Actual Consumption Estimation Algorithm for Occupancy Detection

using Low Resolution Smart Meter Data

Shunichi Hattori and Yasushi Shinohara

Central Research Institute of Electric Power Industry, 2-11-1 Iwadokita, Komae-shi, Tokyo, Japan

Keywords:

Occupancy Detection, Smart Meter, Electricity Consumption, Non-intrusive Load Monitoring.

Abstract:

This paper proposes an actual consumption estimation algorithm that achieves highly accurate occupancy

detection using electricity consumption data derived from smart meters. In Japan, electricity consumption

data on households will soon be available because smart meters, which enable electric power companies to

monitor how much electric power people are using in each household, have been installed in all households.

Occupancy detection is a major technique that leverages electricity consumption data and can be applied

to various services such as ambient assisted living, sales promotions, and peak load shifting. However, it

is difﬁcult to conduct high-accuracy occupancy detection using electricity consumption data automatically

derived from smart meters because of their low resolution: 30-min intervals and 100 Wh increments. An

actual consumption estimation algorithm is therefore proposed to generate data that reﬂects the characteristics

of a household’s state from low-resolution smart meter data. Occupancy detection is implemented using the

estimated consumption data, which are generated by the proposed algorithm, and the results of experiments

show that its performance is improved compared to the result obtained using raw smart meter data.

1 INTRODUCTION

In Japan, the restructuring of the electric power mar-

ket began in the 1990s and full deregulation of the

electric power market started in April 2016. Whole

new services creating additional value for the sales

departments are desired because this deregulation

brings intensive competition from new entrants for the

existing electric power companies.

At the same time, network-connected and remote-

controlled electricity meters called smart meters have

been installed in all households for the purpose of au-

tomated meter reading. The standard communication

method with a smart meter is called the A-route and

it enables electric power companies to monitor how

much electric power people are using in each house-

hold. That is, electricity consumption data for all

households are becoming available without any ad-

ditional cost through the use of A-route. Analysis tar-

geting the consumption data derived from the A-route

is therefore a promising approach for developing use-

ful and competitive services for electric power com-

panies.

Occupancy detection (Chen et al., 2013;

Kleiminger et al., 2015; Molina-Markham et al.,

2010) leveraging electricity consumption data is

known as one of the major non-intrusive load mon-

itoring techniques (Hart, 1992). This technique

was mainly developed in Europe and the United

States for the purpose of efﬁcient control of Heating,

Ventilation, and Air Conditioning (HVAC) systems.

Moreover, occupancy detection is also considered

to be applicable in various useful services such as

ambient assisted living (AAL), sales promotions,

peak load shifting, and delivery route optimization.

However, it is difﬁcult to conduct high-accuracy

occupancy detection using electricity consumption

data derived from the A-route, because the A-route

readout is low resolution data: 30-min intervals and

100 Wh increments (Komatsu and Nishio, 2015;

Nomura et al., 2014). The data contain phantom

demand variations because of these characteristics

and they degrade the performance of occupancy

detection using machine learning algorithms.

An actual consumption estimation algorithm is

therefore proposed to leverage low-resolution A-route

readout for occupancy detection. Occupancy detec-

tion is implemented using the estimated consumption

data generated by the proposed algorithm and its ac-

curacy, precision, and recall are improved better than

those of results obtained using raw A-route readout.

Related work about non-intrusive load monitor-

Hattori S. and Shinohara Y.

Actual Consumption Estimation Algorithm for Occupancy Detection using Low Resolution Smart Meter Data.

DOI: 10.5220/0006129400390048

In Proceedings of the 6th International Conference on Sensor Networks (SENSORNETS 2017), pages 39-48

ISBN: 421065/17

ing, occupancy detection, and its applications are re-

viewed in Section 2. Section 3 proposes the al-

gorithm that estimates actual electricity consump-

tion data from low-resolution A-route readout data.

The feasibility of the proposed algorithm is discussed

based on the performance of the occupancy detection

in Section 4. Section 5 concludes the paper.

2 RELATED WORK

2.1 Non-intrusive Load Monitoring

The approach used to analyze the change of

appliance-level electricity consumption from aggre-

gated data is called disaggregation (Froehlich et al.,

2011; Zoha et al., 2012). Disaggregation is catego-

rized into non-intrusive load monitoring, which ana-

lyzes loads without any sensor or measuring equip-

ment inside buildings except for the aggregated elec-

tricity consumption meter (Hart, 1992).

Disaggregation is a well-studied problem and

there are many studies that deal with it based on

various characteristics and methods such as har-

monics (Nakano and Murata, 2007), neural net-

works (Chang et al., 2010), and factorial hidden

Markov models (Batra et al., 2014; Kim et al., 2011),

which is an expansion of the hidden Markov model.

It can be said that disaggregation is an approach

that is similar to occupancy detection in terms of ac-

tivity annotation on households or buildings. If such

annotated data regarding appliances are obtained,

the performance of occupancy detection algorithms

should be improve. In addition, more detailed state in-

formation such as sleep, bathing, and cooking behav-

ior might be classiﬁable. However, these approaches

require electricity consumption data with high fre-

quency, generally from 1 kHz to 100 MHz, because

they try to extract the features of appliance-level con-

sumption based on the characteristic pattern or elec-

trical noise of each appliance (Zoha et al., 2012). This

paper does not focus on this approach because the

proposed method is designed for electricity consump-

tion data with low resolution such as A-route readout.

2.2 Occupancy Detection

Occupancy detection is a technique that estimates the

occupancy state of commercial or residential build-

ings (e.g., whether a household is occupied or unoc-

cupied). This technique was mainly developed for the

purpose of energy-efﬁciency optimizations for HVAC

control and can be categorized into two approaches:

intrusive and non-intrusive. In the former approach,

various methods using environmental sensors such

as motion (passive infrared), CO

, acoustic informa-

tion, and doors (magnetic contact) have been pro-

posed (Nguyen and Aiello, 2013). In contrast, elec-

tricity consumption data are mainly employed in the

latter approach (Chen et al., 2013; Kleiminger et al.,

2015; Molina-Markham et al., 2010).

Non-intrusive occupancy monitoring has become

a well-studied problem since Molina-Markham et al.

suggested that occupancy state can be classiﬁed based

on aggregated electricity consumption data (Molina-

Markham et al., 2010). The advantage of the non-

intrusive approach is that it detects occupancy without

the need to install any sensors in buildings. Nowa-

days, it is a major research ﬁeld in some regions of

Europe and the United States, where smart meters

have been installed before the rest of the world.

Fig. 1 shows the occupancy detection procedure

using electricity consumption data based on classiﬁ-

cation criteria. Occupancy is generally detected using

the following steps.

Step 1: Extract features of a training set T =

, . . . } for discriminating occupancy

states and generate a classiﬁcation criteria

based on the features.

Step 2: Input test set T

= {t

, . . . } to the classiﬁ-

cation criteria generated in Step 1.

Step 3: Obtain the set of classiﬁed occupancy states

for T

Using the procedure shown in Fig. 1, Chen et al.

proposed a threshold-based method that classiﬁes oc-

cupancy state using simple criteria such as maximum

value, standard deviation, and range (the change be-

tween maximum and minimum value) (Chen et al.,

2013). Beckel et al. employed machine learning

algorithms such as support vector machine (SVM)

and hidden Markov models for state classiﬁca-

tion (Kleiminger et al., 2015). They also provide the

ECO data set,

which includes pairs of 1 Hz electric-

ity consumption data and the occupancy state ground

truth of ﬁve Swiss households (Beckel et al., 2014).

These studies target electricity consumption data

whose frequency ranges from 1-s to 1-min intervals.

A method targeting low-resolution data, such as smart

meter data in Japan, has not yet been studied. The de-

tailed characteristics of smart meter data communica-

tion in Japan, which is called A-route, are described

in the next section.

https://www.vs.inf.ethz.ch/res/show.html?what=eco-data

[Accessed 4 October 2016]

SENSORNETS 2017 - 6th International Conference on Sensor Networks

Classifier

′

Electricity consumption

Test set





′



Occupied

Unoccupied

Occupied

Occupancy state

′ ′ ′ ′ ′ ′

Classified occupancy state of ′

Step1: Generate classifier

with the features of 

Step3: Output

classified state

Step2: Input test set ′

to generated classifier

Occupied

Unoccupied

     

Electricity consumptionOccupancy state

Training set 

Occupancy state of  (Ground truth)

     

Figure 1: Procedure of occupancy detection from electricity consumption data based on classiﬁcation criteria.

Table 1: Speciﬁcation and Characteristics of Smart Meter Data in Japan.

A-route

Route Specification Advantages

• From 5-s to 5-min intervals

(depending on equipment)

• 1 W increments

Disadvantages

B-route

• 30-min intervals

• 100 Wh increments

• High-resolution data

• Needs no dedicated equipment

• Data on all households are

available

• Need dedicated equipment to

obtain consumption data

• Low-resolution data

• Contain phantom power

variations

2.3 Smart Meters

A smart meter is a network-connected and remote-

controlled digital meter that monitors electricity con-

sumption online for meter reading. These meters

have been installed in some regions of Europe and the

United States. In Japan, smart meters will be installed

in all households as of 2023. Although the main pur-

pose of the installation is automated meter reading for

billing, smart meters also enable electric power com-

panies to obtain time series data regarding electricity

consumption for each household. Occupancy detec-

tion targeting households employing smart meter data

is therefore a promising approach to developing vari-

ous competitive services.

There are two communication routes, called A-

route and B-route, that are used with smart meters in-

stalled in Japan. Table 1 shows the speciﬁcation and

characteristics of these routes. In A-route, electricity

consumption data of all households can be obtained

automatically through the power distribution sectors

of the electric power companies, although they are

low frequency and coarse sampling data (Komatsu

and Nishio, 2015; Nomura et al., 2014). In con-

trast, the route that acquires electricity consumption

data through the home area network is called B-route.

Whereas data with higher resolution are derived from

B-route, dedicated equipment such as a home en-

ergy management system (HEMS) is required to uti-

lize this route. Hence, the utilization of B-route is

currently totally impractical because HEMS is gen-

erally expensive equipment (it costs approximately

USD 1,500 as of 2016).

Hence, data acquisition using A-route is easy and

economical because it needs no extra equipment such

as HEMS. However, utilization of A-route data must

take into account its speciﬁcation: low frequency and

coarse sampling. As shown in Table 1, A-route is

30-min intervals and 100 Wh increments consump-

tion data. In A-route, the fractions of meter readouts

less than 100 Wh are rounded off and carried over to

the next readout in 30 min.

Actual Consumption Estimation Algorithm for Occupancy Detection using Low Resolution Smart Meter Data

500

1000

1500

2000

2500

9:00 9:30 10:00 10:30 11:00 11:30

100

200

300

(b) A-route readout in 30-min intervals and 100 Wh increments

9:30

10:00 10:30 11:00 11:30

Time

12:00

Electricity consumption (W)

Electricity consumption(Wh)

(a) B-route data in 1-min intervals and 1 W increments

Time

Figure 2: Example of smart meter data: (a) B-route and (b)

A-route data.

Figs. 2(a) and (b) show an example of electricity

consumption data over the same period and house-

hold via B- and A-routes, respectively. The household

can be assumed unoccupied after 10:00 because the

consumption and its variation are relatively low com-

pared to those before 10:00. However, the A-route

readout shown in Fig. 2(b) oscillates between values

of 0 and 100 Wh after 10:30 because of the round off

and carry over. These characteristics are fatal for oc-

cupancy detection, which classiﬁes occupancy state

based on features such as standard deviation, because

this phantom ﬂuctuation tends to be regarded as the

characteristics of an occupied state. A method for es-

timating actual consumption which reduces the phan-

tom ﬂuctuation during the unoccupied state is there-

fore needed to detect occupancy with high accuracy

using smart meter data.

2.4 Applications of Occupancy

Detection

Although occupancy detection has mainly been ap-

plied to building automation systems such as HVAC

control in Europe and the United States (Nguyen and

Aiello, 2013), some additional applications are ex-

pected.

AAL.

AAL is a key technology for tackling the prob-

lems of an aging society. Rashidi et al. indicated

that it is imperative to develop AAL technologies

that help older adults in their own homes (Rashidi

and Mihailidis, 2013). Non-intrusive AAL is ex-

pected to be based on occupancy detection be-

cause occupancy detection enables us to moni-

tor the daily behavior of older adults without in-

stalling any equipment or medical devices.

Sales Promotion.

Advertisement delivery systems are another

promising way to exploit occupancy detection

technology. Understanding the lifestyle in each

household could enable just-in-time ad serving,

although it might elicit a negative response re-

garding privacy issues.

Peak Load Shifting.

Mitigating electrical peak demand has been a crit-

ical issue for electric power companies because

it improves the efﬁciency of electric power gen-

eration and reduces generation capacity require-

ments. For example, some Japanese electric

power companies conducted experiments that dis-

tributed coupons to people as an incentive to go

to commercial facilities when electricity demand

is close to the limit of the existing power supply.

Occupancy detection is expected to contribute ef-

ﬁcient encouragement to people who are always

at home during peak time.

Delivery Route Optimization.

Redelivery due to absence accounts for a quarter

of the total delivery distance in Japanese courier

companies.

Delivery route optimization employ-

ing household occupancy states is a promising ap-

plication to reduce redelivery cost and CO

emis-

sions.

The actual consumption estimation algorithm in

the next section, which detects occupancy with high

accuracy using electricity consumption data with low

resolution, is proposed as a fundamental technique for

realizing these applications.

3 ACTUAL CONSUMPTION

ESTIMATION ALGORITHM

In order to estimate occupancy state from A-route

data more accurately, an actual consumption estima-

tion algorithm is proposed. This algorithm estimates

actual electricity consumption based on cumulative

consumption. Figs. 3 and 4 show examples of ac-

tual consumption and A-route readout in 30-min in-

tervals, respectively. Although these two ﬁgures show

the same period and household consumption, A-route

http://www.ft.com/cms/s/0/5670af9e-0b22-11e4-9e55-

00144feabdc0.html [Accessed 4 October 2016]

http://www.mlit.go.jp/report/press/tokatsu01 hh 00023

4.html [Accessed 4 October 2016, written in Japanese]

SENSORNETS 2017 - 6th International Conference on Sensor Networks

100

200

0 0.5 1 1.5 2 2.5 3

Electricity consumption (Wh)

Elapsed time (h)

Figure 3: Actual electricity consumption in 30-min and 1

Wh increments.

100

200

0 0.5 1 1.5 2 2.5 3

Electricity consumption (Wh)

Elapsed time (h)

Figure 4: A-route readout in 30-min and 100 Wh incre-

ments.

readout data differ from the actual consumption be-

cause of the characteristics described in Section 2.3.

In this algorithm, the actual consumption data are

estimated using the cumulative consumption of the A-

route readout. In Fig. 5, which is converted into cu-

mulative consumption from Fig. 4, two stair-like solid

lines are drawn with cumulative A-route readout and

the values with 100 Wh added. The lower line shows

the lower limit of the A-route readout range. In addi-

tion, the upper line shows its upper limit. That is, the

area that consists of these two lines can be regarded

as the range of true cumulative electricity consump-

tion, which monotonically increases. The dashed line

which is the minimal distance within the range area

is calculated, as shown in Fig. 5. The estimated ac-

tual consumption data are ﬁnally calculated as each

increase of the dashed line in 30-min intervals.

This problem can be formulated as shown below.

In these formulas, the minimum increment of con-

sumption (100 Wh) is denoted by l, A-route readout

is x, and estimated consumption is y, respectively.

Minimize

∑

t=1

1 + y

(1)

100

200

300

400

500

600

700

0 0.5 1 1.5 2 2.5 3

Cumulative consumption (Wh)

Elapsed time (h)

Lower limit based on

A-route readout

Upper limit (add 100 Wh

to A-route readout)

Figure 5: True cumulative electricity consumption and esti-

mated cumulative consumption.

sub ject to y

≥ 0

∑

s=1

≤

∑

s=1

≤

∑

s=1

+ l

∑

t=1

∑

t=1

Equation (1) shows that the distance of the estima-

tion line consists of elapsed time and estimated con-

sumption y

. The distance of the estimation line can

be calculated as the sum of the oblique lines, which

show the estimated consumption such as y

and y

Fig. 5. The dashed line minimizing this distance is

determined as the estimated cumulative consumption

in the proposed algorithm.

Each constrained condition corresponds to non-

negativity, the range of actual consumption, and the

consistency of the cumulative consumption. There-

fore, the dashed line that fulﬁlls these conditions cor-

responds to non-negativity cumulative consumption,

which is in the area between the two solid lines in

Fig. 5.

This problem equals the problem shown in for-

mula (2) and its dual problem in formula (3). Formula

(3) is a convex optimization problem called the total

variation optimization problem.

Minimize

∑

t=1

(2)

sub ject to y

≥ 0

∑

s=1

≤

∑

s=1

≤

∑

s=1

+ l

∑

t=1

∑

t=1

Actual Consumption Estimation Algorithm for Occupancy Detection using Low Resolution Smart Meter Data

Minimize

∑

t=1

− x

)

∑

t=1

− y

t−1

(3)

= x

, x

= x

(t > 0)

Optimized cumulative consumption (the minimal-

length dashed line) can be calculated with the algo-

rithm shown below. The algorithm sequentially up-

dates the upper and lower lines, which indicate the

upper and lower limits of acceptable gradients. The

dashed line is ﬁxed when both lines depart from the

range of true cumulative consumption. Finally, the

starting point is updated based on the end-point of the

ﬁxed dashed line.

(1) The starting point and upper and lower lines are

determined (see Fig. 6(a)).

(1-1) Starting point: the terminal of the determined

dashed line.

(1-2) Upper line: A line connecting the starting point

and upper limit of the range of true cumulative

consumption.

(1-3) Lower line: A line connecting the starting

point and lower limit.

(2) Increment the time by 30 min and update the up-

per and lower lines (see Fig. 6(b)).

(2-1) When the upper line is larger than the upper

limit, update the upper line.

(2-2) When the lower line is smaller than the lower

limit, update the lower line.

(3) If both lines depart from the range of true cumu-

lative consumption, the dashed line and starting

point are updated (see Figs. 6(c), (d), and (e)).

(3-1) When the lower line is smaller than the up-

per cumulative consumption, the line connecting

the starting point and contact point with the lower

limit of the area are determined as the dashed line.

(3-2) When the upper line is larger than the lower

cumulative consumption, the line connecting the

starting point and contact point with the upper

limit of the area are determined as the dashed line.

(3-3) The contact point is determined as the new

starting point. The upper and lower lines are up-

dated.

(4) Return to Step 2 unless the time is terminated.

Fig. 7 shows an example of the estimated con-

sumption data, which are generated by the proposed

algorithm from the A-route readout in Fig. 4. It can

be seen that the estimated data in Fig. 7 are closer to

the actual data in Fig. 3 than the A-route readout in

Fig. 4.

4 EXPERIMENTS

4.1 Outline of Experiments

Occupancy detection using actual electricity con-

sumption data was conducted to consider the fea-

sibility of the proposed algorithm. The ECO data

set (Beckel et al., 2014) referred to in Section 2.2

was used for evaluating performance. This data set

consists of the 1 Hz electricity consumption data and

occupancy state on ﬁve Swiss households from June

2012 to January 2013.

In the experiment, the following four types of

electricity consumption data were prepared from the

ECO data set for the performance comparison.

B-route.

Actual instantaneous data in 1-min intervals and 1

W increments. The B-route data is the consump-

tion data with the highest resolution in this exper-

iment, and it corresponds to data that could be de-

rived from dedicated equipment such as HEMS.

30-min/1 Wh.

Actual watt-hour data in 30-min intervals and 1

Wh increments. These data correspond to the an-

swer data that the proposed algorithm described

in Section 3 aims to estimate.

A-route.

Watt-hour values in 30-min intervals and 100 Wh

increments. This data corresponds to the readout

derived from a smart meter via A-route.

Estimated.

Watt-hour estimated data in 30-min intervals and

1 Wh increments. The data mean estimated con-

sumption data generated from the A-route readout

by the proposed algorithm.

Occupancy detection was conducted every hour.

Random forests and SVM, which are known as

high-accuracy machine learning algorithms, were em-

ployed for classiﬁcation. The explanatory variables

shown in Table 2 are employed as the feature for oc-

cupancy detection. Note that the temporal resolution

is different from the others in the B-route data.

Although occupancy detection was conducted ev-

ery hour, its state (occupied or unoccupied) is in 1-s

intervals in the data set. The hourly state is deﬁned as

follows.

1. When the entire hourly state is unoccupied, the

state is regarded as unoccupied.

2. When the hourly state is mixed occupied and un-

occupied states, the state is regarded as occupied.

3. When the entire hourly state is occupied, the state

is regarded as occupied.

SENSORNETS 2017 - 6th International Conference on Sensor Networks

ݐ ݐ ݐ

(a) Determine a starting point and

upper and lower lines at

(b) Update the upper and lower

lines at

range at

(d) Fix the dashed line and update

the starting point

(e) Re-update the upper and

lower lines at ݐ

Starting point

Upper or lower line

Fixed dashed line

(estimated consumption)

Range of true

cumulative consumption

Figure 6: Procedure of the actual consumption estimation algorithm.

100

200

0 0.5 1 1.5 2 2.5 3

Electricity consumption (Wh)

Elapsed time (h)

Figure 7: Estimated actual consumption data in 30-min in-

tervals from A-route readout.

That is, the state becomes occupied when it in-

cludes an occupied state of at least 1 s. The state is

used as an objective variable in occupancy detection.

In this experiment, the target hours of occupancy

detection are from 6:00 to 21:59 because almost all

the states during night and early-morning are occu-

pied in the ECO data set.

4.2 Results of Actual Consumption

Estimation

The actual consumption estimation algorithm de-

scribed in Section 3 was applied to the A-route read-

out, which was prepared from the ECO data set. Fig. 8

Table 2: Explanatory Variables for Occupancy Detection.

Variable Description

id Unique ID of household

mean Average consumption in the target interval

max Maximum consumption in the target interval

min Minimum consumption in the target interval

range Difference between maximum and minimum values

std Standard deviation in the target interval

hour Hour when occupancy detection is conducted

temp Average temperature in the target interval

season Summer or winter (dummy variable)

shows the visualized results of the four types data in-

cluding B-route, 30-min/1 Wh, A-route and estimated

consumption. As described in Section 4.1, 30-min/1

Wh consumption data shown in Fig. 8(b) corresponds

to the answer that the proposed algorithm aims to es-

timate. Fig. 8(c) shows that the A-route readout in-

cludes sequentially iterated values consisting of 0 and

100 Wh when consumption is relatively low, as de-

scribed in Section 2.3. In contrast, Fig. 8(d) shows

the estimated result, which suppresses the ﬂuctuations

and represents the actual consumption more precisely

than the raw A-route readout, although it is smoother

than the 30-min/1 Wh data.

As described in Section 3, the estimated consump-

tion data are generated based on the dashed line,

which is the minimal distance within the range of true

cumulative consumption. Given these conditions, the

Actual Consumption Estimation Algorithm for Occupancy Detection using Low Resolution Smart Meter Data

Figure 8: Visualized consumption data of the ECO data set using (a) B-route, (b) 30-min/1 Wh, (c) A-route, and (d) Estimated

data.

proposed algorithm presupposes the continuation of

nearly equal consumption. For that reason, the pro-

posed algorithm tends to smooth the estimated results

when the actual consumption is low or surges quickly.

4.3 Performance of Occupancy

Detection

Occupancy detection was conducted based on the

conditions described in Section 4.1. The variables

shown in Table 2 were employed as features and the

occupancy state was classiﬁed into a binary class: the

household is occupied or unoccupied.

Generally, it is known that classifying a minority

class is harder than the majority one when machine

learning-based classiﬁcation is conducted on imbal-

anced data sets (Japkowicz et al., 2000). In the ECO

data set, classifying an unoccupied state is difﬁcult

because it accounts for only about 20% of the data.

Precision and recall of the unoccupied state are there-

fore employed as the performance criteria in addition

to accuracy, which shows the total rate of classiﬁca-

tion for occupied and unoccupied states.

Figs. 9 and 10 respectively show the performances

of occupancy detection using random forests and

SVM methods, which employ four types of electricity

consumption data: B-route, 30-min/1 Wh, A-route,

and estimated consumption. The performances of

B-route are basically highest, followed by estimated

consumption, 30-min/1 Wh, and A-route in descend-

ing order. The accuracy, precision, and recall of

the estimated consumption data outperformed the 30-

min/100 Wh and A-route data results for both ma-

0.925

0.839

0.771

0.903

0.782

0.715

0.861

0.662

0.623

0.917

0.816

0.752

0.0 0.2 0.4 0.6 0.8 1.0

Performance

B-route 30-min/1 Wh A-route Estimated

Accuracy

Precision

(Unoccupied)

Recall

(Unoccupied)

Figure 9: Performance of occupancy detection by random

forests.

chine learning algorithms. Moreover, these results

are comparable to the results obtained with B-route

data, which corresponds to high resolution consump-

tion data.

The contribution ratio of features by the random

forests classiﬁer is shown in Table 3. The contribution

ratio is used to evaluate the efﬁciency of each explana-

tory variable. This table shows the variables that are

independent of electricity consumption, such as hour

and temp, have relatively high ratios in the results ob-

tained with A-route, whereas max is the highest ratio

in the results obtained with the others. As described in

Section 2.3, A-route readout contains phantom varia-

tion that consists of alternating 0 and 100 Wh val-

ues. The discrepancy between the actual consump-

SENSORNETS 2017 - 6th International Conference on Sensor Networks

0.902

0.752

0.762

0.877

0.690

0.695

0.874

0.707

0.633

0.898

0.750

0.734

0.0 0.2 0.4 0.6 0.8 1.0

Performance

B-route 30-min/1 Wh A-route Estimated

Accuracy

Precision

(Unoccupied)

Recall

(Unoccupied)

Figure 10: Performance of occupancy detection by SVM.

Table 3: Contribution Ratio of Each Explanatory Variable

by Random Forests.

Variable B-route 30-min/1 Wh A-route Estimated

id 0.103 0.131 0.049 0.121

mean 0.183 0.165 0.149 0.173

max 0.211 0.236 0.072 0.208

min 0.149 0.138 0.116 0.130

range 0.129 0.084 0.028 0.032

std 0.093 0.088 0.033 0.023

hour 0.056 0.074 0.232 0.147

temp 0.005 0.073 0.316 0.160

season 0.070 0.009 0.006 0.007

tion and A-route readout tends to be large when con-

sumption is small. As a result, its contribution ratio of

explanatory variables regarding electricity consump-

tion decreased because it does not reﬂect the charac-

teristics of occupied and unoccupied household well.

In contrast, the estimated consumption data are con-

sidered to reﬂect the characteristics appropriately be-

cause its ratio of explanatory variables regarding elec-

tricity consumption is higher than that of A-route.

5 CONCLUSION

This paper proposed an actual consumption estima-

tion algorithm that detects occupancy with high accu-

racy using electricity consumption data with low reso-

lution. The proposed algorithm estimates actual con-

sumption using the cumulative consumption of the A-

route readout and the segmented line that exists within

the range of true cumulative consumption. The exper-

imental results of occupancy detection using the con-

sumption data estimated by the proposed algorithm

show an improvement in performance compared to

the result obtained with raw A-route readout. The re-

sults also show that the estimated consumption data

reﬂects the characteristics of occupied and unoccu-

pied states appropriately.

The proposed algorithm is expected to be useful

for various tasks such as proﬁle analysis of household

attributes based on A-route data. Future work also

includes occupancy detection targeting Japanese do-

mestic households and efﬁcient feature selection for

improving performance.

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