Bausch, Nils

Tewkesbury, Giles

English

Portsmouth University Research Portal (Pure)

Source Code - GO, Country Code 21.                                                                       Shelmark 4964.150000  
Journal of computing in systems and engineering, 2013 (14).                                                                                    PCR   ISSN  1472-9083 
 
272
 
 
 
 
 
AI AND SENSOR APPLICATIONS IN ’SMART HOMES’: 
CURRENT RESEARCH ACTIVITY AND APPLICATIONS IN 
DOMESTIC HOMES 
 
 
N Bausch and GE Tewkesbury 
 
Systems Engineering Research Group, School of Engineering, University of Portsmouth, PO1 3DJ. 
 
Email: giles.tewkesbury@port.ac.uk 
 
 
 
 
Abstract 
 
This paper introduces the current research in the area 
of Smart Homes and then introduces a modelling 
approach for a domestic home. The modelling 
approach for a domestic home in the second part of 
this paper investigates the inherent properties of a 
house, which could facilitate further research into 
energy flow, timing methods and optimization. 
 
Keywords: domestic, Smart Home, prediction, AI, 
sensor fusion, smart environment, thermal comfort. 
Introduction  
The area of Smart Homes was researched in different 
sub-areas, such as computer science, electronics, and 
medicine. The idea of Smart Homes was not new, but 
had been mentioned and researched several times in the 
past literature, either as pervasive or ubiquitous 
computing [1], which is the implementation of 
computer technology into every day life with as little 
obstruction as possible, aiding the human in daily tasks 
at home. To give an overview on the variety on 
research in algorithm applications, sensor use cases and 
energy management, the following section introduces 
current research projects. It is followed by the project 
the authors of this review are currently undertaking. 
Current research projects 
Research activity has ranged from complete system 
solutions to the technology and algorithms behind such 
systems. Recent reviews [2,3] focused on research 
projects that incorporated complete system solutions, 
and discussed sub areas in Smart Homes. 
Smart Environments. Smart, intelligent environments 
were built and used in different projects. One of these 
projects was the Gator Tech Smart House[4] , 
developed at the University of Florida. The authors 
introduced a Smart Home using the Open Services 
Gateway Initiative (OSGi) technology, a framework 
based on Java. The system consisted of several, so 
called "hot spots", which represented different 
appliances in the house, for example Smart Mailbox, 
Smart Front Door, Smart Plug, and many more. 
The DOG Gateway, researched at the Politecnico di 
Torino [5] in Italy, inter- connected different home 
automation systems. The researchers simplified the 
management of the systems by creating a neutral home 
automation system. 
The Easy Living In Kitchen (ELIK) system, developed 
at the University of Pisa, Italy [6], provided a kitchen 
with a distributed embedded system. The system 
supported the user’s activities, for example with a 
recipe database, controlled the actuators, for example 
the oven and cooking time, helped in safety issues, for 
example finding gas leaks and reporting/logging them 
and made the environment smarter with regard to 
power consumption as well. 
Algorithms and data processing.  A research project 
investigating and applying AI methods in Smart Home 
en- vironments was MavHome (Managing An 
Intelligent Versatile Home) [7],[8], which was 
researched at the University of Texas in Arlington, 
USA. Inside the MavHome, user data was collected 
and analyzed to train and use prediction algorithms. 
Understanding and categorizing the data collected from 
sensors, for example in a Smart Home, was presented 
by Diane J Cook [9], at the Washington State 
University. The author’s paper described tools and 
algorithms that analyzed and processed sensor data. 
Source Code - GO, Country Code 21.                                                                       Shelmark 4964.150000  
Journal of computing in systems and engineering, 2013 (14).                                                                                    PCR   ISSN  1472-9083 
 
273
Energy efficiency and thermal comfort.  Research 
into the energy efficiency of homes was undertaken in 
various areas. The integration of sensor-fusion into 
Smart Homes and the application in night saving 
heating showed an improvement of approximately 30% 
in energy usage [10]. 
The placement of radiators in relation to windows was 
proven to be crucial in heat distribution and airflow in 
a room; based on computational fluid dynamics 
simulations[11]. 
Research into thermal comfort was also undertaken. 
Thermal comfort was a measurement of satisfaction 
within an enclosed environment. A project in France 
observed facial expressions to deduce new methods 
and models of thermal comfort detection [12]. 
The Bundeswehr University in Munich studied 
methods to estimate thermal values in a room without 
disturbing the inhabitants by using thermodynamic 
models in a closed system environment [13]. 
In comparison to the research undertaken to improve 
energy efficiency or the methods of calculations, the 
standard used in ISO 13790:2008 [14] gave detailed 
advice on how to arrive at an Energy Performance 
Certificate (EPC). The methods described in the 
standard used yearly or static determined data, with 
variations for seasons. 
Discussion 
Research into Smart Homes has been active over the 
last few years, as shown in the literature review. The 
areas of input, the data processing and the outputs 
identified during the literature review of Smart Homes 
are shown in Fig 1. The main focus of the energy 
efficiency research in Smart Homes has mainly been 
focused on new technologies and not on the integration 
into already existing homes. Wireless technologies, for 
example ZigBee [15] or meshed sensor networks could 
make a difference. A recent survey showed that the 
improvements provided with EPCs were not followed 
[16], and the use of wireless technologies would enable 
the inhabitants of a domestic home to pay a lower price 
than running cable based solutions through existing 
infrastructure. New buildings and developments were 
able to integrate new technologies in the design phase. 
It can be concluded that one of the key incentives to 
attract inhabitants’ interest in this sector could be 
achieved by integrating low cost modifications to 
housing and approved methods of saving energy. 
The methods to determine energy performance in the 
European Norm (EN) ISO standards were detailed and 
adequate. One of the standards’ weaknesses was the 
reliance on the use of static data and historic data 
collected over years for energy performance 
calculations. A dynamic approach could give a house 
owner a more accurate view of energy performance 
from day to day, instead of being based on yearly data 
or standard years. 
Smart Home Components
Input OutputData Processing
Connected via:
Wired: Ethernet, ModBus, BACnet, KNX/TP1
Wireless: RF, WiFi, ZigBee, Bluetooth
thermal comfort
automation
adapting to user
health care
energy saving
facilitating tasks
predict task
enable ageing at home
HVAC control
PDA/Touchscreen/
Computer input
switches
temperature sensors
occupancy sensors e.g. 
PIR
tracking i.e RFID
thermal imaging
wearable sensors i.e 
smart clothes
internet sources
accelerometer
AI algorithms
databases with event history
sensor-fusion 
user proﬁles
tracking information
middleware
home automation systems
Figure 1 Smart Home components 
Source Code - GO, Country Code 21.                                                                       Shelmark 4964.150000  
Journal of computing in systems and engineering, 2013 (14).                                                                                    PCR   ISSN  1472-9083 
 
274
Sensor applications in a domestic home 
The following approach will use different kinds of 
sensors to help create and refine a model of a domestic 
home. A first draft of the model can be seen in Fig 3. 
The figure and the approach will be described in the 
following subsections. 
 
The house as a model.  A domestic home is subjected 
to inputs and outputs, for example rain, wind, solar 
radiation, inhabitants, seasonal change, air temperature, 
gas, electricity, transferred heat, waste or air 
temperature in the inside. The inputs and outputs 
regarding temperature development are mentioned in 
Fig 2. 
Capturing the inputs and outputs could be achieved by 
measuring elements, such as temperature sensors, 
pyranometers or gas meters. Using these sensors in 
calculating accurate energy performance for buildings 
were methods encouraged by the ISO 13790:2008 and 
subsequent standards. The measurements with and 
installation of accurate sensor equipment equipment 
were advised by the World Meteorological 
Organization [17] to acquire data accurate enough for 
the calculations of energy performance certificates. 
Some of the data was acquired over a longer period of 
time, sometimes years, if an average estimate was 
desired. In fast changing environments these 
procedures could be enhanced with dynamic 
approaches. 
On this basis, the idea of a model-based approach is 
introduced. In such a model, a domestic home could be 
viewed as a system with specific inputs and outputs. 
The connections between outputs and inputs will be 
partly formulated and interim results defined. The 
testing of this model will be carried out in an ongoing 
experiment as well as system responses of a domestic 
home. 
Use cases for the model could be: optimization of the 
energy efficiency, the time the heating needs to reach a 
certain set-point temperature, or the energy required to 
maintain a certain set-point temperature. 
The connections and relations in the interim and actual 
data could be retrieved by using AI applications, such 
as ANNs, finding data patterns or classifying and 
clustering of data, as described in [9] by Diane J. Cook. 
An approach to the outputs and interim results used in 
the data analysis is shown in Fig 3. In addition to the 
aforementioned inputs, thermal comfort was also 
considered. It will be an additional target that should be 
addressed when modeling a domestic dwelling, as the 
inhabitants’ thermal comfort was dependent on the 
temperatures and temperature developments inside. 
 
Input Output
house
properties/
model  
coefficients
- temperatures 
- energy usage
Variable
- weather 
conditions 
- energy input
- inhabitants
- inhabitants
- step input
- day/night
Figure 2: Input  and outputs to a domestic home 
Source Code - GO, Country Code 21.                                                                       Shelmark 4964.150000  
Journal of computing in systems and engineering, 2013 (14).                                                                                    PCR   ISSN  1472-9083 
 
275
  
actual 
outside 
air temp
daylight 
hours
Colour key:
measured data
calculated data
model properties
forecasted  
outside air temp
house 
emissivity
inside air/core 
temp 
distribution
predicted 
energy 
input
forecasted 
wind
forecasted 
rain
date & 
time
actual sensor/ 
security system/
diary/
Timer
web 
scraping
sensor or 
given values
calculated 
values
model 
parameters
time of 
vacancy
actual 
outside 
surface 
temp
actual 
inside core 
temp
actual 
inside air 
temp
predicted 
inside core 
temp
predicted 
inside air 
temp
actual heating 
water return 
temp
actual gas 
input
actual heating 
water input 
temp
inside temp 
prediction
location
Prediction model /
vacant space
emissivity 
inﬂuence on 
inside temp
predicted heating 
inﬂuence on inside 
temp
target inside 
preferred 
temp on 
return
planned 
heating and 
heating 
demand 
proﬁle
possible 
heating 
proﬁles
target 
heating 
demand
user 
proﬁle
target 
heating 
proﬁle
knowledge
 side
application
 side
outside 
environmental 
inﬂuence on inside 
temp
energy 
efﬁciency
Figure 3: Initial layout of the model for prediction 
Source Code - GO, Country Code 21.                                                                       Shelmark 4964.150000  
Journal of computing in systems and engineering, 2013 (14).                                                                                    PCR   ISSN  1472-9083 
 
276
 
How to build the model with sensor data.  The 
temperature data will be gathered with sensors placed 
at different locations inside the domestic home. These 
sensors will then be probed for temperature readings at 
a given interval. A specific term was phrased during 
the initial layout of the model - core temperature. It 
referred to a wall inside a domestic home which is not 
an outside wall or connecting to another adjacent house 
and made out of solid material It was expected, that the 
rate of change affecting the core temperature would not 
change as fast as outside temperatures. This 
phenomenon can be seen in Fig 4, showing a sample 
recording of temperature data. The other temperatures 
of interest for the model will be selected dependant on 
their impact and correlation to this core temperature. 
The other sensory data will be scraped from web sites, 
such as internet forecasts and weather station data close 
to the location of the domestic home. Together with the 
inside house data, this data will be analysed and used 
for following purposes: 
1. Create model parameters, which are so far outside 
impact, house emissivity and heat influence/distribution  
2. Aid in heating profiles based on forecasts and 
aforementioned parameters  
3. Build applications that use the parameters and 
predictions  
4. Improve and adjust the current model to reflect the 
real world   To achieve these goals, neural networks 
(NN) will be used as well as data mining tools. 
The domestic home as a system. Apart from 
collecting sensor data continuously, experiments will 
be carried out to find a system response. The output of 
these experiments will be similar to Fig 5. In blue the 
step input and in red the response over time. Such an 
experiment would for example be carried out by having 
a start condition and subject the system to a step input 
and recording the response over time. These results 
will give an insight into the heating parameter. 
Figure 5 Expected result for model response 
The model vs thermal comfort.  The model will 
include thermal comfort in the future. Devices to 
collect user input are installed and enable the user to 
input if the temperature felt is too warm, too hot or ok. 
This data will be used as an opposing goal compared to 
the optimal energy efficiency. Therefore a balance 
needs to be found in which both are satisfied, that is the 
user as well as the optimal energy efficiency. This 
optimization problem could be addressed with findings 
on optimal control [18]. In our experiment the user 
input will be used to build a profiling and it will 
influence the predicted heat. 
Current status.  The current model has to be tested 
and experiments to be undertaken. As soon as the 
experiments start, the heating parameter can be 
derived. The outside impact parameter can be derived 
from existing continuous data and the emissivity 
parameter can be derived from data collected inside 
and outside the house. 
Conclusions 
Selected current research projects were presented and 
the topic of energy efficiency discussed. The second 
part of the paper introduced a modelling method for a 
domestic home as a system with inputs and outputs. 
The incentives to look into and optimize heating 
systems were improvements to the existing EPC, 
representation of a domestic home as a system and also 
to help improve the two thirds of old heating systems in 
the UK [19]. As soon as the system response 
experiments have begun, more results will be 
published. 
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Source Code - GO, Country Code 21.                                                                       Shelmark 4964.150000  
Journal of computing in systems and engineering, 2013 (14).                                                                                    PCR   ISSN  1472-9083 
 
277
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AI and sensor applications in 'smart homes'

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