Conference PaperPDF Available

Optimizing Energy Consumption using Fuzzy Logic for HEMS in a Smart Grid

Abstract

Energy consumption minimization and user comfort enhancement in Home Energy Management System (HEMS) are the major challenges in a smart grid. In HEMS, appliances of Heating, Ventilation, and Air Conditioning (HVAC) have a large impact on the energy consumption. For user comfort, one needs to take into account different environmental factors among which humidity plays an important role in determining the suitable temperature for optimal user comfort. In order to minimize energy consumption without compromising user comfort, fuzzy logic techniques are widely used without considering humidity. In this paper, we tune the Fuzzy Inference System (FIS) by including humidity as well as we propose a method for the automatic rule generation for FIS. Automatic rule generation is devised using combinatorics. The proposed system is evaluated by the membership functions of the input parameters and the results are compared with Mamdani FIS and Sugeno FIS. Indoor temperature, outdoor temperature, occupancy, price, initialized set points of thermostat, and humidity are the input parameters of the system. Performance metrics used for the evaluation are energy consumption, Peak-to-Average Ratio (PAR), cost, and efficiency gain. Simulation of one month energy consumption with proposed technique is performed in MATLAB®. Simulation results validate the proposed technique and show that despite all the energy savings, the proposed technique manages to be in the user comfort zone while achieving electricity cost reduction up to 24%. Moreover, optimization using FIS provides the reduced energy consumption up to 28%. The proposed technique seems to have a potential for improved demand-side energy management in a smart grid.
Optimizing Energy Consumption using Fuzzy Logic
for HEMS in a Smart Grid
Qurat-ul-Ain1, Sohail Iqbal1,*, Nadeem Javaid2
1SEECS, National University of Sciences and Technology (NUST), Islamabad
2COMSATS Institute of Information Technology, Islamabad
*Corresponding author: sohail.iqbal@seecs.nust.edu.pk
Abstract - Energy consumption minimization and user comfort
enhancement in Home Energy Management System (HEMS) are the
major challenges in a smart grid. In HEMS, appliances of Heating,
Ventilation, and Air Conditioning (HVAC) have a large impact on the
energy consumption. For user comfort, one needs to take into account
different environmental factors among which humidity plays an
important role in determining the suitable temperature for optimal user
comfort. In order to minimize energy consumption without
compromising user comfort, fuzzy logic techniques are widely used
without considering humidity. In this paper, we tune the Fuzzy
Inference System (FIS) by including humidity as well as we propose a
method for the automatic rule generation for FIS. Automatic rule
generation is devised using combinatorics. The proposed system is
evaluated by the membership functions of the input parameters and the
results are compared with Mamdani FIS and Sugeno FIS. Indoor
temperature, outdoor temperature, occupancy, price, initialized set
points of thermostat, and humidity are the input parameters of the
system. Performance metrics used for the evaluation are energy
consumption, Peak-to-Average Ratio (PAR), cost, and efficiency gain.
Simulation of one month energy consumption with proposed
technique is performed in MATLAB®. Simulation results validate the
proposed technique and show that despite all the energy savings, the
proposed technique manages to be in the user comfort zone while
achieving electricity cost reduction up to 24%. Moreover, optimization
using FIS provides the reduced energy consumption up to 28%. The
proposed technique seems to have a potential for improved demand-
side energy management in a smart grid.
Key words - Smart Grid, Fuzzy Logic, Energy Management, User
Comfort
I. INTRODUCTION
Demand Response (DR) plays an important role in energy
consumption minimization for the smart grid environment.
HEMS is a demand response tool which is an important part of
the smart grid that enables the residential users to create optimal
energy consumption by considering many objectives such as
energy cost, load profiles, and consumer comfort. The
worldwide fossil fuel resources are declining at an escalated
pace which signifies the need of energy management and
minimization [1]. Heating, Ventilation, and Air Conditioning
(HVAC) appliances contribute the major part of total energy
consumption worldwide in the current scenario. HVAC also
shows great effects on peak load management during peak
demand hours especially during summer days. Therefore,
different techniques are used to schedule the HVAC to reduce
peak load. Different pricing mechanisms or tariffs such as Real
Time Pricing (RTP), Time of Use (TOU) [2] and critical peak
pricing [3] are used to encourage the electricity user for
reducing load demand during peak hours. Electricity cost is
determined by the utility at different times of day based on TOU
rates for the High Peak (HP), Mid Peak (MP), and Off Peak
(OP). Based on these values, load control in HEMS is either
shifted or curtailed especially for the HVAC.
A wind driven optimization based energy scheduling
technique [4] is used to reduce the energy cost and PAR by
shifting the load during peak hours to off peak hours. PAR is
useful measure which describe how peak electricity
consumption affects the system. It is often seen that users find
it difficult to remember that they need to update their thermostat
particularly during critical situations. In [5] programmable
communicating thermostat incorporated model which helps to
reduce the PAR along with energy consumption.
Occupancy and user participation are the factors that directly
affect the functionality of thermostats resulting in more optimal
energy consumption and bill savings. Occupant’s activities and
presence has been observed in [6] to evaluate the energy
savings. The user negligence while using thermostat effects the
energy consumption in many cases where customers forget or
neglect to participate in DR during peak prices [7].
Previous research shows that evaluation of energy
consumption for HVAC by varying different parameters is
performed. Energy evaluation is performed using different DR
techniques day-ahead electricity prices, TOU rates, real time
pricing is used to reduce energy consumption and electricity
bills for residential consumers by shifting home appliance from
high peak hours to off peak hours [8],[9]. Challenges that are
often faced during the use of programmable thermostat (PT) is
user’s lack of communication with new technologies such as
smart meters [10]. PTs are improved into Programmable
Communicating Thermostat (PCT) [11] with the advancement
in communication benefitting users to participate in DR
program promulgated by the utility. Currently, types of
thermostat that being used to participate in DR are: 1)
programmable communicating thermostat, 2) price responsive
thermostats, and 3) occupancy responsive thermostat to reduce
the residential HVAC energy consumption. Price responsive
thermostat uses price signals from smart grid and change the
thermostat set points to the values already defined by the
residential users. Occupancy based thermostats sense the
occupancy of the user and modifies the building or room set
points. During these studies, it is observed that user comfort is
heavily sacrificed while participating in the demand response
programs [11]. PCT is used because of its feature of
communicating with the smart meter in order to read the price
Presented at International Conference on Computing and Information Sciences (ICCIS) held at PAF-KIET, Karachi, Pakistan
from March 26-27, 2018.
signals that are decided by the utility on interval basis. PCTs
allows user to participate in DR programs where user can vary
the thermostat set points according to the TOUs tariffs using
the intervals of off peak, mid peak, and high peak. However,
the constant interaction of the user with thermostat often
irritates the residential users making the behavior of PCT as
programmable thermostat (PT).
Keshtkar et al. [12] evaluate the load reduction in HVAC
system using fuzzy logic model. Outdoor temperature, price,
occupancy, and initialized set points are the input parameters of
that system. However, the proposed system lacks adaptability
in thermostat. The system in [13] is the extension of [12] where
authors introduced the adaptive autonomous thermostat. In [13],
system is made adaptive using fuzzy logic approach by training
the thermostat on initialized set points. It considers the three
consecutive changes of same set points for the same day of the
week and then modify the thermostat set point to the optimized
set point. Although, this technique is good but it is limited to
cold regions of the world i.e. Canada in this case. And the result
are reliable only for the country based research.
Extending the idea of [13], a model of worldwide adaptive
thermostat is proposed by Javaid el al. [14]. The proposed
technique uses Fuzzy Logic Controllers (FLC) to set the
thermostat set points. Input parameters of this system are
outdoor temperature, price, occupancy, and initialized set
points for hot and cold cities. Their system is evaluated using
Mamdani and Sugeno FIS. Although this technique showed
good results in energy consumption minimization but there are
multiple parameters that can be considered for the improvement
of results.
This limitation lead us to extend the existing study for further
improvement in the energy consumption minimization using
thermostat set points optimization.
The organization of paper is as follows: in Section II,
problem considered is elaborated while the problem
formulation is described in Section III. Proposed system is
presented in IV and simulations along with the result analysis
are discussed in Section V. The paper is concluded in Section
VI.
II. PROBLEM STATEMENT
Residential HVAC systems contribute to a significant part of
world’s energy consumption. These devices are the primary
electrical load during peak hours which often leads to peak load
blackouts. As the energy prices increases during the peak hours
such as TOU, the household electricity bill is strongly
dependent on the HVAC system. Thermostat is widely used in
order to save energy as well as to maintain the temperature of
residential building in user desired range. PT is the kind of
thermostat used in HEMS where users maintain the set point
temperature on interval basis for day which depicts their
schedule and user preferences [13].
Many fuzzy logic techniques have been developed that
targets to save energy without compromising the user comfort.
It is observed that different factors play a significant role in
determining the thermostat set points which in result affect the
energy consumption. Some of them have been considered in
previous research work but many of them are still missing.
Humidity is an important factor that determines the way a user
sets the set points. There is a need to design a model that
incorporates the humidity to show the effect of humidity while
setting the thermostat set points in order to reduce energy
consumption without much compromising on the user comfort.
It is known that generating and writing the rules for the FIS
is a very tedious and time consuming task. Measuring the effect
of different parameters on the energy consumption is a
beneficial methodology that will help us in setting the
thermostat set points in a way that not only minimizes the
energy consumption but the temperature will also remain
within the user comfort range. But increase in the number of
input parameters increases total numbers of rules to be defined
hence increasing the complexity of defining the rules for the
FIS. Defining an automatic way to set the rule base is an
important task.
III. PROBLEM FORMULATION
This section discuss the details of the formulation of
proposed scheme, energy consumption, cost, PAR for HEMS.
The proposed system is developed using fuzzy logic rules and
evaluated using FIS. Fuzzy logic technique has a major
advantage as compared to ON-OFF control as controlled
variables used in this study varies continuously during a period
of time [15]. FLC responds very well to these changes. The
input and output of FLC are real variables which are connected
through IF-THEN rules to achieve the desired output. The
major advantage of FLC as compared to other controllers is its
requirement of little mathematical modelling. Another reason
for using the FLC is that the rules defined are purely on human
intuition which is effective and more expressive. Mamdani and
Sugeno are among the types of FIS that are most commonly
used for evaluation. The input parameters used in this study are
directly related to energy management and user comfort in
residential buildings. Energy consumption is evaluated with the
help of fuzzy rules. In this paper, energy consumption is
calculated by considering humidity and without humidity.
A. Mamdani and Sugeno FIS
FIS takes the crisp inputs, fuzzifies it, apply fuzzy operators
on premise (antecedent), perform implication from premise to
conclusion (consequent), aggregate conclusion across fuzzy
rules to generate fuzzy output and defuzify it to get a crisp
output. The model proposed is evaluated and tested using the
Mamdani and Sugeno FIS.
Mamdani FIS uses linguistic variables for the rules and its
premise and conclusion are both linguistic variable. Fuzzy rules
are generated using the linguistic variables. e.g.
.
Outdoor- " " Indoor-
" " Rate " "
" " " " " ",
" "
IF Temp is Normal AND Temp is
Normal AND is High Peak AND Occpancy is
Absent AND ISP is Low AND Humidity is Low
THEN energyconsumption is Low
Defuzzification method used in Mamdani FIS is centroid
which is calculated using the formula [16]:
 
 
z . zdz
C
zz dz
C
(1)
Sugeno FIS takes the premise part as a linguistic variables,
however, its conclusion part is function which can be zero order
(constant) or first order. Fuzzy rules are generated using the
function which is efficient, for example:
Outdoor- " " Indoor-
" " " "
" " " " " ",
= ( ,
in
IF Temp is Normal AND Temp is
Normal AND Rates is High Peak AND Occpancy is
Absent AND ISP is Low AND Humidity is Low
THEN energyconsumption energyconsumption temp
tem , , , ) ,
out
p price occupancy ISP humidity
Defuzzification method used in Sugeno FIS is weighted
average which is calculated using the formula:
z . z
C
zz
C
(2)
To conclude, Mamdani is intuition based that is well suited
for the human input whereas Sugeno is computationally
efficient method and is well suited for the mathematical
analysis [17].
In order to calculate the total cost following formula is used:
( ) ( )* ( )Cost h EC h Rates h
(3)
Here,  is the hourly cost whereas  is the
electricity consumption on hourly basis and  are the
hourly pricing tariffs based on TOU.
PAR is calculated using the formula as follows:
max ( )
max 0 1
() 1
1
0
a
Pn
nN
PAR anN
Pavg an
Nn
 

(4)
Fig. 1 Comfort Zone using Psychrometric Graph [20]
IV. SYSTEM MODEL
In this manuscript, an extension to the adaptive fuzzy
learning model [13] and worldwide adaptive thermostat model
[14] is developed. Proposed technique introduces humidity
along with the existing input parameters like outdoor
temperature, indoor temperature, prices, occupancy, and set
points of thermostat in hot and cold cities for energy
consumption minimization without disturbing the user comfort.
The two cities considered for analyzing the cooling and
heating power consumption in any residential building around
the world are the Wadi Halfa in Sudan and Yakutsk from Russia.
Wadi Halfa is one of the hottest cities in the world and Yakutsk
is the coldest city in the world. Then, we have selected one of
the hottest and coldest day from the respective cities. Highest
temperature in Wadi Halfa has been recorded during the month
of June. Whereas, coldest weather in Yakutsk has been
observed during January. Outdoor temperature for Wadi Halfa
and Yakutsk is taken form weather forecasting website [18] and
[19] respectively. The initialized set points are used for
controlling the indoor temperature for both cold and hot cities
are defined using the psychrometric chart mentioned in [20].
Comfort zone defined in Fig. 1 shows the temperature range
that can be used as thermostat set point with respect to a
particular relative humidity value which results in little
disturbance on user comfort. Values for occupancy and TOU
pricing tariff are taken form [13].
In Fig. 2, computation model used in this system is depicted.
Input parameters provided to the FIS are measured by
deploying sensors. This system works for the cold and hot cities
along with their thermostat heating and cooling points. The
price is communicated to user by utility using smart meters of
the residential buildings. These values are used to set
thermostat set points.
Fig. 2 HVAC Control System in HEMS
Inputs of the system are indoor temperature, outdoor
temperature, price, occupancy, thermostat set points, and
humidity. Membership functions defined for the indoor
temperature (  ) and outdoor temperature
( ) are: 1) Very Cold (VC), 2) Cold (C), and 3)
Normal (N) for cold cities whereas for hot cities the
membership functions are 1) Normal (N), 2) Hot (H), and 3)
Very Hot (VH). The user occupancy () has two membership
functions: 1) Absent (A) and 2) Present (P). Price () is
defined on the basis of TOU tariff according to which
membership functions are defined as: 1) Off Peak (OP), 2) Mid
Peak (MP), and 3) High Peak (HP). The membership function
used for thermostat set points ( 
) and humidity
() are: 1) Low (L), 2) Medium (M), and 3) High
(H). Output parameter of this system is energy consumption
(EC). The output membership functions are 1) Very Low (VL),
2) Low (L), 3) Medium (M), 4) High (H), and 5) Very High
(VH). Figs. 3-5 shows the membership functions of some
parameters used in this system as well as the defined ranges:
Fig. 3 Outdoor Temperature for Hot Cities
Fig. 4 TOU Price Rates
Fig. 5. Relative humidity membership function
The membership functions of input and output parameters
used in this system are defined as trapezoidal which has a flat
top or it can be said it is a truncated triangular membership
function. Although the triangular membership function is
simple to use but the parameters used in this study are best
defined using trapezoidal membership function since the
temperature, set points, price and humidity do not suddenly
drops their value and maintains same value for a length of time.
So, these flat line membership functions have the advantage of
simplicity [21].
The system is evaluated with the help of fuzzy rules in order
to determine the energy consumption. When FIS is defined
without incorporating humidity, there are 4 variables with 3
values and fifth variable with 2 values. In this case, total
number of rules defined in the rule base for both Mamdani and
Sugeno FIS are 162. In the second scenario, FIS considering
humidity has 5 variables with 3 values and sixth variable with
2 values resulting in total four hundred and eighty six (486)
rules in the rule base. Some of the fuzzy rules defined for FIS
decision making are shown in the Table 1.
Table 1. Sample Fuzzy Rules for Energy Consumption Optimization.








1
L
L
HP
A
L
L
VL
2
L
M
OP
P
L
L
M
3
L
H
MP
P
H
H
M
4
M
H
OP
A
M
H
H
5
M
L
MP
P
H
M
M
6
H
M
OP
A
L
M
M
7
H
H
OP
P
H
H
VH
A. Automatic FIS rule Base Generation
It is observed that defining rule for the rule base of FIS is a
very lengthy and tedious process. Developing an automatic FIS
rule generation process using combinatorics method is also
proposed in this paper.
The major steps of FLC are as follows:
First step is the fuzzification process in which all
the membership functions of the system
parameters are initialize and define.
Second step is defining the rules in the rule base by
giving weightage to membership functions of input
parameters and then assigning the suitable output
fuzzy value.
Third step uses the Mamdani and Sugeno FIS to
evaluate the energy consumption.
After rule evaluation, defuzzification is performed
to get the crisp value for the energy consumption.
Calculation of remaining performance measures is
performed.
Formula to compute the  used in the Algorithm 1 is as
follows:
 
   
   
1
0
11
00
11
00
n
Score Temp i
outdoor
i
nn
Temp i P i
indoor rates
ii
nn
O i ISP i
s
ii








(5)
Algorithm 1: Automatic Rule Generator
1:       
2:    
3:  
4:     
5: 
  
8: for  then
9: for    then
10: for  then
11: for  then
12: for 

 then
13: Compute  // defined in Eq. (5)
14: if  = 0 or  =1 then
15:  
16: else if  = 2 or = 3 then
17:   
18: else if  = 4 or = 5 then
19:   
20: else if  = 6 or = 7 then
21:   
22: else
23:  
24: end if
25: end for
26: end for
27: end for
28: end for
29: end for
V. RESULTS AND DISCUSSION
In this section, we are going to discuss the results of our
proposed FLC in HEMS. The proposed controller works for
both cold and hot cities using the inputs: 1) ,
2), 3), 4), 5)
, and 6).
We have simulated the effect of these parameters for four
scenarios: a) energy consumption in hot cities without humidity,
b) energy consumption in hot cities considering humidity, c)
energy consumption in cold cities without humidity, and d)
energy consumption in cold cities using humidity. Furthermore,
all these scenarios are evaluated for following performance
measures: energy consumption, cost, PAR and efficiency gain.
A. Energy Consumption in Hot Cities
Energy consumption computation based on outdoor
temperature and humidity variations during the 24 hours for
one of the hottest city in the world and on one of its hottest day
is performed and the hourly energy consumption is presented
in Fig. 6. Maximum hourly energy consumption of Mamdani
without humidity, Sugeno without humidity, Mamdani with
humidity, and Sugeno with humidity is 5.7kWh, 5.5kWh,
4.5kWh, and 4.5kWh respectively. Our proposed FLC
improves the energy consumption by effectively maintaining
the user comfort up to 21% for both techniques.
Fig. 6 Energy Consumption over a day for Hot Cities
Fig. 7 shows the monthly energy consumption of our
designed FLC using both FIS with and without considering
humidity. Energy consumption shown is calculated by
evaluating and analyzing the fuzzy rule base. In our study, the
energy consumption is low when the initialized set points are
high.
Fig. 7 One month simulation of energy consumption for Hot Cities
We have run this simulation for one month. The energy
consumption of FLC using Mamdani without humidity is
2954kWh, Sugeno without humidity consumes 2837kWH,
Mamdani with humidity shows 2270kWh energy consumption
and Sugeno with humidity consumes 2295kWh energy.
Mamdani with humidity improves 23% energy consumption
while Sugeno with humidity is improving 22% energy
consumption as compared to the energy consumed using
Mamdani without humidity.
The Mamdani FIS performs better than Sugeno FIS because
it is simple in nature and have more energy efficiency. As the
demand of HVAC varies on hourly basis in a residential
building, the set points are modified by using temperature and
humidity information. Thermostat set points are used according
to Fig. 1, where it shows the range temperature that lies in the
user comfort zone for a particular relative humidity value.
B. Energy Consumption in Cold Cities
Now, we are going to discuss energy management in a
residential building using proposed FLC in the cold cities. Input
for the occupancy, TOU prices remains same during evaluation.
Outdoor temperature, relative humidity, thermostat set points,
and indoor temperature are used accordingly to cold cities
weather. The effect of these input parameters using cold cities
is shown in the Fig. 8.
Fig. 8 Energy Consumption over a day for Cold Cities
In Fig. 8, energy consumption of the techniques proposed
and existing technique for comparison is presented. The
maximum energy consumption in cold cities is 6.26kWh,
6.30kWh, 4.5kWh, and 4.5kWh using FIS Mamdani without
humidity, Sugeno without humidity, Mamdani with humidity,
and Sugeno with humidity where Mamdani with humidity and
Sugeno show 28% efficiency in energy consumption then
Sugeno without humidity.
Fig. 9 shows the energy consumption of cold cities run for
one month simulation using Mamdani and Sugeno while
considering and leaving the humidity parameter. The behavior
of energy consumption is maintained at a desired comfort level
using
. Although the energy consumption for cold cities is
greater as compared to hot cities, our proposed system succeed
in energy consumption minimization which shows the
efficiency of proposed scheme to the earlier schemes.
The monthly energy consumption of Mamdani without
humidity is 3630kWh, Sugeno without humidity is 3653kWh,
Mamdani with humidity uses 2959kWh, and Sugeno with
humidity consume 2922kWh of energy. Efficiency in energy
consumption for Mamdani with humidity is 19% whereas in
Sugeno with Humidity is 20% as compared to Sugeno without
Humidity.
Fig. 9 One month simulation of energy consumption for Cold Cities
C. PAR
PAR of the cold cities is shown in Fig. 10, which shows
Mamdani with humidity achieved 12% efficiency as compared
to the Mamdani without humidity whereas efficiency of Sugeno
with humidity is 10%. However, if the simulations are run for
the hot cities no prominent efficiency gain is observed. This is
mainly because the proposed system is mainly focused on the
energy consumption minimization. Improvement in the PAR
efficiency for the hot cities can be regarded as the byproduct of
the proposed scheme.
Fig. 10 PAR for Cold Cities
D. User Comfort
User comfort is mostly sacrificed in previous techniques. In
this scheme we initialized the thermostat according to Fig. 1,
which allows the setting high set points for hot cities and low
set points for cold cities considering a particular relative
humidity values. Therefore, values for 
are selected that not
only reduces the energy consumption but also keeps the
residential environment in user comfort zone.
E. Cost in Hot Cities
Cost reduction is an inevitable consequent of energy
consumption minimization. Cost is computed using the Eq. (3)
and proposed system performs best among all approaches.
Using technique of Mamdani without humidity, cost is nearly
8.92 $, Sugeno without humidity costs 8.6 $, Mamdani with
humidity approach cost 6.79 $, and Sugeno with humidity FIS
costs 6.86 dollar per day as shown in the Fig. 11. Approach
using Mamdani with humidity reduces the cost by 23.87% and
Sugeno with humidity to 23.09% as compared to the method
using Mamdani without humidity. Mamdani outperforms here
because of its simple nature and so having energy efficiency.
F. Cost in Cold Cities
Cost is computed using the Eq. (3) which was also used in
the cost calculation for cold cities. As shown in Fig. 12. Scheme
using Mamdani FIS without humidity costs 11.19 $, Sugeno
without humidity costs 11.25$, Mamdani with humidity costs
9.4 $, and Sugeno with humidity costs 9.3 $ for energy
consumption in a day.
Moreover, Mamdani with humidity shows efficiency of
16.44% in cost reduction and 17.33% efficiency of Sugeno with
humidity as compared to the Sugeno without humidity.
Fig. 11 Cost of energy consumption in a day for Hot Cities
Fig. 12 Cost of energy consumption in a day for Cold Cities
G. Result Analysis
Effect of humidity on energy consumption minimization and
cost reduction is studied in this paper. It is observed that
considering humidity while setting thermostat set points plays
an important role in achieving the defined objectives. It can be
concluded that information of humidity values helps user to set
the 
in such a way that not only reduces energy
consumption but also guarantee little disturbance in user
comfort. Mamdani and Sugeno, both FIS give the reasonable
results and show improvement in comparison of previous
techniques. However the competition between the proposed
Mamdani FIS and Sugeno FIS is very close, but it can be seen
that Mamdani FIS performs better in hot cities whereas Sugeno
FIS outperforms in cold cities.
Following points are concluded by analyzing the
performance of Mamdani and Sugeno FIS used for both hot and
cold cities:
i. in order to maintain the residential building
temperature as close to user comfort zone, cold
regions require more energy consumption as
compared to hot regions because appliances for
heating requires more energy than the appliances
used for cooling purpose.
ii. Reason behind the slightly better performance of
Mamdani in hot cities is the simplest nature of the
rules defined through Mamdani FIS.
Although the variations in outdoor temperature influence the
temperature range of user comfort, but proposed scheme is
minimizing energy consumption without disturbing user
comfort.
VI. CONCLUSION
In this paper, we extended the worldwide adaptive
thermostat model to include the parameter of humidity and
observed the effect it does on the energy consumption. An
algorithm for the automatic generation of the fuzzy rules and
their initialization in rule base is also proposed. Automatic
generation of rules avoids the time consuming process of fuzzy
rules initialization. Proposed scheme of work tracked the
energy consumption of the HVAC in residential buildings
throughout the world along with cost and PAR Simulation
results show that proposed methodology significantly reduced
the energy consumption and cost while maintaining the user
comfort. Efficiency gain in energy consumption using
Mamdani and Sugeno is 23% and 22% whereas cost obtained
using Mamdani and Sugeno is 23.87% and 23.09% efficient
when dealing with hot cities. In cold cities efficiency gain in
energy consumption using Mamdani and Sugeno both is up to
28% whereas cost obtained using Mamdani and Sugeno is
16.44% and 17.33% efficient.
In future, more parameters that effect the user’s decision of
setting the thermostat set points should be considered along
with the closed control loop. It can be extended for using more
pricing schemes in order to observe the real time effect of
dynamic pricing.
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