=Paper=
{{Paper
|id=Vol-2590/short31
|storemode=property
|title=Smart Home Systems Design Approach with the Thermal Management Problem Example
|pdfUrl=https://ceur-ws.org/Vol-2590/short31.pdf
|volume=Vol-2590
|authors=Andrey Tsaplin,Alexey Platunov,Vasiliy Pinkevich
|dblpUrl=https://dblp.org/rec/conf/micsecs/TsaplinPP19
}}
==Smart Home Systems Design Approach with the Thermal Management Problem Example==
https://ceur-ws.org/Vol-2590/short31.pdf
Smart Home Systems Design Approach with the
Thermal Management Problem Example
Andrey Tsaplin1[0000−0002−5119−8323] , Alexey Platunov1[0000−0003−3003−3949] ,
and Vasiliy Pinkevich1[0000−0002−8635−5026]
ITMO University, Kronverksky prospekt 49, Saint Petersburg, 197101, Russian
Federation
tsaptsaplin@gmail.com aeplatunov@itmo.ru vupinkevich@itmo.ru
Abstract. The aim of the research is to develop a new approach to
shaping and implementation of functionality of middle price range smart
home systems. The approach being developed is based on the ideas of a
cyber-physical systems design paradigm and deep insights of distributed
embedded systems architecture. The current state of the smart home
automation systems market and issues of available products are dis-
cussed. The classification of smart home automation systems is given.
The promising way to solve applied issues of smart home systems is
demonstrated with a simple example of the thermal identification of an
object. A brief description of the distributed LMT4Home platform used
in the experiments is given.
Keywords: smart home · cyber-physical systems · energy efficiency ·
object identification · embedded systems design.
1 Introduction
Cyber-physical systems are a modern stage of automation and embedded systems
development, which is characterized by a new level of sensors, actuators, and
computing components integration with the object under control and with each
other, as well as their high computational performance. This stage enables a
quantum leap to a significant increase in the complexity of the solved task using
relatively low amounts of resources and to significantly improve the properties
of the created systems.
Within the area of smart home systems, the application of the cyber-physical
approach and its elements allows us to solve problems that previously required
expensive specific equipment and were present only in mission-critical industrial
systems. At the same time, during the design process it is necessary to consider
the specific features of low-cost off-the-shelf systems for the wide market and
to find a balance between the complexity of the applied tasks, the computing
resources required, and the cost of the necessary hardware.
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 Andrey Tsaplin, Alexey Platunov, and Vasiliy Pinkevich
Thus, some of the key stages in the development of modern smart home
systems are to reveal meaningful applied problems and to find their solutions
that fit the composition of embedded controllers with limited resources connected
to cloud devices (servers and terminals) via unreliable communication channels.
The paper provides a brief overview of the smart home products market,
provides an example of thermal management problem solving using the limited
computing resources of home automation controllers.
2 Home and building automation systems
The topic of home automation has been developing since the mid-’70s of the
last century. Over the past time, a huge number of solutions have been proposed
and implemented. Standards, protocols, families of hardware modules, embedded
software, SCADA, management, and service software have been developed.
Today, a large number of diverse products related to the smart home seg-
ment are presented on the market. Here are some examples: Wiren Board [1],
Ksytal [2], nooLite from Nootekhnika [3]. There are systems from “big com-
panies” such as Google, Apple (Apple Home Kit), Amazon, Xiaomi (Xiaomi
Smart Home Kit) and several others. Russian IT and telecommunication com-
panies such as Yandex, Rostelecom, Megafon, MTS actively develop smart home
products. House and building automation systems based on standard industrial
automation solutions (e.g. ABB i-bus R KNX and ABB-free@home R ) are still
present on the market. Technologies based on a number of standards for indus-
trial wired and wireless networks dominate at this market segment. For example,
KNX is the open international building automation standard (ISO/IEC 14543-
3). Wi-Fi, ZigBee, LoRaWAN and other network standards are widely used. A
lot of various Internet services with different specialization and functionality are
presented for PCs, tablets, and smartphones. These products range from the
simplest hardware consoles (“Logika doma” – “Home logic” application, Blue-
tooth Terminal HC-05), integration applications (IFTTT [4]) to energy analysis
services (Bidgely [5]), monitoring systems (“Nardony Monitoring” project [6])
and full-fledged SCADA (ISaGRAF , SAYMON, iRidium).
From this brief overview we can conclude that the “smart home systems”
class can be divided into a number of categories:
1. The simplest devices of local automation (“smart socket”).
2. Devices with remote control (“GSM socket”).
3. Centralized and distributed automation devices with limited functionality
(simplest applied algorithms, no user programming).
4. Distributed systems with “deep” programmability (are often based on in-
dustrial automation solutions).
5. Scalable intelligent systems with flexible functionality, which should be based
on the principles of cyber-physical systems design [7, 8].
Let us briefly note the main issues of today smart home systems. In the seg-
ment of simple solutions, there is essentially no integration of functions, only
Smart Home Systems Design Approach with the Thermal Management. . . 3
the simplest scenarios and algorithms are available. In fact, there are no pro-
gramming capabilities for the consumer. In the segment of expensive systems,
the user almost completely depends on third-party integration companies if it is
necessary to change or expand the system.
Thus, the possible goal for the designers of smart home platforms today can
be the creation of flexible, intelligent and end-user-customizable home automa-
tion tools in the class of mid-range off-the-shelf solutions.
3 LMT4Home platform
The LMT4Home system [9] is designed for the intelligent control of various
devices in a cottage (country house), office or enterprise area with remote control
and monitoring of the object. Due to the unique LMT4HomeFusion technology,
the system provides a high level of various sensors and actuators integration in
automated control tasks, as well as flexibility in operation algorithms tuning and
good scalability. A variant of the appearance of the controller is shown in Fig. 1.
Fig. 1. A variant of LMT4Home controller hardware
LMT4Home is a cyber-physical system, ready for integration into the Internet
of Things (IoT) and it already actively uses the principles of distributed architec-
ture, supports a variety of communication channels and cloud services. Reliable
autonomous intelligent control is provided by LMT4Home built-in algorithms,
while cloud-based monitoring, analysis, and prognosis allow expanding the ca-
pabilities and make the person’s communication with smart home automation
as comfortable and efficient as possible. LMT4HomeFusion technology is aimed
at providing functional integration of smart home services.
The system allows you to combine a wide range of devices from smart sockets
and electricity meters to smartphones and tablets. The LMT4Home hardware
platform is built with highly reliable components, allowing operation in a wide
temperature range (–40...55 ◦ C). The application of industrial design and im-
plementation standards guarantees reliable operation of the system over a long
period of time (more than 10 years).
LMT4Home is permanently evolving. This primarily refers to the expanding
base of applied algorithms. The main topics here are a set of thermal manage-
4 Andrey Tsaplin, Alexey Platunov, and Vasiliy Pinkevich
ment tasks, electrical energy management, improving the flexibility and conve-
nience of a human-machine interface (HMI).
4 Smart home systems implementation principles
The most important principles that should be implemented in a system designed
using the proposed approach can be stated:
1. Implementation of adaptive algorithms that correct their behavior with re-
spect to the system working history.
2. The smallest possible amount of required initial setup and configuration, the
optimal “average” mode of operation must be preset.
3. At least two sets of parameters: a simple set for the novice user, and an
advanced set for fine-tuning.
4. High level of fault tolerance and survivability, adaptation to failures and
graceful degradation of applied functions.
5. Self-diagnosis, issuing warnings about the present and potential problems,
recommendations for improving the system and the controlled object.
Formal approaches and methods that can be used to analyze data about the
object and the control system: analytical models, spline interpolation, neural
networks, fuzzy logic, statistical processing, ontologies, and others. Today, they
are more often united under the title of “Big Data Analysis”. The designer’s
goals are to look for a solution with minimal algorithmic complexity for a resi-
dent implementation (in the controller) for time-critical functions and to provide
extensive cloud support for advanced analysis.
5 Thermal management problems
As an example, let us consider a set of thermal management problems rele-
vant to a country house (a cottage) with a full-time or partial-time year-round
inhabitation. This is the segment of cottage settlements and summer cottage
cooperatives.
The following typical subtasks can be defined:
1. Object identification.
2. Object monitoring:
– short-term monitoring for real-time control;
– long-term monitoring for identification of degradation processes, evalu-
ation of the effectiveness of cottage repairs, etc.;
– emergency or failure detection.
3. Energy-efficient transfer of the object to a given thermal regime by a given
time (heating, cooling).
4. The thermal regime maintenance (stabilization).
5. Energy-efficient “safe mode” of the object (standby and conservation regimes).
Smart Home Systems Design Approach with the Thermal Management. . . 5
6. Prediction of the state of the object in the future (short-term, long-term
prognosis).
7. Management in an emergency (failure) situation (minimization of the dam-
age, estimation of risks).
Some of these subtasks overlap with other areas such as electrical energy
management, security, etc., but this article deals with their thermal aspect.
While requesting a minimum amount of information from the user, it is
necessary to obtain the characteristics of the object for the subsequent use of
this model in energy-efficient heaters management.
The developed approach is aimed at obtaining the necessary data about the
object without detailed room blueprints and thermal models provided by the
user. This will allow implementing a user application interface that has reason-
able complexity and solves thermal problems with limited controller resources,
in offline mode, if necessary.
6 Example of thermal identification of an object for a
cottage
Let us show an example of applying the proposed approach to the problems
of thermal management in a cottage using the LMT4Home controller. Thermal
sensors are connected to the controller to monitor the temperature inside and
outside the building. Also controller drive heater control relays.
We use a simple thermal model of the room based on the electrothermal
analogy. We simulate the room as an RC circuit that can receive, accumulate
and give away heat (thermal energy). We need to calculate the thermal resis-
tance of the walls in the room and the rate of cooling/heating using the real
monitoring data. For this experiment, we have chosen a room with dimensions
of 2.8x1.4x2.5 m, with an electric heater with a power of 0.35 kW . The area of
the walls, ceiling, and floor of the room is 28 m2 .
The coefficient of thermal resistance that shows the resistance of a wall with
an area of 1 m2 can be calculated as follows:
(t1 − t2 ) · S
R= , (1)
Qavg
where S is the area of all the surfaces of the room, t1 and t2 are the temper-
atures on the outer and inner surfaces of the wall, Qavg is the average power of
the heater.
The controller maintains a constant temperature in the room, turning the
heater on and off in thermostat mode. As a result, the required average heater
power is determined by the temperature difference inside and outside the room.
Table 1 shows the monitoring data and calculated R values for three time
periods during October 2019. Fig. 2. shows the experimental data used for the
second case. The calculated R values are quite close to each other. As the value
6 Andrey Tsaplin, Alexey Platunov, and Vasiliy Pinkevich
The average The average
Coefficient
Heater temperature temperature
Experi- Time, of thermal
power on the exter- on the inter-
ment hours resistance
Qavg , W nal surface nal surface 2 ◦
R, m W· C
t 1 ,◦ C t 2 ,◦ C
1 20 140 7 23 3.2
2 20 113.5 9 23 3.45
3 11 190 0 23 3.39
Table 1: Diagrams of temperature obtained from sensors and heater status
Fig. 2. Diagrams of temperature obtained from sensors and heater status
of the coefficient of thermal resistance for further use, we take the average value
2 ◦
of the results obtained: Rexp = 3.346 m W· C .
Let us also calculate thermal resistance for this wall according to the reference
data using the formula R = h/λ as we know the composition and properties of
the materials. h is the thickness of the layer of wall materials and λ is the
coefficient of their thermal conductivity.
The wall consists of a layer of wood with a thickness of h = 0.05 m (λ =
0.05 m·W◦ C ) and mineral wool with a thickness of h = 0.15 m (λ = 0.045 m·W◦ C ).
The resistances of the individual layers of the material should be summed up.
2 ◦
As a result, we get Rtheor = 3.666 m W· C .
A comparison of the theoretical and experimental values of thermal resistance
shows a good correlation and demonstrate the applicability of this method for
processing real monitoring data.
Smart Home Systems Design Approach with the Thermal Management. . . 7
As a next step, we determine the rate of temperature change in the room.
Thermal engineering used in the construction of buildings utilize a variant of the
classical Newton’s law of cooling to determine the time needed to change the
temperature of an object in a medium with a constant temperature:
tenv − t1
R = β · ln , (2)
tenv − t2
where tenv – environment temperature, ◦ C; t1 – the initial temperature of an
object, ◦ C; t2 – the temperature of an object after z hours, ◦ C; β – coefficient
of heat accumulation of a building, hours; z – time, hours.
The most reliable, sufficiently accurate and simple way to determine the
coefficient of heat accumulation β is the practical measurement of the air tem-
perature change in the room with the heating turned off and with stable outdoor
temperature in cloudy, calm, windless weather without precipitations. Table 2
shows the calculation data for two time periods during October 2019. The for-
mula for calculating β is derived from (2).
Average The temper- Coefficient
Initial tem-
Experi- Time, external ature at the of heat accu-
perature
ment hours temperature ◦ end of experi- mulation β,
t1 , C
tenv ,◦ C ment t2 ,◦ C hours
1 16 5 24 18 42.2
2 16 -1 24 16 41.5
Table 2: The coefficient of heat accumulation calculation
The average value of βexp = 41.85 hours. Now, the calculated data can be
used to predict the temperature in the room, as well as the time necessary for
heating and cooling, amount of energy to warm up the room at the required
time, energy consumption optimization.
Let us verify this by calculating the time required to warm up the room,
and following comparison with the experimental data. From the perspective of
the above calculation formulas, turning on the heater in the room is equivalent
to increasing the outdoor temperature by the amount of temperature difference
between the room and environment that the heater can maintain. This difference
can be calculated as derived from (1): ∆t = (R · Q)/S, and in this case (with
Q = 350 W ) it equals to 41.8 ◦ C. Then the formula (2) can be used as:
tenv + ∆ t − t1
R = β · ln , (3)
tenv + ∆ t − t2
Let us provide the real conditions for warming up the room for the formula
(3): tenv = 6 ◦ C, t1 = 11 ◦ C, t2 = 23 ◦ C. The calculated time is 16.5 hours.
However, according to real data, 10 hours have passed.
As can be seen from this test, the results differ by more than 1.5 times, but
for the simplest model used this is already a pretty good result. To improve the
8 Andrey Tsaplin, Alexey Platunov, and Vasiliy Pinkevich
quality of calculations, more complex models can be used. For example, it should
be taken into account that when a room cools down, the walls give heat away
first, and the air cools later. But when a room is warmed up with a heater, things
are vice versa: the air warms up first. Using the current model, the results can
be significantly improved by using the separate value of β calculated during the
heating of the room. As another option, an average value calculated for cooling
and heating can be used.
The presented example shows that simple models with a minimum of data
about the object are acceptable and meet the requirements of practical appli-
cations. The user only needs to set the location of the temperature sensors and
assign a relay to control the heater. The heater power and room area are not
required in this model, because it is enough to calculate the complex parameter
(R ·Q)/S using the formula (1) with information about the fraction of the heater
active time in thermostat mode.
Permanent monitoring of the object during the system operation allows the
system to refine the parameters of the models and adapt to changes of the
object (sensor relocation, heater change or room redevelopment), as well as give
recommendations to the user on the power of the heater, improving the thermal
insulation of the room, etc.
7 Conclusion
While the problems of country house automation may seem to be solved, the
practice shows an extensive range of open problems. The real needs (expecta-
tions) of the user from the low-cost smart home system greatly differ from what
the market offers. Research and development activities, some examples of which
are presented in this work, certainly are promising.
Application of the proposed principles for implementing the smart home
automation features demonstrates the ability to achieve a sufficiently high level
of user service with limited resources. The LMT4Home platform selected for
experiments met expectations as an effective solution for real automation of
objects and as a rather powerful and convenient tool.
References
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5. Bidgely. URL: https://www.bidgely.com.
6. “Narodny Monitoring” project. URL: https://narodmon.ru.
7. Lee, Edward. ”Cyber Physical Systems: Design Challenges”. University of Califor-
nia, Berkeley. Technical Report No. UCB/EECS-2008-8. Retrieved 2008-06-07.
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