AbstractThis paper reports the development of a new methodology for examining all associations and correlations between various household appliance behaviour, thereby discovering hidden patterns of occupant behaviour in residential buildings. The method is based on a basic data mining technique (association rule mining). Its strength lies in its ability to analyse both continuous and nominal data, and examine all associations and correlations automatically. To demonstrate its applicability, it was applied to the measured end-use electricity data in a selected residential building with comprehensive household appliances in Japan. The results show that both direct and indirect associations between occupant behaviour were discovered. The results obtained could provide a deep insight into the interaction between different behaviour, offer detailed recommendations for reducing building energy consumption, and enable the best input parameters of occupant behaviour prediction models to be identified

Han, J.

Li, H.Q.

Li, J.

Yu, Z.

Zhang, G.Q.

Elsevier - Publisher Connector 

1876-6102 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the CENTRO CONGRESSI INTERNAZIONALE SRL
doi: 10.1016/j.egypro.2015.11.024 
 Energy Procedia  78 ( 2015 )  591 – 596 
ScienceDirect
 
6th International  Building  Physics  Conference,  IBPC  2015 
 
A novel methodology for identifying associations and correlations 
between household appliance behaviour in residential   buildings 
Z. Yua,*,  J. Lia,  H.Q.  Lia, J. Hana,  G.Q. Zhanga 
aCollege of Civil Engineering, Hunan University, Hunan, Changsha, 410082, China 
 
 
 
 
Abstract 
 
This paper reports the development of a new methodology for examining all associations and correlations between various 
household appliance behaviour, thereby discovering hidden patterns of occupant behaviour in residential buildings. The  method 
is based on a basic data mining technique (association rule mining). Its strength lies in its ability to analyse both continuous and 
nominal data, and examine all associations and correlations automatically. To demonstrate its applicability, it was applied to the 
measured end-use electricity data in a selected residential building with comprehensive household appliances in Japan.  The 
results show that both direct and indirect associations between occupant behaviour were discovered. The results obtained could 
provide a deep insight into the interaction between different behaviour, offer detailed recommendations for reducing building 
energy consumption, and enable the best input parameters of occupant behaviour prediction models to be identified. 
 
© 2015 The Authors. Published by Elsevier Ltd. 
Peer-review under responsibility of the CENTRO CONGRESSI INTERNAZIONALE SRL. 
 
Keywords: Occupant behaviour; Building energy consumption; household appliance; Datamining; Association rule mining 
 
 
 
1. Introduction 
 
Occupant behaviour has a significant impact, both direct and indirect [1], on residential building energy 
consumption. In recent years increasing interests have been attracted in investigating occupant behavio ur as well as 
its impact qualitatively and quantitatively. For e xa mple, different models, including probabilistic models [2, 3], 
Markov  Chain  models   [4],  and  decision-tree  based  models   [5]  have  been  proposed  for  predicting occupant 
 
 
 
 
* Corresponding author. Tel.: +1-731-8882-1040; fax: +1-731-8882-1040. 
E-mail address: jerryzhuny@hotmail.com 
Available online at www.sciencedirect.com
© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the CENTRO CONGRESSI INTERNAZIONALE SRL
592   Z. Yu et al. /  Energy Procedia  78 ( 2015 )  591 – 596 
 
behaviour. Moreover, various methodologies have been developed in order to discover behaviour patterns [6] and to 
improve occupant behaviour [1,7]. These studies provide a deep insight into its effect on building energy 
consumption. However, very few studies focus ed on the complex interactions between various occupant behaviour, 
particularly associations and correlations, though such interaction plays a crucial role in understanding occupant 
behaviour. This is possibly caused by two reasons: first, commonly a lot o f behaviour is involved and it is difficult 
for researchers to use traditional statistical methods to analyse the associations and correlations between every 
combination of them [8]; Second, occupant behaviour in residential buildings, mainly household appliance 
behaviour such as turn on/off TVs, is normally treated as nominal variables and thus is difficult to be processed 
analytically. In order to address the above two issues, we developed a new methodology for e xa mining all the 
associations and correlations between different household appliance behaviour, occupant behaviour and its possible 
influencing factors (e.g. user/building-related parameters and climatic data), thereby discovering hidden patterns of 
occupant behaviour. The method is based on a basic data mining technique (association rule mining). 
 
2. Methodology 
 
A new methodology is proposed for efficiently identifying all the associations and correlations between various 
household appliance behaviour. The proposed methodology can be demonstrated in a four-step process, as shown in 
Fig. 1. 
 
 
 
Fig. 1. Proposed four-step methodology 
 
Each step is briefly explained as  follows: 
 
Step 1 Measure and store end-use electricity use data in residential buildings. Hourly or daily end-use data of 
various household appliances need to be measured for a season or a year; 
Step 2 Remove outliers of the collected data; 
Step 3 Identify all the associations and correlations between various household appliance behaviour through  a 
data mining technique, association rule mining (ARM); 
Step 4 Discover the hidden patterns of occupant behaviour and extract further useful knowledge based on the 
achieved associations and correlations. 
 
In the following section, ARM and the method for removing outliers are briefly introduced. 
 
2.1. Association rule mining 
 
ARM is a widely-used data mining technique and its objective is to discover all the association rules of various 
Data measurement and collection 
Outlier removal 
Mining association rules in the data 
Knowledge discovery 
 Z. Yu et al. /  Energy Procedia  78 ( 2015 )  591 – 596 593
 
investigated variables that are frequently associated together. To better understand association rules and how it is 
mined, a hypothetical e xa mple is given. Assume that 100 families live in a same building and each family has both a 
TV and a computer. Moreover, 80 families turn on their TVs and 60 families turn on their computers. If 40 families 
turn on them simultaneously, it can be calculated that these 40 families account for 40% of all the families (40/ 100 = 
40%), and 50% of the families who turn on their TVs (40/80 = 50%). Then, an association rule can be made to 
display the tendency for the families  to turn on their computers when they turn on their TVs: 
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In the above rule, support and confidence are two parameters that indicate its validity and certainty. Users can set 
different thresholds for them in order to filter uninteresting rules, thereby discovering useful knowledge eventually. 
Mathematically, support and confidence can be calculated as follows: 
�������ሺ՜ሻൌሺ׫ሻ (2) 
����݅�����ሺ՜ሻൌ ሺȁሻ (3) 
Where: 
 X, Y: premise and consequence in the sequence, respectively; 
P(X׫Y),  P(Y|X):   probability and  conditional probability, respectively. 
Another parameter, lift, can be used to demonstrate the correlation between X and Y: 
 
݈݅��ሺ՜ሻൌሺ׫ሻ
ሺሻሺሻ
ሺȁሻൌ
ሺሻ
(4) 
A  lift  value greater than 1 represents  a positive  correlation  while a lift  value less  than  1 represents  a negative 
correlation. If the value is equal to 1, there is no correlation. In order to conduct ARM, the value of quantitative 
variables needs to be discretized into categorical values. In this study a two-interval scale (i.e., HIGH and LOW) 
was applied to each variable of end-use electricity loads, representing relatively high and low frequency behaviour. 
In addition, we employed the open-source data mining software W EKA [9], along with the Apriori algorithm, to 
mine association rules due to its high efficiency and wide applicability. For the specific algorithm of the Apriori the 
reader can refer to [10]. 
 
2.2. Identification and removal of outliers 
 
Outliers are those data objects whose values are distant from others in the database and thus are usually indicative 
of measurement errors. Identification and removal of outliers are crucial to the process of ARM, since outliers may 
have a significant effect of skewing the partition of data into different intervals. Various methods have been 
developed for the identification and removal of outliers. In this study a method based on the lower quartile (Q1) and 
the upper quartile (Q3) of the standard boxplot was adopted due to its simplicity [11]. Specifically, data values that 
are less than Q1 – 1.5 × (Q3 – Q1) or larger than Q3 + 1.5 × (Q3 – Q1) were defined as outliers and removed. 
 
3. Data collection 
 
From December 2002 to November 2004, a two-year research project entitled “Investigation on Energy 
Consumption of Residents All over Japan ” was carried out by the Architecture Institute of Japan [12]. Daily end-use 
electricity data of different household appliances, as well as indoor/outdoor parameters, were measured in 80 
residential buildings in different districts in Japan. Fig. 2 shows the measuring instruments which were used to 
monitor temperature and consumptions of electricity, gas, and/or kerosene. 
594   Z. Yu et al. /  Energy Procedia  78 ( 2015 )  591 – 596 
 
 
   
 
 
Fig. 2. Measuring instruments for electricity, gas, kerosene and air temperature 
 
For demonstration purpose, one building with comprehensive household appliances was selected for data analysis. 
Table 1 shows the basic information of th is building. Table 2 shows the household appliances in the building and 
some measured environmental parameters. All these parameters were used in ARM. 
 
Table 1. Basic information of theselected building 
 
Energy sources by usage 
Building 
type 
Number of 
occupants 
Floor area 
[m2] 
Heat loss 
coefficient 
[W/m2K] 
 
Space 
cooling 
 
Hot water 
supply 
  
Location 
Kitchen  
Detached 
house 
 
3 
 
109.3 
 
1.77 
 
Electrical 
 
Electrical 
 
Electrical 
Honzyo, 
Tohoku 
 
Table 2. Household appliances and environmental parameters 
 
No. Items No. Items No. Items 
1 Refrigerator 13 Electric water heater 2 25 Living room temperature 
2 Bedroom outlet 14 Washing machine 26 Living room humidity 
3 Air conditioner (AC, stairs, 2F) 15 Microwave oven, ricecooker 27 Bedroom temperature 
4 AC (bedroom) 16 TV  28 Hot water supply 
5 AC (living room, 2F) 17 Personal computer 29 AC (total) 
6 Light (living room) 18 Video 30 Light (total) 
7 Living room outlet 19 Telephone and fax 31 Kitchen (total) 
8 Children room outlet 20 Warm-water toilet seat 32 Sanitary (total) 
9 Other outlets 21 Humidifier 33 Entertainment (total) 
10 Dishwasher 22 Outdoor air temperature 34 Total energy use 
11 Medical machine 23 Outdoor relative humidity 35 Others (unclear items) 
12 Electric water heater 1 24 Outdoor air velocity   
 
Due to the seasonality of occupant behaviour, seasonal data was mined instead of annual data. In particular, the 
summer data in 2003 was pre-processed and mined to generate association rules. 
 
4. Results and discussion 
 
4.1 Associations and correlations between various occupant behavior 
 
In order to filter potentially useful association rules from all the obtained rules, a support of 50%, a confidence of 
 Z. Yu et al. /  Energy Procedia  78 ( 2015 )  591 – 596 595
 
90% , and a lift of 1 were set as minimum thresholds. Consequently, 521 rules were obtained and five ru les between 
household appliances behaviour were selected for demonstration, as shown in Table 3. 
 
Table 3. Selected association rules (min_supa* = 50%, min_conf b*= 80%, min_liftc*=1) 
 
 
 
 
 
 
 
 
 
a* Minimum support, b* Minimum confidence, and c* Minimum lift. 
 
Rule 1 shows a positive association between Video and Light (living room). Thus, it is highly possible for the 
occupants to use Video and Light (living room) simultaneously. This may have occurred since the occupants always 
watched videos with lights on. Accordingly, an effective way of saving energy is to watch videos with dim light or 
even switching the light off if the occupants do not feel visually uncomfortable. 
 
Rule 2 can be explained in a similar way. It can be inferred that the occupants usually watched TV and used their 
computer at the same time. If these two machines are used by the same occupants, then it can be recommended that, 
if not necessary, the occupants should use either of them at a time so as to reduce energy consumption . 
 
Rules 3 and 4 show that Kitchen (total) and Air conditioner (total), Medical machine, Dishwasher and Microwave 
oven, rice cooker have a strong positive association. From these two rules, it can be inferred that occupants normally 
used  medical  machine  during  cooking  time   with  air conditioners  being  turned  on .  Also,  dishwashers   were 
frequently used together with the microwave oven and rice cooker. Building researchers could take 
advantage   of  this   information   in   future   behaviour  modelling   and   prediction,   particularly   in    identifying input 
parameters. 
 
An une xpected result was that Refrigerator and Living room outlet have a strong positive association with a 
confidence of 96% and a lift of 1.06, as shown in Rule 5. As a piece of kitchen equipment, refrigerators are 
commonly connected to kitchen outlets. However, this ru le shows that in this building the refrigerator might be 
connected to living room outlet. Further field surveys are still necessary for confirmation . Similar to Rules 3 and 4, 
this information may be helpful in future building energy demand modelling. 
 
4.2 Associations and correlations between occupant behavior and environmental parameters 
 
Environmental parameters were also involved when conducting ARM in order to find associations between these 
parameters and household appliances behaviour. Two rules were selected for demonstration, as shown in Table 4. 
 
Table 4 Selected association rules (min_supa* = 50%, min_conf b*= 80%, min_liftc*=1) 
 
No. Premise Conclusion Sup. Conf. Lift. 
Rule 1 Outdoor air temperature [LOW] Other (unclear items) [HIGH] 0.54 0.91 1.44 
Rule 2 Outdoor air temperature [LOW] Kitchen (total) [LOW] 0.6 1 1.3 
 
These two rules show that outdoor air temperature has a negative association with Other (unclear items) and a 
positive association with Kitchen (total). This indicates that the decrease in outdoor air temperature tends to increase 
the  usage  of unclear  appliances  but  reduce  the  likelihood  that  occupants  prepare  and  cook  food. A possible 
No. Premise Conclusion Sup. Conf. Lift. 
Rule 1 Light (living room) [LOW] Video [LOW] 0.51 0.98 1.02 
Rule 2 TV[LOW] Personal computer [LOW] 0.58 0.87 1.07 
 
Rule 3 
 
Kitchen (total) [LOW] Air conditioner(bedroom) [LOW], Medical 
machine [LOW] 
 
0.75 
 
0.97 
 
1.15 
Rule 4 Dishwasher [LOW] Microwave oven, ricecooker [LOW] 0.61 0.86 1.05 
Rule 5 Refrigerator [HIGH] Living room outlet [HIGH] 0.51 0.96 1.26 
596   Z. Yu et al. /  Energy Procedia  78 ( 2015 )  591 – 596 
 
e xplanation for the latter is that the decrease in air temperature may cause the occupants to lose their appetite to 
some extent. Therefore, these rules can help understand the factors influencing household appliance behaviour. 
 
5. Conclusions 
 
A new data mining-based methodology for e xa mining all associations and correlatio ns between various 
household appliance behaviour in residential buildings has been developed. In order to demonstrate its applicability, 
this methodology was applied to analyse the measured end-use electricity data in a selected residential building with 
comprehensive household appliances in Japan. A number of potentially -useful association rules were found, and 
associations and correlations between different household appliance behaviour were discovered. Based on these 
rules, detailed recommendations for reducing building energy consumption were offered to the occupants. Moreover, 
building researchers could take advantage of these associations in future behaviour modelling and prediction, 
particularly in identifying input parameters. However, considering that daily data was used, the obtained rules do not 
necessarily indicate that household appliance behaviour in the rules occur simultaneously. Future studies should 
focus on mining more detailed rules based on high resolution data such as hourly data. 
 
Acknowledgements 
 
This work was financially supported by the National Natural Science Foundation of China (No. 51408205), the 
Fundamental Research Funds for the Central Universities and Hunan Provincial Science and Technology Major 
Project of China (No. 2011FJ1007). 
 
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A Novel Methodology for Identifying Associations and Correlations Between Household Appliance Behaviour in Residential Buildings 

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A Novel Methodology for Identifying Associations and Correlations Between Household Appliance Behaviour in Residential Buildings

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