An Energy CompetitionDashboard

Bachelor’s thesis

July 17, 2015

Student: Jeroen Brandsma, Michel Dekker

Primary supervisor: prof. dr. A. Lazovik, drs. F. Nizamic

Secondary supervisor: dr. R. Smedinga

Contents

1 Introduction 11.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 The idea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.2 Test environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.3 Energy measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Research goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Related work 42.1 Energy saving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Gamification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3 Concept 63.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2.1 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.3 Energy saving algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.3.1 Consumption baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4 Realization 144.1 Use cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.2 Non-functional requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.3 Used techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.3.1 PHP with Zend Framework 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.3.2 AngularJS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.3.3 Bootstrap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.3.4 Composer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.3.5 d3js . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.4 Project setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.4.1 Global implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.4.2 Energy savings algorithms implementation . . . . . . . . . . . . . . . . . . . . . 18

5 Evaluation and results 205.1 Testing the application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.1.1 Test group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205.1.2 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205.1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215.1.4 Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

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5.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

6 Conclusion 23

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Abstract

At this moment, a lot of research is going on regarding energy consumption. Many of these researchesfocus on systems to reduce the consumption automatically. This thesis will investigate something new,reducing energy consumption by giving people insight in their own energy consumption.

In this thesis we will aim at the human aspect regarding energy consumption. The literature researchwill aim at current energy consumption reduction methods and at how to enforce the competition feelingbetween people. Based on these results a dashboard is build that is accessible by the test group. This testgroup consists of five employees of the University of Groningen, who made their office available for ourexperiment.

Preliminary results show that our application could work. However, we cannot give any well foundedconclusion since we were not able to gather enough data because of the time constraints of this project. Inthe future more research should be done to get enough data to give any real conclusions.

Chapter 1

Introduction

This chapter provides some background information about the project and explains the research goals forthis thesis. The primary goal for this thesis is developing a system which motivates users to reduce energyconsumption.

First, section 1.1 provides the context of the research. This includes why the system is developed, wherethe system is tested and which previous developed systems will be used as part of this research. Section 1.2gives a more extensive explanation about the research goals for this project. In section 1.3 the relevanceand contributions are listed. Finally, section 1.4 gives an overview of this remainder of this thesis.

1.1 Context

1.1.1 The idea

In 2012 the University of Groningen organized a contest to make the University more sustainable. FarisNizamic and Tuan Anh Nguyen proposed a prize winning plan [1] to make the Bernoulliborg in Groningena more sustainable building. During the last few years the plan had some good results [2].

The proposal included ideas to reduce waste, water consumption and energy consumption of theBernouilliborg. One of the ideas was to make people more aware of their energy and water consumptionusing a real-time utility display [1, p. 8]. A display was added to the building’s lobby to inform occupantson the building’s total energy and water consumption. This bachelor thesis extends this idea. The goal ofthe project is to make occupants of the building better aware of their individual energy consumption.

To achieve this goal we developed a personal energy dashboard. This dashboard informs occupantson their individual energy consumption. As an additional effort to save energy the dashboard includesfeatures which allows occupants to compare their energy consumption to the energy consumption of otheroccupants of the building. This feature was added in a playful manner to even further increase energysavings.

1.1.2 Test environment

The system was developed and tested in the Bernoulliborg in Groningen. The Bernoulliborg can accom-modate 350 staff members and 500 students [3]. The building’s dimensions are 33 by 83 meter and a height

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of 27 meter.

During the development of this project five offices were equipped with sensors to measure energyconsumption. All the offices were occupied by members of the distributed systems group of the computingscience department. The offices are located on the fifth floor of the Bernoulliborg.

1.1.3 Energy measurements

For the original proposal a system was developed to measure, store and retrieve energy consumption data.The system developed by Marcel Jillings and Sijmon Heitmeijer, as described in their master thesis [4],meets our requirements for measuring, storing and retrieving energy consumption data. Because this sys-tem is already up and running in the Bernoulliborg and all the required hardware was at our disposal weused their system as a source for the consumption data.

During their master thesis they compared multiple sensors for measuring energy consumption. Thosesensors were used as a part of a greater system, consisting of multiple loosely coupled software compo-nents. For this project we use a subset of those software components to measure, store and retrieve theenergy consumption data.

1.2 Research goals

The primary goal of this project is to develop a system which encourages its users to reduce energy con-sumption. To achieve this we need to answer the following questions.

• Which elements and information should be part of a dashboard for energy competition be-tween office occupants? To create an environment that stimulates users to actually think about theirenergy consumption and adapt their behavior in that way, we need to include the right motivatingelements.

• What could be considered energy savings for a workplace and how should this be determined?All the primary functions of the dashboard rely on the amount of energy saved. This means we haveto determine if someone is actually saving energy, we need to design an algorithm that takes theconsumption data of an office and calculates the saved energy.

To answer these questions, we should give a definition for the term workplace. We consider a work-place to consist of all personal electric devices, for example a PC, monitors and a office telephone. Allshared electric devices like lighting or heating are not considered to be part of the workplace. The energyconsumption of the equipment of this particular person is measured.

1.3 Contributions

We developed a system that can measure the energy savings of office occupants. However, there are morethings possible with the developed system and algorithms.

• The system is ready for more research to see if it has long-term effects. In the time we have fordoing this bachelor project, we only have time to retrieve short-term data. This means we cannot testwhether the system has any long-term effects on energy consumption.

• The system could be used in any other office. Because the system is developed in an office en-vironment, it is easy to export it to any other office building. This also includes other university

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buildings.

• Potentially the system could be ported to the home environment. The requirements for such asystem would be a little bit different from the office environment, however our algorithm for energysavings could be a good starting point.

• We developed an algorithm to determine if someone is saving energy or not. The most importantpart of the system is to determine if someone is actually saving energy or not. This idea could beapplied to other systems as well.

1.4 Overview

In the next chapters we will further explain our project. In chapter 2 we do literature research on energysaving techniques and gamification. After this we define the idea and design of our project in chapter 3.Then in chapter 4 we discuss the techniques used to develop and implement our idea. When our prototypewas finished, we held a competition between the office occupants. These results are gathered and discussedin chapter 5. Finally, we come to a conclusion in the last part, chapter 6.

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Chapter 2

Related work

As explained in chapter 1 the primary goal of the system is to reduce energy consumption. Section 2.1 listsdifferent other research efforts to reduce energy consumption. Secondly, section 2.2 lists related researchpapers on using game concepts in non-game contexts, also known as gamification. We also tried to findresearch on baselines or how to determine energy savings. Unfortunately we could not find any researchwhich focuses on determining the energy savings on an individual level. This means we have to developthis ourselves.

2.1 Energy saving

In 2030 the European Union wants to cut the annual energy consumption by 27% [5]. This is one of reasonsthat many research is done on energy consumption and energy savings. Many of these researches focus onautomatic Building Management Systems (BMS) to cut down power consumption. However, these BMSsystems mostly act on floor or wing level and therefor are not optimal [6]. It would be more efficient if itcould monitor and control individual offices [4].

Besides saving energy by a BMS system, lighting is another unnecessary source of big energy con-sumption [7]. Many different systems have been developed in order to automatically control the lights inan office or even landscape offices. B. Roisin et al showed that is does matter where offices are located andhow they are oriented [8]. When an office is located in Athens and is oriented towards the sun, it can saveup to 61% only for lighting. This number decreases to 45% when an office is oriented away from the sunin Stockholm. On the other hand, when an office is oriented towards the sun, it can become hotter in thesummer compared to the offices who are towards the sun, which can lead to higher energy consumption byusing air conditioning.

2.2 Gamification

Research in the field of gamification is relatively new, the first documented use was in 2008 [9]. Fromthat moment on “gamification” is a heavily contested term [9]. There are two main related concepts. Thefirst is “the increasing adoption, institutionalization and ubiquity of (video) games in everyday life” [9].This definition of gamification means that the adoption of games in everyday life becomes more common.However, in our thesis we adopt the second concept that is defined [9]. This concept views gamificationas a way to make everyday life tasks or products more engaging and is officially stated as follows “gameelements should be able to make other, non-game products and services more enjoyable and engaging aswell” [9].

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In 2009 Power Agent was presented [10]. Power Agent is a game designed to encourage teenagers andtheir families to reduce energy consumption in the home. One of the elements to achieve this is to encouragecompetition between families. Two groups played the game (figure 2.1) for ten days and achieved greatresults. However, the researchers could not conclude any post game effects on behavior. This competitionelement is what we will use in our project. We want to create a competition between users of the systemwhich ideally will affect their behavior on the long term.

Figure 2.1: Screenshots of the Power Agent competition

To develop our dashboard we want to use the most effective elements. Leaderboards, levels, badges andchallenges are all received positive by the users, they also achieve good results on the short term [11]. Toengage users in the long term you need internal motivation instead of external motivation [12]. This meansinternal motivation needs to be fueled instead of just relying on the external motivation of leaderboards andchallenges.

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Chapter 3

Concept

The third chapter provides information about the answers for the research questions and also for the designof our system. In section 3.1 and 3.2 some information about the user interface and the architectural designis provided. To retrieve the data that is needed for our system we use the API that is created by a formermaster thesis, information about this can be found in section 3.2.1. To calculate energy savings from thisdata we needed to develop an algorithm. This algorithm is explained in section 3.3.

3.1 Design

Our eventual goal is to create an energy competition dashboard. To do this, we have to answer the researchquestions. One of these questions is what elements and information we should include in such a system.In chapter 2 we found out that the most effective gamification elements are: leaderboards, levels, badgesand challenges. Since we do not have time to implement all these elements, we chose to implement theleaderboards and the badges. Besides this there is also a dashboard page with an overview for the user anda consumption page with raw data.

To make the application easily accessible for the users, we chose to make a web application. This webapplication needs to be simple and easy to use. Because of this requirement we chose to have just 4 viewpages.

• DashboardThe dashboard is the first that is shown when a user logs in. This page includes the most importantleaderboards so a user instantly knows how well he or she did in the last period. First of all a usersees the total energy consumption of the last day, week and month. In the same table is also shownhow much energy the user saved in rates. After this a summary of the leaderboards is displayed. Thecurrent and previous daily, weekly and monthly leaderboards are displayed.

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Figure 3.1: Screenshot: Dashboard

• LeaderboardOn the leaderboard page we show a more detailed leaderboard than on the dashboard page. In thissection you can see the leaderboards for any date. You can also specify if you want to see the daily,weekly or monthly tables. Besides the total energy consumption and the percentage of energy saved,we also show the absolute value of the energy savings. We only show the first three users. We do notshow more users because we do not want to discourage users who are mostly on the bottom of theranking. Hopefully the lower-ranked users will get motivated when do not appear in the top three.

Figure 3.2: Screenshot: Leaderboard

• ConsumptionIn this section we display the raw consumption data of the user. A graph with all the raw data pointsis displayed. The user can update the graph by changing some options. If a user wants so see the

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consumption data it can simply pick another date and update the graph. A user can also change somegraph render settings, which will be explained in more detail in section 3.3. Next to the graph is atable with some precise values about the energy consumption.

Figure 3.3: Screenshot: Consumption

• MedalsThe medal page displays all the medals won by the users. The users are ranked first by the numberof gold medals, secondly by the number of silver medals and last by the number of bronze medals.A user will receive a medal if it is in the top three of a daily, weekly or monthly ranking. Since itis harder to obtain a medal in a monthly ranking, this medal has a higher value than a medal in aweekly or daily ranking.

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Figure 3.4: Screenshot: Medal table

Besides these 4 main pages there are a few more pages. First of all there is the settings page. Here a usercan change some settings like changing an avatar or reset the password. On the profile page you can viewyour personal account, your name, avatar and achieved medals.

3.2 Architecture

The second research question we have is to define what energy saving is. To do this we need an algorithmthat takes all the consumption data and gives the amount of energy saved. So before the algorithm can bedefined, we need the consumption data of all users. We have access to 5 plugwise devices [13]. Thesedevices create a wireless network in which they send the data. The data can then be read by a plugwisestick. This stick is put in a usb port of a computer and becomes part of the wireless network. To get usefuldata out of this a separate system had to be developed. This was done by two former master students [4].They developed a system that takes the data of the plugwise devices and puts it in a database. After thisan API was created which gives us the opportunity to reach the data very easily. This data is specified inmore detail in section 3.2.1 below. After this we can use the data from the API. How we process this datais shown in section 3.3.

3.2.1 Data description

As described in de part above we can get to the generated data by accessing the API delivered with thedatabase. We get the results of a specified energy meter for a certain period. This period can be adjusted toour needs. The device sends data every 5 minutes, this means we have 12 ∗ 24 = 288 data points per day.The data is delivered in the human readable data format JSON (Javascript Object Notation) [14]. Figure3.5 shows a data point that is given by the API.

Figure 3.5: JSON object

A data point consists of the following 5 elements.

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• timestampThe timestamp consists of the date and time in the Amsterdam timezone, this means UTC(CoordinatedUniversal Time) +2 in the summertime and UTC +1 in the rest of the year.

• process_idThe process_id is a remainder of a previous project and not relevant for our project.

• sensor_idThe sensor_id is a unique identifier for a type of sensor. This means if a sensor is of the same type,it has the same sensor_id.

• instance_idTo identify a specific sensor an instance_id is given. Every sensor has its own instance_id.

• valueThe value that is measured by the energy meter in Watt(W).

3.3 Energy saving algorithm

After we received the consumption data of the offices, we could start developing an algorithm to determineenergy savings. This algorithm has some requirements that all should be taken into account. For example ifa user has a day off, we should not consider this as energy saving. We also want to create a fair competitionso we decided to compare energy savings percentages. We calculate this percentage by dividing the energysaved by the total energy consumption the user could have made if no energy was saved. We use thefollowing definitions:

• First time consumptionThe first time during the day the energy consumption, for a certain amount of succeeding minutes,has a higher value than the consumption value at the beginning of the day. This way, users who leavesome of their equipment on at night will not start their work day at 00:00.

• Last time consumptionThe last time during the day the energy consumption, for a certain amount of succeeding minutes,has a higher value than the consumption value at the end of the day. This way, users who leave someof their equipment on at night will not end their work day at 23:59.

• Work dayThe period between the first time during the day and the last time during the day energy is consumed.

• Consumption baselineThe user his energy consumption during normal usage.

• Energy savingEnergy will be considered “saved” when a user uses less energy than his consumption baseline duringthe work day.

3.3.1 Consumption baseline

As mentioned above the consumption baseline is defined as the user his energy consumption during normalusage. But since energy consumption can vary over a day, how can we determine what the baseline is? Wedecided to test some different consumption baselines. We used a day for where it is clearly visible whatarea should be the energy savings. As can be seen in figure 3.6 the user gets into his office past 9 am andturns off all his devices around 10 am. He came back just before 1 pm and around 3 pm. Just before 5 pm

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he came to his office again and left around 7:30 pm. We consider most of the empty space between the firstand last data point energy savings.

Figure 3.6: Raw consumption data

Previous data as baseline

Our first idea was to use the consumption data of some time before the current day. For example, if we look5 workdays back in time, we take the average of these 5 days and take this as a baseline. This could workpretty well, however if someone adds a monitor his baseline is completely wrong for the following 5 days.The baseline will become better day after day, but this is not good enough for us. We want an algorithmthat works for all days, and is therefor not dependent on equipment.

Average consumption baseline

After rejecting the option above, we thought about a new method based on the average. To overcome theproblem of adding or removing energy consumers, we decided to test the possibility of a baseline as theaverage of the current day. This baseline takes the average of all data points of that day. Every point wherethe energy consumption is below this baseline will be made green as a sign of energy savings. As can beseen in figure 3.7 this is a little above 20 Watt. If we look at this, this does not really make sense. Thebaseline should be higher since his standard consumption line is higher than the baseline shown. In thisexample the baseline was calculated with all data points of the specified day. We could modify this a bitand use only the data points that are retrieved between the start of the workday and the end of it. This waythe baseline would become higher, but again not high enough since we still keep the lowest values intoaccount.

Ranges consumption baseline

When we found the average approach will not work, we came up with something else. We do not want totake the lowest value into account since they pull the baseline down. This is why we decided to developa range based consumption baseline. The range based baseline puts every data point into a specific range.

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Figure 3.7: Average based consumption baseline

If a data point is not yet in a range, the algorithm will check if it falls within a current range. If this is thecase the data point is added to this range. If it is not, we check if it is less than 10% away from a currentrange, if this is the case we put the data point in that range and expand the range to the value of the currentpoint. We do this with all data points and when this is done we typically have a few ranges in which all datapoints fall. If a workday was detected we should have at least 2 ranges, a top range and a bottom range.This can be seen in figure 3.8.

Figure 3.8: Ranges in raw consumption data

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From the bottom range we take the highest value and set this value as the off state baseline. The offstate baseline is the energy consumption when the user is not at work. From the highest range we take thelowest value and set this as the consumption baseline. Figure 3.9 shows the consumption baseline.

Figure 3.9: Consumption baseline in raw consumption data

After the baselines are calculated, we can determine the energy savings. All points where the energyconsumption is below the consumption baseline and above the off state baseline, we consider energy con-sumption. If we add this to the graph, we get the following result in figure 3.10.

Figure 3.10: Energy savings in raw consumption data

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Chapter 4

Realization

After we defined the answers for the research questions in chapter 3, we now implement these ideas to areal working system. To do this we define use cases in section 4.1 and some requirements in section 4.2.After this we explain the techniques used in section 4.3 and the setup of the project in more detail in section4.4.

4.1 Use cases

For our system we defined three groups of use cases: user, user management and data representation. Webriefly discuss the different use cases here, for more detailed information on this, we refer to the use casedocument appendix.

• UserFor a user we defined the standard user actions. A user has to be able to login, logout and reset hispassword. Besides this, we think it is a good idea to give users a profile. In this way someone canpersonalize his or her account and therefor become more attached to the system because they canview other accounts too. For this feature we need to implement a section where users can changetheir profile picture.

• User managementUsers cannot add themselves to the system. In order to add someone we need to create an adminaccount. This account will be able to add and remove users. Besides this it should also be able tolink a device to a user. We think this is more convenient than letting users do this by themselves.Maybe if the system will be deployed in more rooms, it would be handy to change this for the sakeof scalability.

• Data representationThe users can view the retrieved data in a few different ways. When a user logs in, he or she firstcomes on the dashboard page. This is an overview of the energy savings of that particular userand also an overview of the leaderboards of the last period. Secondly, users can view their ownraw consumption data. For this we have a consumption view where users can see their own dataand energy savings. This data is also used to generate the leaderboard, which users can also view.Besides this there is also a medal page with the medals obtained by the users.

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4.2 Non-functional requirements

Besides the requirements mentioned in the section above, there are also some requirements which are notfunctional but nevertheless very important.

• Low couplingWe want as less as possible coupling between the different components. This is because it shouldbe easy to add or remove users and their devices. It also should be possible to change the plugwisedevices of a user. If a plugwise device is in someway hardcoded for a user, this is not possible at all.

• MaintainabilityThe system should be easy to maintain. This requirement is especially important for this projectbecause we do not know if someone will work on it after us. To make the code more understandablewe will add as much documentation as we can.

• RobustnessIf a part of the system crashes, this should not have any effect on the rest of the system. If forexample a plugwise device will break down and no data is retrieved anymore then we do not wantthe leaderboard to rollover.

• ScalabilityWe assume the project could be a success and will be extended within the Bernouilliborg or otherbuildings. The system should be prepared for this. In this case all parts need to scale up as much isneeded.

4.3 Used techniques

As defined in section 4.2 we have to create a maintainable, robust and scalable solution for our problem.This means the code should be highly structured and logical. In this way other people could improve orexpand the system if necessary. In the next subsections we will explain the techniques used to create asystem that meets all requirements.

4.3.1 PHP with Zend Framework 2

We chose to develop the application in PHP since we are both very familiar with it [15]. PHP is a server-side scripting language created for webdevelopment in 1995. It is one of the most popular languages todevelop webapplications with. PHP can very easily be abused to create bad and unstructured code. Sincewe want to avoid this, we chose to use the Zend Framework 2 [16]. The Zend Framework is one of the morepopular PHP frameworks around on the internet [17]. To structure the code even more, we implementedthe Model View Control (MVC) design pattern. This design pattern is standard integrated with the ZendFramework. MVC is created to separate the responsibilities of the different components. The code for themodel, view and controller should not be mixed in the same files. For larger applications this would maketerrible and unreadable code.

4.3.2 AngularJS

AngularJS is an opensource framework for webapplications [18]. It is developed in 2010 and maintainedby Google and a group of developers and companies. AngularJS makes it very easy for us to handle data.You can simply bind data to a variable and do computations with it, this can all be done with just a few

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commands. Besides that AngularJS serves a Model View Controller (MVC) design pattern. Developingbecomes easier with AngularJS due to the Model View Controller (MVC) design pattern and the testingpossibilities. Unit and End to End tests can be designed and executed easily by using certain AngularJStesting tools. These tools are developed to make life easy for the programmer. Besides all this, AngularJSis a very nice framework to develop Single Page Applications (SPA). Since our goal was to develop suchan application, this was one of the reasons to choose AngularJS as our framework.

4.3.3 Bootstrap

To make a good looking and intuitive User Interface (UI), we decided to use the Bootstrap framework[19]. Bootstrap is a front-end framework for HTML, CSS and JavaScript. Originally it was developed bysome Twitter employees to make front-end development easier. Bootstrap is designed to create responsiveapplications. This means your application will look good no matter the size of your screen. Bootstrapconsists of all sorts of components, which all can be used independently. For example, we used the datepicker component. This is a component that gives a graphical representation of a calendar and lets a userpick a date.

4.3.4 Composer

As mentioned above, our application should be scalable. This also means it should be portable to otherbuildings. To make it easy to deploy our application, we decided to make a composer file. This file storesthe dependencies we have with the right version numbers. If the application needs to be deployed toanother server, we simply run de composer to install all dependencies. The composer tells the server weneed version 2.4 of the Zend Framework and PHP version 5.3.2. Besides this we also map two namespacesto two paths. This is done to prevent us from typing the complete path over and over again.

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4.3.5 d3js

To represent the raw consumption data in a user friendly way, we decided to present the data in a graph.After some research we found the d3js library [20]. d3js stands for Data-Driven Documents, js stands forjavascript. It is a JavaScript library which brings data to life by manipulating documents based on data.It can create interactive and good looking graphs. Besides this it gives smooth transitions when switchingbetween data sets. d3js works on all modern browsers and on many old browsers, this can be importantsince offices might not have the most up to date browsers. Figure 4.1 shows the possibilities of d3js weused.

Figure 4.1: Example of d3js possibilities

4.4 Project setup

We created several folders to split the code. We define a cache, config, data, module and public_html folder.The cache folder contains the API data of the last 30 days. When we calculate the monthly leaderboards,we need the data of the last 30 days. If we have to get all this data for all users from the API again, it willtake a lot of time. To set some global settings for different pages we have created the config folder. Thedata folder stores the avatars that the users can upload to personalize their accounts. We decided to separatethe API code and the actual application code. These two folders are in the module folder. In the API folderwe define the code for retrieving the data and the algorithm to perform computations on the retrieved data.Besides this it also defines the logic for the other pages in our application. In the folder for applicationcode we define the layout for the application. All HTML, CSS, JavaScript, images and libraries are storedhere.

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4.4.1 Global implementation

The consumption data for a day is stored in the ConsumptionDataDay class. Basically, it only stores the rawconsumption data returned from the REST API. The class has some additional methods to add algorithmsto its class. Multiple algorithms can be added to the ConsumptionDataDay class to calculate differentmetrics and manipulate data.

All algorithms should extend a ConsumptionDataDay/AlgorithmAbstract class. This simple class willcontain the algorithm in the execute(ConsumptionDataDay $data) method. This method will be calledfrom within the ConsumptionDataDay class to calculate the metrics. The ConsumptionDataDay class willprovide itself as the parameter to the method.

At least, every ConsumptionDataDay/AlgorithmAbstract class has two methods: dependsOn() and pro-vides(). All algorithms will provide some kind of statistic. For example, the NormalizeData algorithm willprovide “normalizedData”. Other classes can depend on and require “normalizedData”. For example,to algorithm which calculates the amount of energy saved depends on “normalizedData”, “startTimeWork-Day”, “endTimeWorkDay” and “consumptionBaseline”. When this algorithm is called, all other algorithmswhich it depends on will be called as well.

The way the algorithms provide data and depend on other data has a few advantages:

• Multiple algorithms may depend on the same kind of data, for example the normalized data. Thenormalized data can be cached and can be reused by other algorithms.

• One algorithm can be changed without affecting the other algorithms. For example, let say we wantto change the way the consumption baseline is determined. We can implement the AlgorithmAbstractclass again and implement a different algorithm to determine the consumption baseline. This doesnot affect how the amount of energy saved is calculated. It will execute the algorithm which providesthe “consumptionBaseline”.

• Other metrics and data may be added in the future without the need to change the ConsumptionData-Day class.

4.4.2 Energy savings algorithms implementation

We implemented a few standard algorithms. We explain every algorithm in detail and explain them in theorder they are used to calculate the energy saving percentage.

• NormalizeData: (provides: normalizedData)This algorithm will normalize the raw data. At the moment the plugwise devices will be polled aboutevery 5 minutes. This algorithm will return 1 minute intervals. It will create data between the firstdate from the raw data and the last date from the raw data. For every minute it will take the lastknown value from the raw data before this time. Missing data and incorrect values (e.q. -1) will beset to the last known value as well.

• DetectRanges: (provides: ranges; dependsOn: normalizedData)This algorithm creates a number of energy consumption ranges, or intervals. It will iterate over everyvalue in the normalized data. Every value is added to a range if it falls within an already existingrange or if it is close to an existing range. Close to an existing range simply means: the differencebetween the consumption value and the range is less than a certain percentage (default: 10%). If noexisting range matches the consumption value a new range will be created.

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• FindConsumptionBaseline: (provides: consumptionBaseline; dependsOn: ranges)This algorithm will find the consumption baseline. It will iterate over all ranges and returns thesmallest value from the highest range.

• FindStarTimeWorkDay: (provides: startTimeWorkDay; dependsOn: ranges, normalized-Data)This algorithm will try to determine the start time of the work day. It will take the first value fromthe normalized data and find the corresponding range. As soon as a number of consecutive minutes(default: 8) the energy consumption is above this range, it will mark the first minute of those consec-utive minutes as the start of the work day. If there are no consecutive minutes of energy consumptionabove the selected range it will return “NULL” to indicate no start of the work day could be found.

• FindEndTimeWorkDay: (provides: endTimeWorkDay; dependsOn: ranges, normalizedData)This algorithm will try to determine the end time of the work day. It works exactly the same way asthe algorithm which finds the start time of the work day, with the difference that it will iterate overthe normalized data in reverse order.

• SumWorkDayConsumption: (provides: totalWorkDayConsumption; dependsOn: normalized-Data, startTimeWorkDay, endTimeWorkDay)This algorithm will sum the energy consumption between the start and end time of the work day. Itasumes the values in the normalized data are given in Watt. It divides this number by 60 to get theenergy consumption in Watt per hour for this minute. All those values will be summed and finallydivided by 1000 to provide the energy consumption in kWh for this work day.

• CalculateEnergySaving: (provides: energySaving; dependsOn: normalizedData, startTime-WorkDay, endTimeWorkDay, consumptionBaseline)This algorithm will calculate the number of energy saved per minute between the start and end timeon the work day. Every minute between the start and end time of the work day will be given a value.This value is calculated as the consumption baseline minus the value from the corresponding timefrom the normalized data. If the value from the corresponding time from the normalized data isgreater or equal than the consumption baseline the value will be set to 0.

• SumEnergySaving: (provides: totalEnergySaving; dependsOn: energySaving)This algorithm will sum the energy saving data. This is done in exactly the same way as the energyconsumption is summed for the work day. The resulting number is the energy saving in kWh.

• CalculateEnergySavingPercentage: (provides: energySavingPercentage; dependsOn: totalEn-ergySaving, totalWorkDayConsumption)It will take the total energy saving and total energy consumption and divide the energy saving valueby the consumption value divide by 100 to get the percentage of energy saved.

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Chapter 5

Evaluation and results

In order to determine if our solution actually works, we did a small experiment. In section 5.1 we definethe goal and the set up of this experiment. The experiment gave us the opportunity to collect useful data,which we present and discuss in section 5.1.3.

5.1 Testing the application

After we finished the application, we did an experiment with a test group in the Bernouilliborg. We in-formed the users about our project, that we created an energy competition tool that implements an algorithmto determine energy savings. Before we started analyzing their consumption data, we asked if they wouldhave any problems with this since we do not want to intrude their privacy. After all users accepted, weadded them to the dashboard. To motivate users even more to join the competition, an email is sent dailywith intermediate results. The competition ran a week from June 15th until June 22.

5.1.1 Test group

As mentioned in chapter 1, we have a small test group of five people at the fifth floor of the Bernouilliborg.These five persons represent different types of office occupants. Some test persons work more than othertest persons, while others have a different pattern of energy consumption. Besides this we noted duringdevelopment that some users are actually already busy saving energy by turning of monitors when they arenot needed and turning off computers when they leave the office for a while. All this together gives a diversgroup of users to test the application with.

5.1.2 Hypothesis

The energy savings algorithm as defined in chapter 3 was tested with different data sets and works correctlyfor most cases. Therefor we expect the algorithm will give correct values for energy savings. However, weare not sure if the users will actually save more energy then when they are not involved in the competition.One week is too short to get enough data for a statistically well founded conclusion. That is why thisexperiment is only an indication whether it could be a success or not. To test if our application works,especially in the long-term, we should gather results over a significant longer period.

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5.1.3 Results

When the test week was over we gathered all the data. We took the energy savings percentage of everyoffice occupant and compared it with their energy savings percentage in the month before the test. Thismeans we have two numbers for every office, the energy they saved in percentage before the test week andthe energy saving percentage of the test week. We put these results in figure 5.1. As we can see, the first

Figure 5.1: Results of the test in the Bernouilliborg

test user already saved some energy before competing in the competition. However, while competing userone saved even more energy than he already did. User one saved around 35% during the competition. Thebest performing user in this competition is user two. User two increased the energy savings with a factorof around 5. The third user is the one with the least energy saving percentage. User three is also the leastpresent in the office. This means that when he is around, he turns on the equipment, works and then turnseverything off and leaves. This means he does not have much time to save any energy. The fourth userincreased the energy savings, with a factor just over 3. Besides these increasing percentages, we also haveuser 5. This user actually saved less energy than the weeks before the experiment. Globally this means thatthe energy saving percentage increased from around 20% to just above 30%.

5.1.4 Survey

At the end of the test competition the users were asked to fill in a small survey. The survey consisted ofseveral statements which could be answered on a scale from “Totally disagree” to “Totally agree”. Thestatements questioned various topics regarding the usability of the system, motivation for energy savingsand privacy of the users and system. At total of four of the five users filled in the survey.

Usability

The users did not seem to have any problem with the usability of the system. All users thought the systemwas easy to use. As an improvement the rules of the competition and an explanation of the energy savingsalgorithm could be added to the system. Not every user thought this was very clear.

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Motivation for energy savings

The survey contained several questions regarding the motivation for every savings. First of all, all usersfound it useful, on at least some level, to learn about their electricity consumption at work. Half of theusers thought this raised their awareness regarding electricity consumption at work, the other half answeredneutral.

The users answered three other questions about how different parts of the system motivated them tosave energy. One of the users did not seem to be motivated at all. The other users thought the energydashboard and the overall competition motivated them to save energy. The email notifications did not seemto motivate the users. The email notifications contained the results of the previous day and were meant toremind the users about the competition. A possible explanation for this could that some users did not worktwo consecutive days. At the moment they received the email notification they were not at work.

As a last question on motivation, the users were asked if they maintained interest even after 2 weekspassed. Most users were still slightly interested, but there is some room for improvements here. Someother incentives might keep the users more interested over a longer period of time.

Privacy

Finally, the survey contained a few questions regarding privacy. Most users did not think measuring theirenergy consumption is a violation of their privacy, although they did not totally disagree. On the otherhand, none of the users thought the dashboard shared too much of their private information.

5.2 Discussion

The results we retrieved during this test are very limited. During our test we found that office occupantssave more energy when competing in the competition than when they have normal consumption behavior.However, we cannot give this as a final conclusion. The test only ran for a week with 5 different users. Toget a better conclusion we should gather way more data. This means a longer test run with more and morediverse users. In this way we should get a better picture about the energy saving percentages. The currentdata cannot give us any statistically founded conclusion. Therefor we can only say that our preliminaryresults give an indication

Besides these discussion points, there are more things that are open for discussion. First of all ouralgorithm to calculate the energy saving percentage. Since we could not find any related work on thistopic, we had to develop our own algorithm. Of course we tested our algorithm and it does what it shoulddo. However, the algorithm could be improved to take more parameters into account. The algorithm aswe developed it is vulnerable for cheaters. We did not focus on that too much, but it is possible to startyour computer, let it run for 10 minutes, shut it down again and be gone till the end of the day to run thecomputer again. This will give a very high energy saving percentage. Maybe it would be possible to takean activity sensor into account to fix this glitch.

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Chapter 6

Conclusion

The goal of our project was to develop a system that encourages users to reduce energy consumption. Inthis thesis we looked at research done in the field of energy saving algorithms. Unfortunately we could notfind any research on this. Therefor we needed to develop an algorithm ourselves to determine the energysaving percentage. Besides this we needed to find out what gamification components we needed. We didfind gamification methods and implemented these in the application.

To find out what would be a good algorithm to determine energy savings, we tested some differentimplementations. After we found out what the right algorithm was we implemented this and also imple-mented the gamification components in the energy competition dashboard. When this was done we testedthe application in a real world environment in the Bernouilliborg.

Unfortunately we could only test the application for one week. We found out that more persons tend tosave more energy while competing in the competition. However, there were some persons who did not savemore energy. Globally around 12% extra was saved during the competing compared to the period beforethe test week. Since the test period is not long enough for a statistically founded conclusion, we can givean indication that our application could work, at least in the short-term. To determine if it really works inthe long-term, further research should be done.

We are satisfied with the created work and learned a lot about working together on a big project andwriting a thesis. Unfortunately there is no time left to expand the application. We would like it if someoneelse would continue with our project to expand it and test whether it works on the long-term.

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