SMART HOME

From User’s Behavior to Prediction of Energy Consumption

Lamis Hawarah, Mireille Jacomino and Stephane Ploix

Laboratoire G-SCOP, INP Grenoble, UJF, CNRS, 46 Avenue Felix Viallet, 38031 Grenoble, France

Keywords:

Energy consumption, User’s behavior, Bayesian network, Learning system.

Abstract:

This paper concerns a home automation system of energy management. Such a system aims at keeping under

control the energy consumption in housing. The expected energy consumption is scheduled over one day.

Each hour a total amount of energy is available that is a resource constraint for the expected energy plan. The

expected consumption is totally derived from users behavior which are quite different from one housing to

another, and rather difﬁcult to predict. This paper proposes a Learning System to predict the user’s requests of

energy. The proposed method relies on Bayesian networks.

1 INTRODUCTION

A Home Automation System basically consists of

household appliances connected by both energy and

communication networks. Smart Home and more

generally Smart Building are spreading out. They

aims at ﬁrst increasing comfort and security, second

enabling remote access to information about the ap-

pliances and the buildings and third managing the ap-

pliances. The system addressed in this paper is con-

cerned with energy management in Smart Home. It

aims at planning the best energy assignment satisfy-

ing the availability energy constraints and the users’

requests (Palensky et al., 1997), (HA et al., 2006).

In this paper, energy is restricted to the electric con-

sumption. (HA et al., 2006), (Abras et al., 2007)

present a three-layers household energy control sys-

tem: anticipative layer, reactive layer and device

layer. The anticipative layer depicted in (Ha, 2005)

and (Abras et al., 2008) is mainly concerned with the

energy plan. The anticipative plan relies on predic-

tions of environmental parameters (weather forecast,

solar radiation, ...) and energy consumption.

In order to keep under control the total amount

of consumed energy every hour, and then avoid peak

consumptions and minimize the energy cost, the

Home Automation System has to schedule as much as

possible the energy consumptions in the most appro-

priate time periods. For example, the washing ma-

chine could be planned before or after the oven in

a low energy cost period as far as such a plan sat-

isﬁes the predicted user’s request. The efﬁciency of

the anticipated plan is as good as the prediction of the

user’s request. Indeed if the actual user’s behavior is

far from the predicted one, then the reactive layer has

to stop an appliance in order to satisfy the available

energy constraint for example, and schedule this ap-

pliance later without any energy cost optimization.

This paper focuses on the prediction of the user’s

behavior. A Learning System is proposed to predict

the inhabitant’s requests for each hour of a 24 hours

anticipative time period. The system is based on the

use of Bayesian Network to predict the user’s behav-

ior. Bayesian Networks (BNs) are a ﬁeld of Machine

Learning, capable of representing and manipulating

arbitrary probability distributions over arbitrary ran-

dom variables (Russell and Norvig, 2003), (Na¨ım

et al., 2004). They are especially well suited for mod-

eling uncertain knowledge in expert systems (Hecker-

man, 1995). The paper is organized as follows: ﬁrst,

related works concerning the use of household appli-

ances are presented. The next section shows the way

how a Bayesian Network can be used. The proposed

approach to predict the user’s behavior in housing is

explained in section 2. A real database concerning

100 houses in France is used to build standard pro-

ﬁles from which the Learning System deducesthe pre-

dicted user’s behavior. Finally, some results and per-

spectives are discussed.

1.1 Related Works

Various works have been done to study the impact

of the user’s behavior on the energy consumption in

147

Hawarah L., Jacomino M. and Ploix S. (2010).

SMART HOME - From User’s Behavior to Prediction of Energy Consumption.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, pages 147-153

DOI: 10.5220/0002947901470153

 SciTePress

the housings. (Wood and Newborough, 2003), (Wood

and Newborough, 2007) study the interaction be-

tween the user and the appliances. The appliances

are classiﬁed into four categories according to their

level of automation and the number of settings. For

the appliances with low level of automation the user

needs to be in the proximity of the appliances to be

set. They achieved up to 10∼20% reduction in energy

consumption of households by changing the user’s

behavior. Other studies are interested in modeling

and simulating the activities of the user (Zimmer-

man, 2007). The activities of one and several peo-

ple are integrated into simulators of buildings perfor-

mances to get more use dependent results. This ap-

proach models all users as individual agents with dif-

ferent behaviors. Different functions and functional

units such as work places are modelled also. The

main results of this work is that the activities of in-

dividuals and groups in ofﬁce environments can be

modelled on the basis of communicating agents. (Ha

et al., 2006) studies and analyzes the user’s behavior

in housing to make embodiment of user’s interface in

Ubiquitous environment. Behavior patterns are ana-

lyzed by classifying data into 5W(who, what, where,

when and why) and 1H (How). All these works fo-

cuses on the design of displays in order to change the

user’s behavior.

1.2 Bayesian Networks

A Bayesian Network (BN) is a graphical model

for probabilistic relationships among a set of vari-

ables (Pearl, 1986). BNs model causal relation-

ships. They are represented as directed acyclic

graphs, where each node represents a different ran-

dom variable. A directed edge from the node X (cause

node) to the node Y (effect node) indicates that X has

a direct inﬂuence on Y. This inﬂuence is quantiﬁed by

the conditional probability P(Y|X), stored at node Y.

The nodes in a network can be of two types: evidence

node when its value is observed, and query node when

its value has to be predicted. A Conditional Prob-

ability Table (CPT) is assigned to each node in the

network. Such probabilities may be set by an ex-

pert or using a registered data. BNs are based on the

conditional independence; each node is conditionally

independent of its non-descendants given its parents.

When a node has no parent, its CPT speciﬁes the prior

probability. There are two types of learning: 1) the

structure learning in which the best graph represent-

ing the problem is searched; 2) the parametric learn-

ing in which the structure of the network is known

and the conditional probability is estimated at each

node. Once the Bayesian Network is constructed, it

Figure 1: Bayesian Network for Coma problem.

can be used to compute the probability distribution

for a query variable, given a set of evidence variables.

This operation is called inference. For example, one

can identify the causes by calculating the most prob-

able cause given some information (Figure 1), or pre-

dict the effects of a cause by calculating the most fre-

quent value of a node given some observations. Ex-

act and approximate approaches of inference can be

used (Russell and Norvig, 2003), (Na¨ım et al., 2004).

Bayesian Networks are used in a large range of ap-

plications: telecommunications (Ezawa and Schuer-

mann, 1995), display management for time-critical

decisions (Horvitz and Barry, 1995), industry (Hart

and Graham, 1997), health (Becker et al., 1998), com-

munication (Barco et al., 2002), etc.

2 PROBLEM STATEMENT

The objective of the work presented in this paper is to

propose aLearning System able to deliverto the Home

Automation System the useful information about the

energy consumption in a given housing. This useful

information is the prediction of the user’s requests. A

preliminary step consists in identifying a set of stan-

dard proﬁles of the users’ requests. Then the Learn-

ing System has two tasks: 1) First, identify the most

appropriate standard proﬁle for a given user in or-

der to exhibit the corresponding prediction of the en-

ergy consumption at each hour; 2) Second, built the

learnt model of the energy consumption. The stan-

dard proﬁles are deﬁned for each appliance and asso-

ciated service to the inhabitants. They are built us-

ing a database. A questionnaire is used to identify

the most appropriate standard proﬁle for a given user.

The questions concern the appliances in housing, the

frequency of their use, etc.

The proposed method is depicted in the ﬁgure2.

After the standard starting proﬁle has been chosen

for each service it is integrated in a Bayesian Net-

work which will predict the actual user’s requests.

The actual energy consumption of the user is sent

ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics

148

Figure 2: Structure of the Learning System.

to the Learning System also. This observation con-

cerns the actual consumption of the appliances, the

date, the hour, the consumed energy and the duration.

After some time of observation, the Learning System

can build the new proﬁle dedicated to the actual re-

quests of the user. In this paper, the process to iden-

tify the standard proﬁles and the Bayesian model of

the Learning System are described.

3 BUILDING THE STANDARD

PROFILES

3.1 Energy Database

The project Residential Monitoring to Decrease En-

ergy Use and Carbon Emissions in Europe (REMOD-

ECE)

provides an energy database. This is a Euro-

pean database on residential consumption, including

Central and Eastern European Countries, as well as

new European Countries (Bulgaria and Romania).

This database stores the characteristics of the res-

idential electric consumption by country. The IRISE

project is a part of REMODECE. It has been chosen

for our study to identify the standard proﬁles pr(0).

It deals only with houses in France. One database

is available for every house; in such a database, the

information is recorded every 10 minutes during one

year for each appliance in the house . The consumed

energy at every time period by every appliance is

given in this database. However, these data have to

be processed before using.

It is possible to know the number of people who

live in each house. The presence of the user is impor-

tant for the energy consumption but it is not explicitly

known in the database.

http://www.isr.uc.pt/∼remodece/

Figure 3: The database after treatment for the Electric-oven.

Figure 4: The consumed energy in a weekday in January

and in December.

3.2 Data Preprocessing

From the IRISE data, a user’s request concerns one or

more services like cooking in oven, clothe washing,

water heating, etc. A standard proﬁle is a structured

information derived from these raw data for every ser-

vice. The Home Automation System anticipates the

energy consumption from the following information

about the user’s requests:

• When is the service requested?

• How much energy does the service consume?

• What is the duration of the service?

This information is available in the database ex-

cept for the duration. The ﬁgure 3 shows the prepro-

cessed data for one appliance. Each row is the set of

interesting information for one hour in the year: du-

ration, energy, day, month, the number of times that

the appliance has been started during the hour is also

extracted from the raw data.

Analyzing the data one can notice 1) ﬁrst the ac-

tual diversity of the use of each appliance in a given

house and 2) second how difﬁcult it is to characterize

this use. The ﬁgure 4 shows the mean consumption

SMART HOME - From User's Behavior to Prediction of Energy Consumption

149

of the Electric-oven, the Micro-wave and the Washing

machine in a weekday in January and in a weekday in

December. The consumed energy is not identical at

the same hour in the two months. Nevertheless, some

similarities exist. A standard proﬁle gives the prob-

ability that the appliance will be started at each hour

and the associated expected energy consumption for

different types of days statistically representative.

Given only one day, for example Monday, the ob-

tained information is very accurate. But the learning

of such an information would be long, because an ob-

servation and the derived statistical process could be

involved every seven days for one given day in the

week. On the other hand, the average value obtained

overall the year without any differentiation among the

days would not be interesting because the derivedpre-

diction would be an average very far from each actual

request.

3.3 Statistical Picture

A proﬁle is a statistical picture of a service in a hous-

ing. This statistical study is performed over a time

period that is the largest period allowing to compute

signiﬁcant probabilities. A time period may be the

day, the month or the day in a month. For each ser-

vice, for a chosen time period, the proﬁle consists of:

• The conditional probability that the service starts

every hour;

• The average duration every hour;

• The average consumed energy every hour;

The value of the conditional probability, the dura-

tion or the energy for a service are calculated from the

preprocessing data.

It can also be useful to calculate the probability

that the appliance starts at each hour over all the year

without taking into account any time period. This

kind of information can be used to brieﬂy depict the

proﬁles and then identify the most appropriate proﬁle

to a given user.

Parts of the proﬁle of the Electric-oven service

taken in example are depicted in the ﬁgures 5- 9.

These proﬁles concern the house 2000997 from the

IRISE database.

The ﬁgure 5 represents the probability that the

Electric-oven starts at each hour for each month over

one year, from October to September. For example,

the probability that the Electric-oven starts at 6 pm

in October is 0.41 (ﬁgure 6). The ﬁgure 7 shows the

probability that the Electric-oven starts at 6 pm for

each weekday. For example, the probability on Sun-

day at 6 pm is 0.2. The probability that the Electric-

oven starts at 6 pm in October on Sunday is 0.60 (ﬁg-

Figure 5: The probability of the Electric-oven in the house

2000997 on months.

Figure 6: The probability of the Electric-oven in the house

2000997 at 6pm on months.

Figure 7: The probability of the Electric-oven in the house

2000997 at 6pm on days.

Figure 8: The probability of the Electric-oven in the house

2000997 on Sunday over all the months.

Figure 9: The Energy of the Electric-oven in the house

2000997 on Sunday over all the months.

ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics

150

ure 8). The average energy of the Electric-oven at 6

pm in October on Sunday is 751Wh (ﬁgure 9). Such

proﬁles can be exhibit for the service duration as well.

4 LEARNING BAYESIAN

SYSTEM

The Bayesian Network is used to predict the user’s

requests. The Conditional Probability Distribution at

each node of the BN is computed from both the stan-

dard starting proﬁle and the actual observations of en-

ergy consumption in the housing. There are two types

of nodes in this network: 1) the probabilistic nodes in

which a Conditional Probability Table is associated;

2) the deterministic nodes which values are speciﬁed

exactly by the values of its parents, with no uncer-

tainty (Russell and Norvig, 2003). For the determin-

istic nodes, the probability distributions are no longer

needed to be speciﬁed, but instead only certain states.

In this work, all the energetic services in the house

like the cooking service or the washing service, etc

are represented in the Bayesian Network. There are

three causal nodes in the Learning System:

• Hour with 24 values from 0 to 23

• Month with 12 values from January to December

• Day with 7 values from Saturday to Friday

All these nodes are probabilistic.

Three nodes are associated to each service in the

housing:

• Starting of the service with tow values {yes, no}

• Energy which is a deterministic node

• Duration which is a deterministic node

To simplify the presentation of the network, only the

Electric-oven is dealt with during two days (Saturday

and Sunday) in October. The hour values are reduced

to 3 which are {11am, 12am, 1pm}. This network is

given in the ﬁgure 10.

The Conditional Probability Distribution corre-

sponding to the Starting Electric-oven node is part

of the proﬁle of the Electric-oven. Given the hour,

the month and the day, the Home Automation System

uses this network to obtain the probability of starting,

the average energy and the average duration of the

services. For example, if the hour is Sunday 12am,

the Bayesian Network provides the probability 0.4 of

starting for the Electric-oven.

Figure 10: A part of the Bayesian Network.

4.1 Segmentation between Days and

Months

In order to exhibit the standard proﬁles from the

database with the best accuracy, the discriminating

parameters such as days and months have to be found.

For this purpose, a dissimilarity index and a clustering

algorithm are deﬁned.

4.1.1 Dissimilarity Index

The probability that a service starts at each hour given

the day (ﬁgure 7) or the month (ﬁgure 5) is used to

identify if the days or the months discriminate the ser-

vice. Comparison between the months all over the

year is performed, as well as between the days all

over the year. The proposed Dissimilarity index is

based on the Manhattan distance given in the equa-

tion 1. It is used to calculate the difference between

two months or two days over 24 hours.

Dif f(X,Y) =

∑

i=0

− y

| (1)

Where X=x

is the probability that the service starts in

a month (or day) A a each hour i; Y=y

is the proba-

bility that the service starts in another month (or day)

B . The label i represents the hour. Then, the Dissim-

ilarity index is deﬁned as follows:

DI(X,Y) =

Dif f(X,Y)

∑

)

(2)

∑

)

is a normative coefﬁcient from which the

Dissimilarity index measures the relative dispersion

of the starting probability over the months (or days).

Therefore, when the value of DI(X,Y) is small, X

and Y are close together. If DI(X,Y) is large, then

SMART HOME - From User's Behavior to Prediction of Energy Consumption

151

Table 1: Example of the Dissimilarity Index.

Days1 Days2 Diff

∑

)

DI Decision

Sat Sun 0,79 0,93 0,86 YES

Sat Mon 0,5 0,72 0,69 NO

Sat Tues 0,64 0,81 0,79 NO

Sat Wed 0,83 0,95 0,88 YES

Sat Thur 0,54 0,78 0,69 NO

Sat Fri 0,3 0,75 0,39 NO

Sun Mon 0,71 0,87 0,82 YES

Sun Tues 0,85 0,96 0,88 YES

Sun Wed 0,9 1,1 0,82 YES

Sun Thur 0,91 0,93 0,97 YES

Sun Fri y 0,85 0,9 0,94 YES

Mon Tues 0,37 0,76 0,48 NO

Mon Wed 0,6 0,89 0,67 NO

Mon Thur 0,39 0,73 0,53 NO

Mon Fri 0,56 0,7 0,79 NO

Tues Wed 0,38 0,98 0,39 NO

Tues Thur 0,21 0,82 0,26 NO

Tues Fri 0,46 0,79 0,59 NO

Wedy Thur 0,52 0,95 0,55 NO

Wed Fri 0,69 0,92 0,75 NO

Thur Fri 0,36 0,76 0,48 NO

X and Y are quite different. A threshold is arbitrary

ﬁxed to 0.8.

An example is given in the table 1

. In this example

the difference between two days in the house 2000997

for the Electric-oven over all the year is calculated.

The difference between two days is signiﬁcant if the

ratio between Dif f(X,Y) and

∑

)

is bigger than

the given threshold.

4.1.2 Clustering Algorithm

The Dissimilarity index given in the equation 2 helps

the Learning System to decide if two days or two

months can be merged for a given service. Then the

standard proﬁle associated with this service is re-

duced in size and by the same time it will require less

observations to be adjusted to the actual user. This

type of treatment is called Clustering. Clustering can

be considered as the most important unsupervised

learning problem. It is a process of partitioning

a set of data (or objects) in a set of meaningful

sub-classes, called cluster (Zaane, 1999). A cluster

is therefore a collection of similar objects that are

dissimilar to the objects belonging to other clusters.

There are some clustering algorithms like K-means,

k-Medoid, hierarchical algorithm, etc. In this paper,

a clustering algorithm is proposed as follows based

on the Dissimilarity index. The objects can be the

Sat: Saturday, Sun: Sunday, Mon: Monday, Tues:

Tuesday, Fri: Friday, Wed: Wednesday, Thur: Thursday.

months or the days.

Segment( input E: Set of elements, DI: Dissimilar-

ity indexes; Output C: set of clusters C

)

1. Find the closest two elements (e

, e

) ;

DI(e

, e

) =Min{DI(e

, e

∈ E, e

∈ E, i 6= j}}

Add to C

if:

DI(e

, e

) < 0.8, then C

= {e

, e

}

2. If C

is empty then go to the step 7 else go to the

step 3

3. Calculate the set E

= E-C

4. For each element e

of E

if DI(e

, e

) < 0.8 and DI(e

, e

) < 0.8 then

add e

to C

5. Add the cluster C

to C

6. Segment(E - C

, DI, C)

7. For each element e

of E: Add e

to C

8. Return C

This algorithm takes a set of elements which may be

the starting probabilities at each hour over days or the

months. It takes also the Dissimilarity index between

each two elements (table 1). The ﬁrst step of this al-

gorithm consists in ﬁnding the closest two elements

, e

) which have the smallest Dissimilarity index.

Then, for every remaining element from E, the algo-

rithm ﬁnds all the other elements which are closer to

and e

than the given threshold. The obtained set

represents the ﬁrst cluster. This algorithm is recur-

sive. It is iteratively called to ﬁnd all the clusters. It

ends when the remaining Discrimination indexes are

all greater than the given threshold.

For example, the clusters obtained by applying

this algorithm on the table 1 are: C

= {Sat, Mon,

Tues, Wed, Thurs, Fri} and C

={Sun}. That means

that the use of the Electric-oven is different from the

other days on Sunday .

5 CONCLUSIONS AND

PERSPECTIVES

This paper focuses on the prediction of the user’s be-

havior in housing and his derived energy consump-

tion. It is a very important predictive problem in a

Home Automation System. The objective is to con-

struct a Learning System able to predict the user’s

ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics

152

behavior in housing with regards to his energy con-

sumption. The proposed system builds a set of pro-

ﬁles from the IRISE databases for each appliance. A

proﬁle is deﬁned by the probability that the associated

service starts, the average consumed energy and the

average duration. Also, each proﬁle is characterized

by the set of days and the set of months during which

the consumption is speciﬁc. A questionnaire is pro-

posed to the user concerning the use of its appliances.

The comparison between the response of the user and

the set of standard proﬁles allows to provide start-

ing standard proﬁles to the Home Automation System.

These values are introduced into a Bayesian Network

to be adjusted with the actual consumption of the user.

Future works will be dedicated to perform the seg-

mentation to the IRISE data in order to identify the

standard proﬁles. Then a questionnaire will be de-

ﬁned and the way how to process the comparison be-

tween the response and the standard proﬁles will be

addressed.

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