22.63% - 31

participants), e-mailed bill (21.17% - 29 participants), and bill from electricity provider's website (12.41% - 17 participants). This is shown in Figure 3a. An important parameter needed to design an eective software for energy feedback is the information that represents the most important content of the

4.3

Current System and Motivation

8

Figure 3: (a) Ways of accessing the electricity bill (b) Information read from the electricity bill bill.

96 participants answered this question, whereas the remaining partici-

pants skipped this question. The most popular response was overall bill amount (28.52% - 75 participants), followed by the electricity usage in kWh (21.67% - 57 participants). A signicant number of selections also indicate the time period or bill duration (15.97% - 42 participants), comparison of monthly electricity consumption (11.41% - 30 participants), and price per kWh unit (11.03% - 29 participants). Some of the participants also noticed daily xed charges (6.08% - 16 participants) and electricity meter readings (5.32% - 14 participants). This is summarized in Figure 3b.

Figure 4: Preferred frequency of feedback Above all, 80.15% (105 participants) of participants responded that they either attempted or were attempting to save energy in some way.

Figure 5

represents the stacked graph which shows the energy saving motivation. The motivation can be economical, environmental, both or none. A majority of the

4.4

9

User Preferences

participants chose to save energy for both economic and environment reasons (32.85% - 45 participants).

The next highest motivation among the people

was to save money (31.39% - 43 participants). Very few people opted for saving environment as their motivation (8.76% - 12 participants). Also, Figure 5 shows an interesting insight in which we can observe that 80% (4 out of 5) of 18 to 20-year-old, 75% (60 out of 80) of 21 to 29-year-olds, 73.33% (55 out of 75) of 30 to 39-year-olds, 88.89% (8 out of 9) of 40 to 49-year-olds, 85.71% (6 out of 7) of 50 to 59-year-olds, 100% (2 out of 2) of participants above 60 years attempted to save energy.

From the Figure 5, it is also evident that lesser the age, the

more they are concerned about saving environment or saving both money and environment than just to save money, which is very clear from the following quotes from the respondents (The respondents are identied by an unique ID, `RID').

Resources like coal mine and other minerals are non-renewable, and it is meaningful to save power and resources. - RID: 10692385 (Age group: 21-29) My electricity charges are included in rent so I just make sure that my usage is not exceeding the amount allocated in the rent so that I do not need to pay extra. - RID: 10692457 (Age group: 30-39) It's not good to waste energy. And it also reduces household bills. - RID: 10695622 (Age group: 21-29) Save the energy to protect the Earth - RID: 10718922 (Age group: 21-29) Ensuring power for future generations and preserving the natural resources like coal, which are depletable resources. - RID: 10714742 (Age group: 21-29)

Figure 5: Energy Saving Motivation Vs Age

4.4. User Preferences The participants were also enquired about their preferred way of visualizing the electricity consumption, which would help with the future design and development of a visualization tool. A signicant parameter for the designers to design/develop the visualization is to decide the platform (e.g.

Windows, Android, iOS, etc.)

in which the

software needs to be developed. So, the participants were asked to opt for the devices that they prefer to view the electricity consumption. Almost each of the device options (e.g.

laptop, mobile, tablet) is supported equally by one-third

4.4

10

User Preferences

of the participants. Hence, it can be concluded that the software that is to be developed should support multiple devices.

Figure 6: Number of participants attempting to save energy based on their age groups It is also important to understand how often the users like to get feedback on energy consumption. Figure 6 shows that most of the participants preferred once-in-a-month feedback (30.09% - 34 participants) slightly over once-in-a-week feedback (29.2% - 33 participants), followed by real-time feedback (11.5% - 13 participants), once-in-a-day feedback (11.5% - 13 participants), and once-in-anhour feedback (3.54% - 4 participants).

A small minority of the participants

(7.96% - 9 participants) responded that they do not need any feedback, with the following being one of the reasons cited.

I have an idea of which devices consume a lot of energy, so I don't need a specic overview. I know how to save more energy. I would start with double glazing and insulation.... Also I have switches on sockets so I don't have too many devices sitting around on standby. - RID: 10693154 The participants were asked about the metrics or units that they prefer for viewing their electricity consumption. The popular ones were price in dollars (38.38% - 71 participants) and consumed electricity (kWh) (35.14% - 65 participants), followed by number of trees equivalent to energy consumed (7.57% 14 participants), and CO2 emission (5.95% - 11 participants). And, the most preferred medium to receive the smart electricity conservation tips were e-mail alerts (39.82% - 45 participants), software application (23.89% - 27 participants) followed by text message alerts (31.86% - 36 participants).

There were participants who preferred not to receive any notications. I don't want more notication in my life. - RID: 10693154 I don't need it. I've never wasted energy. It's money for me. - RID: 10711705 Please no extra spam. - RID: 10714580 One of the respondents recommended displaying the energy saving tips alongside the feedback.

Probably alongside the feedback that display the energy consumption information - RID: 10790358 It is quite important to understand what motivates the users to save energy. Out of 137 participants, only 113 responded to this question.

This question

11

had ve options in which the users were asked to rate each option out of 10 where 1 was least preferred and 10 was highly preferred. The highest ratings were received by `rewards and incentives' (862 points  76.28%) as the best motivating factor for conserving household electricity. The next highest rating was received by `disaggregated electricity data', i.e., energy consumed by each appliance (834 points  73.8%), followed by `general electricity saving tips' (780 points  69.02%), `archived data' or historical electricity data (719 points  63.63%), and `comparison with friends, peers, neighbours, etc.' (670 points  59.29%). The users were asked about other electricity related information (apart from the ones available nowadays in practice) that they would like to visualize/view to conserve electricity. The participants recommended: disaggregated electricity information, highest and lowest peaks of energy consumption per day, and separation between space heating and other appliance, etc.

The highest and lowest peaks of electricity consumption everyday - RID: 10706064 Classify the energy used by each electric device - RID: 10712588 Some kind of separation between house heating consumption and other consumption. I think the most things which increase my bill are heaters in the winter. - RID: 10727387 5. Discussion The survey has identied that most of the participants (approximately 77%) have attempted to save energy. There were dierent ways in which people had tried reducing their energy consumption. For example, by analyzing their paper bills or using the latest software applications. There are several advantages with respect to software applications over other forms of feedback: (a) the feedback is immediate and automatically updated, (b) dierent dimensions of the feedback such as real-time energy consumption; and aggregated and disaggregated energy consumption could be provided, and (c) the users are highly informed through notications. But, not many people use software applications (2.74%, see Figure 3a).

Hence, it would be useful to analyze the design decisions of the current

software systems and to overcome those problems in the to-be-developed system. Analyzing the design decisions includes analyzing the functional and nonfunctional requirements of the system. Functional requirements include information visualized in the devices, techniques in visualization (2D/3D), and mode of visualization (time, scale, information modes). Some of the non-functional requirements include performance, quality, privacy, reliability, etc.

5.1. Functional Design Decisions The main advantage of software applications is their ability to store and display multiple dimensions of in-formation about household electricity consumption. From the survey results, the highly motivating parameter to conserve energy is rewards and incentives (76.28%) and so this is one of the important information to be visualized in the software. The visualization would also include

5.2

12

Non-functional Design Decisions

disaggregated electricity consumption (i.e., electricity consumed by individual appliances and devices), electricity saving tips, and archived data of household electricity consumption. With respect to the disaggregated electricity consumption, the survey results recommend separating the space heating from other electrical devices. Also, a comparison of the archived electricity information is worth adding to motivate the users. The preferred medium of sending the electricity saving tips can better be personalized by the users themselves as the survey response is inconclusive to choose one. The preferred mediums were (a) text message alerts (31.86%), (b) e-mail alerts (39.82%), and (c) software application notication (23.89%). So, the default one could be the software application notication as it is easy to generate the notication from the local database rather than sending them externally through email or text messages. Also, there would be an option to disable the notications as some participants recommended. The next functional design decision is to select the appropriate metrics to visualize the electricity consumption. The highly preferred metrics were kWh and price in dollars. As with the electricity tips, the users can also choose the preferred metrics during signup and can later be modied according to their interest. The next design decision is the frequency of the electricity feedback. From the survey results, it can be ob-served that the maximum response was received by once-in-a-month feedback and once-in-a-week feedback. Hence, the users can choose one between them or both. There are several functional decisions that emerged from the open ended

What are the extra information do you need to know from the electricity bill?  Some of the noticeable responses from the participants are specied question,  below.

Ways to consume power and comparison of the usage with the previous months (of the same year). At least three months comparison. - RID: 11120463 Percentage of energy savings corresponding to factors like orientation and insulation of building. - RID: 11120207 5.2. Non-functional Design Decisions Through the online survey, it is important to understand the non-functional design decisions for the to-be-developed software. Through the survey, we understand that people use dierent devices such as laptop, personal computer, mobile phone, tablet, Internet-enabled TV, etc.

to view internet.

Since the

percentage margin of people using various devices is very small, it is quite important to make sure that the users can use the software in dierent devices. So it is good to employ the non-functional design decision,

portability

 a way

of developing software, so that it could be used on multiple platforms.

ity

Some of the other basic non-functional design decisions are

understandabil-

(the ability to understand features to take eective decisions through the

software application),

privacy

(the ability of the software to secure the users'

5.3

13

Comparison of survey results with literature review

electricity data or any personal data) would also be achieved in developing the software for visualization. Privacy was considered as one of the features of the system because the survey respondents were least motivated by sharing their electricity information with their friends, neighbours, etc.

5.3. Comparison of survey results with literature review A systematic literature review (SLR) was conducted to understand the design criteria for visualizing household energy consumption. Full results of the SLR involving 22 research papers from 6 databases are described elsewhere [12]. Table 2 summarizes the comparison of design criteria from SLR with survey results.

Table 2: Comparison of SLR and Survey Results Functional Requirements

Source

Non-

Source

functional requirements Aggregated energy consumption

SLR

Privacy

Both

Disaggregated energy consumption

Survey

Portability

Both

Energy saving tips

Survey

Real-time power consumption

Both

Accessibility

SLR

Remote appliance control

Both

Flexibility

SLR

Archived energy consumption

Both

Scalability

SLR

Security

Survey

Understandability

-

The

functional requirements

Both

that came up both in SLR and the survey re-

sults include remote appliance control; aggregated energy consumption; disaggregated energy consumption; and viewing archived/historical energy consumption. The

aggregated energy consumption

oers the energy consumed by all the

disaggregated energy consumption oers the energy consumed by each appliance. The remote appliance control helps the energy users in controlling all the household applihousehold appliances daily/weekly/monthly/yearly, whereas

ances remotely through local networks or from anywhere through the Internet. The

archived/historical energy consumption helps the energy users in visualizing

the weekly/monthly/yearly comparison of household energy consumption. Some of the other functional requirements are visualizing real-time power consumption and energy saving tips in which the rst one is the result from SLR and the last two are the results from survey. The

real-time power consumption

species the power consumed by all the household appliances at any given time or a relatively short time window. The

energy saving tips

help the users with

recommendations/suggestions on how to reduce the energy consumption. The

non-functional requirements

that were derived from the SLR and the

survey are privacy, portability and understandability.

Privacy

can be dened

as the restricted access to one's own energy information. An individual might like to conceal his/her energy consumption from others and hence the application must support individual privacy.

Portability

is dened as the software

5.4

14

Challenges in Implementing the Design Criteria

application's ability to work on multiple platforms and devices, such as mo-

Understandability

bile, the Internet, in-home display, iPad, etc [9].

is dened

as the ease of recognition of information displayed in the visualization by the human mind. Some of the other non-functional requirements derived from the SLR/survey/both are accessibility, exibility, scalability, modularity and security.

Accessibility

refers to the ease of nding, downloading and using the

visualization application online, such as on websites or social networking sites [13].

Accessibility

could also be dened as the ease of downloading the feed-

back online for oine processing [9]. It is expected that both the application and feedback data are easily accessible.

Flexibility

refers to the ability to tog-

gle between dierent modes of visualization such as aggregated, disaggregated and real-time energy consumption [14]. If the time taken to toggle is less, the application is considered more exible.

Scalability

is the ability of the applica-

tion's architecture to scale in order to accommodate additional information in the visualization [3, 7]. The application is expected to be more scalable.

5.4. Challenges in Implementing the Design Criteria In the previous sections we have listed the design criteria based on user preferences with regards to visualization.

We now discuss the challenges in

implementing these design criteria.

Real-time Aggregated Energy Visualization:

Visualizing real-time energy con-

sumption for the whole household poses various challenges. Let us assume that energy information from the smart meter is received every one second. The main concern is the amount of data received. The total data received per day is 60*24 = 1,440, where 24 is total hours in a day and 60 is number of minutes per hour. The storage space requirements increases day-by-day. Hence, data storage and management is one of the important challenges for real-time systems.

Real-time Disaggregated Energy visualization:

Disaggregated energy visuali-

sation presents the same challenge as the aggregated visualisation. Let's assume that they are 14 appliances in a single household. Hence, the total energy data generated in a household per year is 1440*14*365 = 7,358,400 (1440 rows of data created for every minute, i.e., 24*60=1440; 14 represents the number of appliances; and 365 represents the number of days in an year).

Storing, re-

trieving and manipulating such large amounts of energy data to calculate power consumes computational time and memory, which can degrade the performance of the software.

Archived Energy Consumption (Aggregated):

Typically, users compare their

current energy consumption with previous year's [8]. Hence, the amount of data needed for this comparison is huge in the order of hundred thousands. Again, storing, retrieving and manipulating such large amounts of data can degrade performance.

Archived Energy Consumption (Disaggregated):

Typically users compare the

current energy consumption of high-consuming appliances such as the air conditioning (either heating or cooling) with the previous years' consumption [8]. Hence, the amount of data needed for this comparison is the same as the ag-

5.5

15

Limitation

gregated archived energy consumption and the data doubles if a household has two air conditioning appliances.

Potential Solution:

At the end of the day, the big data such as real-time/

archived aggregated/disaggregated data can be accumulated (i.e., aggregate the per-minute information into per-day information to reduce space) in the database and this would dramatically reduce the memory usage and can be used to compare the information across days/weeks/months/years.

Energy Saving Tips:

There are two ways of providing energy saving recom-

mendations/suggestions to the users. They are (a) manual, and (b) automatic [6].

The manual energy saving tip waits for the user to respond before the

action, whereas the automatic energy saving tip doesn't wait for the user's response. It is quite dicult to categorize the energy tips as manual or automatic as it diers by individual user preferences. Also, while requesting the users to categorize the tips during initial software setup might appear to be a feasible solution, but the number of energy saving tips run often over 200 in number and can be tedious to sort.

Potential Solution:

Intelligent learning algorithms can be employed to cate-

gorize various recommendations as either manual or automatic [4]. For example, if a recommendation and the users' reaction to the recommendation occurs more than three times, that recommendation can be made automatic. In summary, the primary challenges related to the non-functional design criteria are (a) large energy data, i.e., extensive memory requirements, (b) categorization of energy saving tips, (c) selection of appropriate visualization techniques, and (d) achieving understandability. Potential solutions for each were also discussed.

5.5. Limitation The results of this survey may not be used as the only source of design criteria or user requirements. Researchers can use these parameters as a starting point but will likely need to nd ways of validating these requirements by crossreferencing with other sources such as focus groups, user evaluations, etc.

6. Conclusion This paper presented the results of a web-based user-centred survey completed by 137 energy users with the main goal of understanding the functional and the non-functional design requirements to design a software application for visualizing household electricity consumption. The respondents use a variety of devices to access the internet, which concludes that the software to be developed should support multiple devices such as mobile, laptop, tablet, etc. The feedback metrics most preferred by users are consumed energy (kWh) and price (in dollars). Hence, the software system should allow users to personalize their settings accordingly. Smart electricity saving tips are one of the major motivators of reducing household energy consumption. A majority of the respondents preferred notication of such tips via text message alerts or email alerts. Furthermore, this

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should also exible to personalization through the software settings.

As the

major reason behind saving electricity for users is saving money and helping saving environment, the electricity tips should motivate the users either based on monetary benets or based on environmental benets. Additionally, users recommended the following two non-functional requirements: security, and reliability as key.

From the design and development perspective, extensibility

and exibility should also be considered. The design criteria elicited from this user-centred survey were compared with the ones derived from a systematic literature re-view on the subject to summarize the key criteria across both. The likely challenges arising from implementing the design criteria were also discussed along with potential solutions. To further support the design criteria derived from the literature review and this user-centred web survey, focus groups with selected users will be conducted. The focus group will help in selecting the preferred designs for visualization. A nal set of design criteria based on the three sources  literature review, usercentred survey, and focus groups  will then be used to design and implement the envisioned software application for visualizing household energy consumption in order to achieve maximum eectiveness and usability.

Acknowledgment The authors would like to thank all the participants who responded to the online survey. This research is supported by The University of Auckland Doctoral Scholarship.

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