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Comfort Evaluation of Seasonally and Daily used Residential Load in Smart Buildings for Hottest Areas via Predictive Mean Vote Method

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Comfort Evaluation of Seasonally and Daily used Residential Load in Smart Buildings for Hottest Areas via Predictive Mean Vote Method

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In this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mamdani (BPSOF-MAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonally used appliances are considered for the analysis of the efficient energy management through these controllers. Energy management is performed using the two Demand Side Management (DSM) strategies: load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of the daily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances, i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are applied in this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of the hottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occupancy, price, decision control variables, priority and length of operation times of the appliances, whereas the output parameters are: energy consumption, cost and thermal and appliance usage comfort. Moreover, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger's predictive mean vote method. The comfort is further investigated by incorporating the renewable energy sources, i.e., photovoltaic systems. Simulation results show the effectiveness of the the proposed controllers as compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energy consumption and cost of the proposed scenario.
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1
Comfort Evaluation of Seasonally and Daily used
Residential Load in Smart Buildings for Hottest
Areas via Predictive Mean Vote Method
Sakeena Javaid, Nadeem Javaid
COMSATS University Islamabad, Islamabad 44000, Pakistan
Corresponding author: nadeemjavaidqau@gmail.com
Abstract—In this paper, two energy management controllers:
Binary Particle Swarm Optimization Fuzzy Mamdani (BPSOF-
MAM) and BPSOF Sugeno (BPSOFSUG) are proposed and im-
plemented. Daily and seasonally used appliances are considered
for the analysis of the efficient energy management through
these controllers. Energy management is performed using the
two Demand Side Management (DSM) strategies: load scheduling
and load curtailment. In addition, these DSM strategies are
evaluated using the meta-heuristic and artificially intelligent
algorithms as BPSO and fuzzy logic. BPSO is used for scheduling
of the daily used appliances, whereas fuzzy logic is applied
for load curtailment of seasonally used appliances, i.e., Heating,
Ventilation and Air Conditioning (HVAC) systems. Two fuzzy
inference systems are applied in this work: fuzzy Mamdani and
fuzzy Sugeno. This work is proposed for the energy manage-
ment of the hottest areas of the world. The input parameters
are: indoor temperature, outdoor temperature, occupancy, price,
decision control variables, priority and length of operation times
of the appliances, whereas the output parameters are: energy
consumption, cost and thermal and appliance usage comfort.
Moreover, the comfort level of the consumers regarding the usage
of the appliances is computed using Fanger’s predictive mean vote
method. The comfort is further investigated by incorporating the
renewable energy sources, i.e., photovoltaic systems. Simulation
results show the effectiveness of the the proposed controllers as
compared to the unscheduled case. BPSOFSUG outperforms to
the BPSOFMAM in terms of energy consumption and cost of
the proposed scenario.
Index Terms—Energy management, thermal comfort, appli-
ance usage comfort, fuzzy logic, fuzzy inference systems.
I. INTRODUCTION
In Smart Grid (SG), energy management is classified in
two categories. One is supply side management and other is
Demand Side Management (DSM). Supply side management
deals with efficient measurements which make the electricity
production, transmission and distribution possible. It is helpful
in maximizing the revenue of power stations at reduced
economical costs by making the power delievery system
flexible and self healing. Whereas, DSM comprises on certain
strategies: making plans, implementation and scheduling the
tasks of main power stations that motivate end-users for
altering their load profiles and activities. Mostly, DSM aims
to motivate end-users for shifting their loads towards low
demand hours, i.e., during night periods or at weekends. As
DSM offers huge costs incentives, so, this is followed by
the end-users widely [
1
]-[
6
]. Multiple DSM techniques are
already presented in [
7
] for effective load management. These
techniques are based on: prioritized load shifting, strategic
load conservation, peak clipping, valley filling and incentive
based pricing tariffs. In addition, multiple power scheduling
algorithms are also described in [
8
], [
9
] in order to minimize
electricity bill along with comfort enhancement. In [
10
], authors
have proposed global model based energy management system
for scheduling home load. Another DSM methodology [
11
]
has been developed in order to get the optimal cost for the
ice preservation where dynamic real time pricing scheme is
considered and this work has achieved the desired results.
The abovementioned studies [
7
]-[
11
] are inclined towards
cost reduction, where length of operation time of appliances
is considered for effective load management. For instance,
a feasible scheduling time for dwelling’s devices has been
discussed in [
12
] using binary linear programming. Demand
response strategy is described using Monte Carlo simulations
in [
13
], where Time of Use (ToU) rates are considered for
defining the optimized schedules of power grid. Another multi-
objective optimization algorithm has been developed for the
optimal assignments of the household demands in specified time
intervals [
14
]. This scheme has reduced the load by shifting the
load to low demand hours. Focusing on the energy management,
user preferences and their comfort standards are considered
pertinent in residential buildings. Different authors have already
proposed several solutions for the assessment of user comfort
[
15
], [
16
]. In [
17
]-[
19
], authors have elucidated three categories
to determine comfort for maintaining the living standards of
the residential buildings’ users. These three categories are:
thermal, visual and air quality comforts. Existing solutions in
[
17
] and [
20
] are based on the Predictive Mean Vote (PMV)
and mathematical methods for determinig comfort levels of end-
users in residential buildings. In our proposed work, we have
focused on thermal and appliance usage comfort. Appliance
usage comfort is another type of comfort which is based on
specifying the appliance scheduling priorities, time slots and
delay timings. The motivation and existing limitations for our
work are described in the subsequent subsection.
A. Motivation and Problem Statement
The proposed work is motivated by the following existing
studies. In [
21
], the authors have discussed the cost-oriented
model for cloud computing resources. They have categorised
the cloud instances as the on-demand and reserved instances
2
for facilitating the consumers. They have considered the power
and price of the cloud computing resources in response to
the consumers’ demands. Another cloud computing scenario
is presented for electric vehicle charging and discharging
services using the public service supply stations in [
22
]. The
purpose of this scheme is based on the minimization of
the peak formation during the high demand intervals. The
demand supply graph is taken as a constraint in this scenario.
Authors in [
23
] have discussed the preliminary necessities
of the smart homes for the home automation. Seven points
are discussed: heterogeneity, self-configuration, extensibility,
context awareness, usability, security, privacy protection and
intelligence. In our work, we will elaborate the concept of
heterogeneity in detail where different types of the appliances
will be taken in account. We will highlight that either the loads
are thermostatically controlled, non-thermostatically controlled,
or other shiftable and non-shiftable. The concept of self-
configuration, extensibility and context-awareness will be
assumed accurate in our work.
A new Building Energy Management System (BEMS) is
proposed in [
24
], where thermostatically controlled appliances:
Heating, Ventilation and Air Conditioning (HVAC) systems
are considered using the cloud computing environment. In our
work, we will integrate appliance priorities for minimizing the
control decision delay for the required services requested by
the consumers. The machine to machine system is developed
for the comfort evaluation in three ways: thermal comfort,
illumination comfort and appliances-usage comfort [
25
]. An
adaptive fuzzy logic system is proposed to automate the
thermostates of the residential buildings. Our proposed work
is based on the Mamdani Fuzzy Inference System (FIS) and
Sugeno FIS with the integration of the meta-heuristic algorithm
for load optimization and comfort evaluation. According to the
motivation, the existing studies have the following limitations:
1) user comfort is not considered and appliances are not
categorised; either these are thermostatically controlled or non-
thermostatically controlled except the aggregated demand of
the consumers [
21
]. 2) Whereas, in [
22
], cost is not computed,
only electric vehicles’ peak load minimization is considered.
The scope of the work is limited, consumers’ comfort is not
calculated which is the essential part of this work. 3) Control
decision delay is increasing due to cloud based system. In
addition, impact of the delay is not computed [
24
]. 4) Work
in [
26
] describes that residential thermostats are automated;
however, their work does not consider the thermal comfort of
the appliances. In the proposed work, two energy management
systems are proposed to manage the load of the daily and
seasonally used appliances. The seasonally used appliances
are alternatively referred to as HVAC systems in the our work
and the requirements of the smart homes are taken as basic
assumption. In addition, the thermal comfort is also calculated
using the Fanger’s PMV indexing method.
B. List of Contributions
This work is the extension of [
47
]. Based on the above
problem statement, there are following list of contributions of
this work. These contributions are elaborated as under.
1)
Two energy management controllers are proposed and
implemented, i.e., Binary Particle Swarm Optimization
Fuzzy Mamdani (BPSOFMAM) and Binary Particle
Swarm Optimization Fuzzy Sugeno (BPSOFSUG).
2)
Each controller is developed with the help of the meta-
heuristic and artificially intelligent techniques: BPSO and
fuzzy logic. Fuzzy logic is further categorised using two
FISs as Mamdani FIS and Sugeno FIS.
3)
Two DSM techniques are considered: load scheduling
and load curtailment for making the controllers efficient
in terms of energy and cost optimization.
4)
Daily used and seasonally used appliances are selected
for checking the effectiveness of the proposed work.
5)
Fanger’s PMV method is applied for computing the
thermal comfort of the appliances. Daily used appliance
comfort calculation is also the part of this system.
6)
Five input parameters: room temperature, outdoor temper-
ature, initialized setpoints, price and occupancy, and three
output parameters: energy consumption, cost, appliance
usage comfort and thermal comfort are considered for
the evaluation of the proposed system.
7)
Two membership functions: triangular and trapezoidal are
considered for this system and 162 rules are developed
for the computation of the thermal comfort and 8 rules
are developed for the computation of the daily used
appliances’ comfort. The membership functions are
developed on the basis of the human intuition method.
8)
Two scenarios are used: Normal scenario: where no
Renewable Energy Sources (RESs) are integrated) and
RESs oriented scenario: where RESs are integrated in
order to enhance the comfort of the residential consumers.
9)
Simulations are conducted to validate the system per-
formance which show that our proposed controllers are
efficient to the unscheduled system.
C. Paper Organization
The rest of the paper is organized as follows: Section II
describes the related work. Section III elaborates the problem
formulation and system model, whereas Section IV discusses
the implementation details. Finally, Section V concludes the
paper and presents possible future challenges.
II. RE LATE D WOR K
In this section, some of the existing techniques relevant
to this work are discussed and critically analysed. Especially,
some energy management techniques using meta-heuristic and
mathematical models are studied. These techniques are either
based on the fuzzy logic or the meta-heuristic optimization.
In [
28
], authors have enhanced the existing model based on
the power consumption patterns and behaviours of the users
or the appliances in the household. They have considered the
following load patterns: patterns for the electric vehicles along
with the other appliances and patterns after the analysis of the
DSM techniques for managing the pricing tariffs against their
load. This model is based on three principles: i) consumers’
tasks are modelled through markove model, ii) logistic and
bayesisan adoptive models and iii) rule-based and optimization
3
models for maintaining the pricing tariffs. This model also
sets up an analytical framework which is the new setup for
managing the newly designed products and activities. It does
not replace the older frameworks because those are limited to
the specific datasets. The rebound peaks mitigation algorithm
is presented to avoid the peak load through automatically
scheduling demand response algorithms [
29
]. The authors have
used the assumption of same cost from all dwellings and utility
over the certain time. The proposed system has been validated
from the numerical outcomes.
The concept of the greener and intelligent supply side is
presented by applying power preservations in district heating
systems. The purpose of this system is to fulfill the heating
systems’ requirements of the buildings [
30
]. Aggregated yearly
load requirements of the buildings are calculated and its
effect on the heating systems’ functionality and greenhouse
emmisions is analysed. In [
31
], a DSM technique is presented
for scheduling the demands of the residential buildings while
maximizing the consumers’ preferences. The input parameters
are power ratings, power consumption and their operation
time. Consumers’ preferences are found on three postulates.
Further, three budget scenarios: $0.25, $0.5 and $1.0 per day
are described to check the effectiveness of the proposed system.
Proposed algorithm gives the effective results in terms of
minimizing the cost and maximizing the comfort.
For controlling the AC power buying and utilization manage-
ment, one new fuzzy logic model is proposed for estimating the
users’ patterns: efficiency in power utilization along with the
load curtailment [
32
]. It considers three parameters: monetary,
personal and environmental comfort. Immense usage of the
power provides multiple chances to power preservation systems.
Another district heating system is presented for managing
the economical and carbon emssions’ effects for both the
consumers and utilities aspects [
33
]. The power prices are
taken into consideration in this scenario. This work has also
described that implementation of this strategy requires the
district heating power plants’ integration which may effect the
total revenue and productivity cost. The authors in [
34
] have
elaborated that the aggregated rate of power consumption is
equivalent to 30% for the indoor air quality, thermal and visual
purpose energy management systems based on the occupants’
living patterns. However, it is not accurately managed due
to inaccurate occupancy awareness. This study provides the
reviews that accuracy in occupants’ living patterns enhances
the energy management systems’ functionality.
In [
35
], an adaptive DSM technique is presented to manage
the load under several grid conditions. In this system, authors
have considered the huge integration of the system modules
which are battery storage devices, Photovoltaic (PV) stations,
power requirements and utility. These modules are integrated to
coordinate the mutual operations of the system under study. In
addition, one prediction based layer is also added in this system
in order to estimate the required power. The current work is
developed using the multi-objective optimization technique
(i.e., Non-dominated Sorting Genetic Algorithm (NSGA)) for
reducing the energy consumption as well as enhancing the
consumer comfort. A new centralized energy management
controller is presented for controlling the day-ahead and real
time load for maintaining the price from the grid in real
environment [
36
]. This work is carried out for the validation
and development of the real-time experiments using the multi-
home artificial bee colony where home based Micro Grids
(MGs) are interconnected in an isolated mode. Operational
cost and working efficiency are reduced in this work.
An optimization algorithm is developed for considering
the economic decisions, power requirement anlysis and their
distribution [
37
]. Authors have applied four meta-heuristic
algorithms for determining the global optimum solution of the
defined problem. This study declares that there is no player in
the game who has the high preference on the remaining players.
It maximizes the savings of the thermal or electrical assets for
the home MGs. The proposed work is validated using the group
of the home MGs. Rebound peaks are studied in [
38
] with
various existing reasons. A new demand response algorithm is
proposed to mitigate the rebound peaks. This work considered
the homogeneous cost function in such a scenario when the
load demands from multiple homes are flat. In another work
[
39
], a fuzzy controller having 25 rules is designed for the
residential grid-connected MG using the RESs and storage
resources. This assumes that both power requirements and
RES power productions are not controllable. Main goal of this
system is the sustainablity of the power from grid and battery
storage resources. Membership functions and fuzzy rules are
defined to control the behaviours of the MGs.
A multi-residential power controlling scheme with multi-
class appliances is presented in [
40
]. Comfort preferences of the
consumers are considered in this study. The comfort preferences
are computed by taking the difference of the summation of
power utilization from the main power stations and their
aggregated bill for the consumed power. Distributed algorithm
is used for power scheduling known as PL-Generalized Benders’
algorithm which also protects the consumers’ personal data. By
applying this algorithm, optimized bounds for the consumers’
comfort have been achieved. The concept of plug-in EVs is
presented for vehicle to grid power allocations in order to
minimize the bill in a day-ahead and real-time environment [
41
].
This scheme is based on decentralized nature and having the fast
convergence ratio. It handles the large number of intermittent
power resources and can avoid the black swarn occurences.
Black swarn occurences are the states of the system where
sudden blackouts may occur and are very difficult to estimate.
The effects of advanced metering infrastucture appliances, smart
metering appliances, direct load control programs and incentive
based programs are analysed in [
42
] which are investigated
for showing their impact on the energy efficiency. This work
further presents the challenges of existing work along with
some future directions.
A grid-connected residential MG layout is proposed for
integrating the powers from PVs, fuel cells and battery storage
resources to distribute the load demands [
43
]. This system is
designed for off-grid and on-grid modes in both real-time and
long term load estimation environment for power production
and utilization. Fuzzy logic is applied for selecting the particular
mode of operation. Authors in [
44
] have proposed another
power management strategy for EVs. The requirements of this
system are: load demands of the consumers, charging time
4
and voltage from the utility along with the pricing tariffs. The
accurate estimation of the occupants in the office environment
is evaluated based on the temperature, humidity, light and
CO2
sensors using multiple statistical models. These models
are: Linear Discriminant Analysis (LDA), Classification and
Regression Trees (CART) and Random Forest (RF) models
which give the better classification and estimation results for the
occupancy detection [
45
]. Power conservation measurements
are obtained through model predictive control using weather
estimations in [
46
]. Two months data is taken for the test
purposes and experiments are performed in real building.
In short, all of the aforementioned techniques are used for
the load curtailment or load scheduling using meta-heuristic
and other determininstic techniques; however, we are focusing
on the daily used and seasonally used appliances’ energy
consumption patterns and their comfort standards at the same
time in this work. In addition, consumers’ thermal and appliance
usage comfort are not evaluated through FISs in previous
studies. So, there is a need to compute the thermal and daily
used appliances’ comfort using the proposed FISs: Mamdani
and Sugeno FISs through Fanger’s PMV method.
III. SYS TE M MOD EL
This section is comprised of specified assumptions for the
system and some figures regarding systems’ components.
A. Assumptions:
Following assumptions are considered for this system.
1)
Concept of self-configuration, extensibility and context-
awareness for the appliances used in the buildings.
2)
Thermostatically controlled appliances are the essential
requirement of the system setup.
3)
Time slots are speculated as discrete and equally dis-
tributed (i.e., one hour for each slot).
4)
The utility is providing a limited power supply capacity.
There may be the cases where blackouts occur during the
consumption period. So, RESs are considered to avoid
blackouts.
5)
Installation and maintenance costs of the RESs are not
considered in the proposed system’s setup.
6)
Energy management controllers are considered to monitor
the home load demand and the grid power supply limits
during each time slot.
B. Problem Formulation
Formulation for thermal and daily used appliances comfort
calculation steps are described in the subsequent subsections
based on the Fanger’s PMV method as displayed in Fig. 1.
1) Thermal Comfort using PMV: PMV method represents
the estimated scale between -3 to +3 according to the In-
ternational Standard Organization (ISO) Standard 7730 (ISO
1984) [
25
]. It is calculated using four environmental factors:
air temperature, air velocity, mean radiant temperature and
air humidity, and two personal factors: clothing insulation
and metabolic rate as demonstrated in Fig. 2. Sensor nodes
are installed in the building area in order to fetch the air
Fig. 1: User comfort types and ranges defined by the Fanger’s
indexes.
temperature information from the surroundings. Metabolic
rate is computed from the tasks performed by the consumers
and whole information is communicated to the EMS via
wireless communication links. Fanger’s idea about consumers’
sensantions is the difference between the produced heat and
heat loss from the environment. So, in this case, the PMV
is computed on the basis of general acitivities of the persons
living in any building and it is represented by the Eq. (1):
PMV
Computation
PMV
Scale
3 Hot
2 Warm
1 Slightly Warm
0 Neutral
-3 Slightly Cool
-2 Cool
-1 Cold
Air Temperature
Radiant Temperature
Air Velocity
Relative Humidity
Activity Level
Clothing Insulation
PMV
Fig. 2: Fanger’s PMV model using four environmental factors
and two personal factors.
T h P MVcons = (0.303 ×exp0.036 ×(M et(Acth))
+0.028) ×Loadth(Acth).(1)
In above equation,
T hP MVcons
represents the thermal com-
fort using PMV of the consumers,
Met(Acth)
indicates the
metabolic rate based on the acitivity level, and
Loadth(Acth)
is the thermal load. Thermal load can be described as the
difference between internal heat produced to the real environ-
ment heat loss and the heat loss due to the sweating level
of the consumer. Air humidity and temperature values are
obtained through the sensor nodes, whereas metabolic rate is
computed via the activity level of the consumers. Mean radiant
temperature and clothing values are considered as constant
because of the limited resources.
As the thermal comfort is relevant to the activities of the
consumers, so these activities are proportional to the metabolic
rate for approximately defining the PMV index. The single
metabolic equivalent of the task is described as 58.2
W/m2
(
18.4Btu/h.f t2
). This quantity becomes equivalent to the
power production rate of the single consumer in the rest
position. When a consumer acts more active, he demands
5
TABLE I: Rules for appliance usage comfort.
Priority
Control
Variable
Occupancy
Energy Con-
sumption
Comfort
P rhDecnhOccpEChComf orth
P rhDecnhOccaECmComf ortm
P rhDecnlOccpECmComf ortm
P rhDecnlOccaEClComf ortl
P rlDecnhOccpECmComf ortm
P rlDecnhOccaEClComf ortl
P rlDecnlOccpEClComf ortl
P rlDecnlOccaEClComf ortl
more metabolic rate. Using the value of new metabolic rate,
the previous equation can be reformulated as;
T h P MV Acthk|ActhiActhk=αk×(0.303×
exp0.036(1
||Acthk||
||Acthk||
X
i=1
Met(Acthi)) + 0.028)
×Loadth(1
||Acthk||
||Acthk||
X
i=1
Met(Acthi)).
(2)
Here,
kth
activity is depicted as the
Acthk
,
Acthi
is any
single activity in the environment. The set of
||Acthk||
tasks or
activities are proportional to the metabolic rate
Met(Acthi)
.
αk
is indicating the constant for normalizing the thermal comfort
values. In our case, we have considered the multiple tasks and
our final thermal comfort will be computed as the average of
all tasks. The set of rules for evaluating the thermal comfort
are described in Table II.
2) Appliance Usage Comfort: We have classified the appli-
ances for maintaining the relationship among activities. Three
classes are considered in this work: shiftable, non-shiftable and
base load appliances. Further, three statuses of the appliances in
first two classes are also elaborated. These statuses are: explicit
on, implicit on and explicit, implicit and standby off statuses.
Explicit on status is allocated to the shiftable appliances and
implicit on status is assigned to the base load appliances.
There are three statuses: explicit on, implicit on and standby
off statuses which are allocated to the appliances throughout
the system testing. These statuses are used for maintaining the
comfort standards and energy efficiency of the system.
At first stage, appliance usage comfort is determind by the
priority value based on certain confidence of the consumers.
Multiple techniques are applicable to find the priority values;
however, this work has computed the priority based on the fuzzy
logic. The priority and control variable values are taken from the
[
25
]. Appliance usage comfort has three inputs: control variable,
priority and occupancy and two outputs: energy consumption
and comfort. The priorities are defined according to the nature
of the activity levels. These priorities are named as: high
and low for each appliances. The rules for input and output
variables’ membership functions are represented in Table I.
Fuzzy logic human intuition based model is used for
modelling the rules of this system. All activities corresponding
to the consumers are determined through these rules. Final
outcome is the priority vector as described in the Eq. (3) [
25
]:
P rN(Acthk) = P r1(Acth1), P r2(Acth2), ..., P rN(Acthk).(3)
Here,
P rN
variable denotes the
N
number of priorities
(i.e., 1, ..., N) for each appliance against each activity level
ranging from
1, ..., k
. When EMS initiates the process, it first
checks whether there is high demand or low demand intervals
according to the price signals defined by the utility, if it finds
the high price rate hours then it starts its power saving services
in the normal scenario where no RESs are integrated. In RES
oriented scenario, it shifts the appliances towards RESs. Further,
it follows the delay decision control vector
DecnD
which is
described in the Eq. (4) [
25
]. Each time delay decision control
is considered as 0 or 1 value. 0 means ”off” and 1 means ”on”.
DecnD(Acthk) = Decn1(Acth1), Decn2(Acth2), ...,
DecnN(Acthk).(4)
This data is taken from [
25
] and we will incorporate the
consumer presence in it later. Now, the appliance usage comfort
is evaluated using the above two equations.
App UComf Acthk|ActhiActhk=roundOf f (S caleF actor)
×sigmoid(
N
X
n=1
P rN(Acthk)×DecnD2).
(5)
The consumer presence vector is defined as below [25];
Cons P C omfC T (Acthk) = Cons P Comf 1(Acth1),
Cons P C omf2(Acth2), ..., C ons P Comf CT (Acthk).(6)
Where,
CT
indicates the total number of consumers’ pres-
ence in any time interval. In this work, the appliance usage
comfort is improved by concatenating the consumer presence
Cons P C omf , so it is reformulated below.
App UComf Acthk|ActhiActhk=roundOf f (S caleF actor)
×sigmoid(
N
X
n=1
P rN(Acthk)×DecnD×C ons P C omf2).
(7)
C. Proposed System
In this system, following components are integrated for the
development of the energy management controllers: HVAC sys-
tems, daily used appliances, wireless communication medium,
smart meters, sensor nodes and utility. First, the HVAC systems
and other daily used appliances are considered for the system
setup. Communication in this system is performed between
smart meter to energy management controller and smart
meter to utility and vice versa. Smart meter is used for the
bidirectional communication between consumers and utility for
sharing the pricing tariffs and users’ electricity consumption
patterns. Sensor nodes are used for sensing the information and
exchanging the signals among heterogeneous home appliances
with smart meters and schedulers. The consumers send their
requests to the energy management controllers which allocate
6
TABLE II: List of rules.
S. No.Room temperature Outdoor Temperature Initialized setpoints Price Occupancy Energy Consumption
1RTlOTlISPlPlOccaECv l
2RTlOTlISPlPlOccpECl
3RTlOTlISPlPmOccaECl
4RTlOTlISPlPmOccpECm
5RTlOTlISPlPhOccaECl
6RTlOTlISPlPhOccpECm
7RTlOTlISPmPlOccaECl
8RTlOTlISPmPlOccpECm
9RTlOTlISPmPmOccaECl
10 RTlOTlISPmPmOccpECm
11 RTlOTlISPmPhOccaECl
12 RTlOTlISPmPhOccpECm
13 RTlOTlISPhPlOccaECl
14 RTlOTlISPhPlOccpECh
15 RTlOTlISPhPmOccaECm
16 RTlOTlISPhPmOccpECh
17 RTlOTlISPhPhOccaECm
18 RTlOTlISPhPhOccpECh
19 RTlOTmISPlPlOccaECl
20 RTlOTmISPlPlOccpECm
21 RTlOTmISPlPmOccaECl
22 RTlOTmISPlPmOccpECm
23 RTlOTmISPlPhOccaECl
24 RTlOTmISPlPhOccpECm
25 RTlOTmISPmPlOccaECl
26 RTlOTmISPmPlOccpECm
27 RTlOTmISPmPmOccaECl
28 RTlOTmISPmPmOccpECm
29 RTlOTmISPmPhOccaECl
30 RTlOTmISPmPhOccpECm
31 RTlOTmISPhPlOccaECm
32 RTlOTmISPhPlOccpECh
33 RTlOTmISPhPmOccaECm
34 RTlOTmISPhPmOccpECh
35 RTlOTmISPhPhOccaECm
36 RTlOTmISPhPhOccpECh
37 RTlOThISPlPlOccaECm
38 RTlOThISPlPlOccpECh
39 RTlOThISPlPmOccaECm
40 RTlOThISPlPmOccpECm
41 RTlOThISPlPhOccaECm
42 RTlOThISPlPhOccpECh
43 RTlOThISPmPlOccaECm
44 RTlOThISPmPlOccpECh
45 RTlOThISPmPmOccaECm
46 RTlOThISPmPmOccpECh
47 RTlOThISPmPhOccaECm
48 RTlOThISPmPhOccpECh
49 RTlOThISPhPlOccaECm
50 RTlOThISPhPlOccpECh
51 RTlOThISPhPmOccaECm
52 RTlOThISPhPmOccpECh
53 RTlOThISPhPhOccaECm
7
Table II Continued: List of rules.
S. No. Room temperature Outdoor Temperature Initialized setpoints Price Occupancy Energy Consumption
54 RTlOThISPhPhOccpECh
55 RTmOTlISPlPlOccaECl
56 RTmOTlISPlPlOccpECm
57 RTmOTlISPlPmOccaECl
58 RTmOTlISPlPmOccpECm
59 RTmOTlISPlPhOccaECl
60 RTmOTlISPlPhOccpECm
61 RTmOTlISPmPlOccaECl
62 RTmOTlISPmPlOccpECm
63 RTmOTlISPmPmOccaECl
64 RTmOTlISPmPmOccpECm
65 RTmOTlISPmPhOccaECl
66 RTmOTlISPmPhOccpECm
67 RTmOTlISPhPlOccaECm
68 RTmOTlISPhPlOccpECh
69 RTmOTlISPhPmOccaECm
70 RTmOTlISPhPmOccpECh
71 RTmOTlISPhPhOccaECm
72 RTmOTlISPhPhOccpECh
73 RTmOTmISPlPlOccaECl
74 RTmOTmISPlPlOccpECm
75 RTmOTmISPlPmOccaECl
76 RTmOTmISPlPmOccpECm
77 RTmOTmISPlPhOccaECl
78 RTmOTmISPlPhOccpECm
79 RTmOTmISPmPlOccaECl
80 RTmOTmISPmPlOccpECm
81 RTmOTmISPmPmOccaECl
82 RTmOTmISPmPmOccpECm
83 RTmOTmISPmPhOccaECl
84 RTmOTmISPmPhOccpECm
85 RTmOTmISPhPlOccaECm
86 RTmOTmISPhPlOccpECh
87 RTmOTmISPhPmOccaECm
88 RTmOTmISPhPmOccpECh
89 RTmOTmISPhPhOccaECm
90 RTmOTmISPhPhOccpECh
91 RTmOThISPlPlOccaECm
92 RTmOThISPlPlOccpECh
93 RTmOThISPlPmOccaECm
94 RTmOThISPlPmOccpECh
95 RTmOThISPlPhOccaECm
96 RTmOThISPlPhOccpECh
97 RTmOThISPmPlOccaECm
98 RTmOThISPmPlOccpECh
99 RTmOThISPmPmOccaECm
100 RTmOThISPmPmOccpECh
101 RTmOThISPmPhOccaECm
102 RTmOThISPmPhOccpECh
103 RTmOThISPhPlOccaECm
104 RTmOThISPhPlOccpECh
105 RTmOThISPhPmOccaECm
106 RTmOThISPhPmOccpECh
8
Table II Continued: List of rules.
S. No. Room temperature Outdoor Temperature Initialized setpoints Price Occupancy Energy Consumption
107 RTmOThISPhPhOccaECm
108 RTmOThISPhPhOccpECh
109 RThOTlISPlPlOccaECl
110 RThOTlISPlPlOccpECm
111 RThOTlISPlPmOccaECl
112 RThOTlISPlPmOccpECm
113 RThOTlISPlPhOccaECl
114 RThOTlISPlPhOccpECm
115 RThOTlISPmPlOccaECl
116 RThOTlISPmPlOccpECm
117 RThOTlISPmPmOccaECl
118 RThOTlISPmPmOccpECm
119 RThOTlISPmPhOccaECl
120 RThOTlISPmPhOccpECm
121 RThOTlISPhPlOccaECl
122 RThOTlISPhPlOccpECm
123 RThOTlISPhPmOccaECl
124 RThOTlISPhPmOccpECm
125 RThOTlISPhPhOccaECl
126 RThOTlISPhPhOccpECm
127 RThOTmISPlPlOccaECm
128 RThOTmISPlPlOccpECh
129 RThOTmISPlPmOccaECm
130 RThOTmISPlPmOccpECh
131 RThOTmISPlPhOccaECm
132 RThOTmISPlPhOccpECh
133 RThOTmISPmPlOccaECm
134 RThOTmISPmPlOccpECh
135 RThOTmISPmPmOccaECm
136 RThOTmISPmPmOccpECh
137 RThOTmISPmPhOccaECm
138 RThOTmISPmPhOccpECh
139 RThOTmISPhPlOccaECm
140 RThOTmISPhPlOccpECh
141 RThOTmISPhPmOccaECm
142 RThOTmISPhPmOccpECh
143 RThOTmISPhPhOccaECm
144 RThOTmISPhPhOccpECh
145 RThOThISPlPlOccaECh
146 RThOThISPlPlOccpECv h
147 RThOThISPlPmOccaECh
148 RThOThISPlPmOccpECv h
149 RThOThISPlPhOccaECh
150 RThOThISPlPhOccpECv h
151 RThOThISPmPlOccaECh
152 RThOThISPmPlOccpECv h
153 RThOThISPmPmOccaECh
154 RThOThISPmPmOccpECv h
155 RThOThISPmPhOccaECh
156 RThOThISPmPhOccpECv h
157 RThOThISPhPlOccaECh
158 RThOThISPhPlOccpECv h
159 RThOThISPhPmOccaECh
9
Table II Continued: List of rules.
S. No. Room temperature Outdoor Temperature Initialized setpoints Price Occupancy Energy Consumption
160 RThOThISPhPmOccpECv h
161 RThOThISPhPhOccaECh
162 RThOThISPhPhOccpECv h
the aplliances on the desired time slot. In case, if peak
demand hours are arrived then appliances are scheduled to the
off-peak hours.
The electricity requirements are based on the activity level
and presence of the consumers in the buildings. When the
consumers occupy the home, they require more electricity.
When they send requests to the controller, it first checks
the peak demand hours and then schedules the requests via
the RESs. When peak demand intervals are ended, controller
request for the required load from the uility.
These controllers are developed for maintaining the thermal
and appliance-usage comfort. Comfort is defined as the satis-
faction of the mind with the heat exchange to the environment.
Here, thermal and appliance usage comfort is calculated via
Fanger’s PMV [25] method.
In these controllers, energy is managed by scheduling the load
using BPSO at first level and then air-conditioning systems’
load is curtailed using fuzzy logic at second level. The load
is curtailed during the peak hours in order to reduce the
consumers’ aggregated electricity cost in case of the HVAC
systems otherwise load is is scheduled for remaining appliances.
The working of these controllers is shown in Fig. 3 and energy
management in this system is specific to the hot regions of the
world, whereas the cold regions are not included in its study.
Both controllers require different sets of the inputs for each
technique. BPSO uses the inputs: set of appliances, their length
of operation time (LOT), power ratings and their load categories
(i.e., shiftable or non-shiftable). The fuzzy logic requires the
input variables, which are: indoor temperature (
RT
), outdoor
temperature (
OT
), initialized setpoints (
I SP s
), user occupancy
(
Occ
) and utility price (
Prating
), whereas, the output variables
are energy consumption (
EC
), cost and thermal comfort for
the HVAC systems. In case of daily used appliances, there are
three inputs and two outputs for this system. Inputs are:
P rh
,
Decnh
and
Occp
, whereas outputs are
ECh
and daily used
Comf orth
. These variables are further categorized into the
membership functions. Each input has a certain degree of the
membership which is defined by the linguistic variables. The
categorization for the input variables is:
RT
: low (
l
), medium
(
m
) and high (
h
),
OT
:
n
,
ht
, and
vht
,
I SP s
:
l
,
m
, and
h
,
Occ
:
a
and
p
, and
Prating
:
op
,
mp
, and
hp
. Output variables are
described as:
EC
: very low (
vl
),
l
,
m
,
h
and very high (
vh
).
Priorities, decision control variables and comfort (i.e., appliance
usage and thermal) are also categorised as:
l
,
m
, and
h
. Cost
is computed based on the usage of the aggregated energy in
the system. The input and output membership functions are
shown in Figure 5.
D. Working of the Proposed HVAC System
The working of the proposed HVAC system is based on its
inputs which are provided by the consumers in our scenario
and three outputs as shown in Fig. 4. These outputs are heating,
cooling and enegy consumption. Our focus is on computing
the energy consumption of the system in this work.
Fig. 4: Inputs and outputs of HVAC systems.
The flow of the system is described in the Algorithm 1 and
2. In these algorithms,
MitL
,
MitM
and
MitH
mean the
setpoint mitigation upto
1
,
2
and
3
, whereas
γ
is taken as
constant and its value is 2. There are two scenarios which are
considered in this work that is why we have chosen this value
equal to 2. The steps of both algorithms are explained below.
1)
Firstly, initialization of the input parameters:
RT
,
P r
,
Decn
,
OT
,
I SP s
,
Occ
and
Prating
has been performed.
2)
Secondly, the process of fuzzification has been done for
the above-mentioned input and output parameters.
3)
Thirdly, rules‘ definition is specified using the human
intuition method through the information fetched by the
sensors nodes. Sensor nodes are already installed in the
surroundings.
4)
FISs are applied for the efficient rules evaluation in order
to compute the desired outputs:
EC
, cost and
Comf ort
.
5)
Defuzzification process has been performed after the
rules evaluation by the Mamdani and Sugeno FISs.
6)
Defuzzification process provides the concrete values after
the fuzzy values (defined to show the certain truth levels
for any scenario).
7)
Comfort levels are obtained after defuzzification process.
8)
Fourth and fifth steps have been working till the end of
the simulation for obtaining the optimal solution.
9)
Second algorithm is defined for load mitigation purpose.
The way of mitigating the setpoints relevant to the
OP
,
MP and HP hours is described.
10) OP ,M P and HP hours are defined by the utility and
the setpoints are curtailed for maintaing the consumers’
comfort instead of switching off the HVAC system which
creates consumers’ discomfort.
11)
During the
OP
hours, setpoints are mitigated to
1
. In
addition, setpoints are mitigated to
2
during
MP
and
3
are reduced for the
HP
hours as defined by the Fanger’s
PMV model.
10
Region Aggregator
Building-1
Utility Company
Building-N
Building-2
Request \ Response
Request \ Response
Request \ Response
Apartment-N
Apartment -N
Apartment -1
Apartment -1
Apartment -N
...
......
SM
EMCs
Pricing Tariffs
Daily Used
Applainces
Seasonally Used
Appliances
Request\Response
Perform Scheduling
Fig. 3: System model.
Algorithm 1:
Thermal and Appliance usage comfort
using BPSOFMAM and BPSOFSUG.
1Initialization: B (RT ,Decn,P r,T empoutdoor ,
I SP s,Prate and Occ)
2Parameters Fuzzification Process
3RT ← {L, M, H}
4Decn ← {L, M, H}
5P r ← {L, M, H}
6T empoutdoor ← {L, M, H}
7I SP s ←{L, M, H}
8Prate ← {OP, MP, HP}
9HO ← {A, P}
10
Define Rules using the membership functions defined in
Table I and Table II
11
Evaluate the rule base by Mamdani FIS and Sugeno FIS
12 for ts = 1 to T S do
13 Check RT and I SP s
14 if Low demand then
15 if I SP s(ts)==HP(ts)then
16 Check daily usage appliances and check
Decn and P r
17 Schedule them to appropriate ts
18 Check HVAC systems
19 B[I SP s]I SP s M itH
20 else if I SPs(ts)==MP(ts)then
21 B[I SP s]=I SP s M itM
22 else
23 B[I SP s ]=I S P s M itL
24 end
25 end
26 end
27 end
28 else
29 Setpoint HP(tLDM T ,MitL,M itM,M itH)
30 Swap daily used appliane to OPHs
31 Compute thermal comfort using Eq. (2) and
appliance usage comfort by Eq. (7)
32 end
33 Perform defuzzification prosess using the
aggregated fuzzy rules
34 Compute EC using rule base
35 Compute consumers’ cost and Comf ort
36 end
Algorithm 2:
Setpoints Adjustment for Energy Con-
servation (Mit EC Setpoints(B))
1Setpoint HP(tLDM T ,MitL,M itM,M itH)
2for ts=1 to Tdo
3if I SP s(ts)==HP(ts)then
4tLDM T =γMitH
5else if I SPs(ts)==MP(ts)then
6tLDM T =2γMitM
7else
8tLDM T =3γMitL
9A[I SP s ]tLDMT
10 end
11 end
12 end
13 Mit EC Setpoints(B)=A[I S P s ]
14 end
TABLE III: Execution Phases of the EMCs.
Inputs
Energy management mod-
ule
Control actions
Load demand BPSO for load scheduling Towards grid
Indoor tempera-
ture
Fuzzy inference systems
(Mamdani and sugeno) for
load curtailment
Towards RES
Outdoor
temperature
Sensor nodes Towards users
Initialized set-
points
Fuzzy inference systems Towards users
Pricing tariffs Fuzzy inference systems Towards utility
Occupancy Sensor nodes Towards users
E. Working of the Energy Management Controllers
Both controllers use the certain input and output variables
which are explained earlier. BPSO schedules the high power
rate appliances. On the other hand, fuzzy logic curtails the
load while working in the non-RES based scenario. It also uses
sensor nodes for sensing the outdoor environmental conditions
(i.e.,
OT
),
RT
, and
Occ
. When
OT
is high, the proposed
controllers increase the
I SP s
for maintaining the building’s
temperature at comfortable level. Similarly, they increase or
decrease the
I SP s
by sensing the presence of the person in the
building. When building is occupied or empty, they maintain
the
RT
by setting the feasible
I SP s
. The execution of the
11
25 30 35 40 45 50
OT (Degree-Celcius)
0
0.2
0.4
0.6
0.8
1
Degree of Membership
Normal Hot Very Hot
(a) Membership functions for the OT .
10 15 20 25
RT (Degree-Celcius)
0
0.2
0.4
0.6
0.8
1
Degree of Membership
Low Medium High
(b) Membership functions for the RT .
20 21 22 23 24 25
ISPs (Degree-Celcius)
0
0.2
0.4
0.6
0.8
1
Degree of Membership
Low Medium High
(c) Membership functions for the IS P s.
0 50 100 150 200 250
Occ
0
0.2
0.4
0.6
0.8
1
Degree of Membership
Present Absent
(d) Membership functions for the O.
4 6 8 10 12 14 16
P (Cents)
0
0.2
0.4
0.6
0.8
1
Degree of Membership
Off Peak Mid Peak High Peak
(e) Membership functions for the P.
02468
EC (kWh)
0
0.2
0.4
0.6
0.8
1
Degree of Membership
Very Low Low Medium High Very High
(f) Membership functions for the energy consumption.
Fig. 5: Membership functions for thermal and appliance usage comfort.
existing controllers has three phases which are described in the
Table III. Furthermore, RES are incorporated to avoid the peak
load curtailment and enhancing the consumers’ comfort levels.
Two types of the RESs can be incorporated in this research
work for showing that how they effect the consumers’ comfort
standards. These RESs are: PVs and WTs. We have adopted
PVs at this stage. With the help of RESs, consumers’
EC
patterns are better organized and their preferences are fully
managed.
IV. RES ULTS AND DISCUSSIONS
In this section, simulations of the system are carried out
in order to prove the efficiency of the proposed controllers.
These simulations are conducted in Matlab because it provides
more user friendly environment by utilizing its Graphical User
Interface (GUI) services. The simulation setup is based on two
types of appliances: thermostatically controlled appliances and
daily used appliances. These appliances and power ratings are
presented in the Table IV and their data is taken from [
47
].
The occupants’ data is also taken from [
47
] and is mentioned
in Table V. Four performance parameters are considered:
energy consumption, cost, appliance usage comfort and thermal
comfort. Input parameters are set of appliances, power ratings
and length of operation times of the appliances. There are two
cases which are incorporated in this simulation setup: one is
without RES and the other case is integration of RESs.
12
The maximum hourly energy consumption of the proposed
controllers is 18.5 kWh, 16 kWh and 13 kWh, respectively
through the unscheduled and scheduled cases: BPSOFMAM
and BPSOFSUG as shown in Fig. 6. Aggregated energy
consumption obtained in this scenario from the proposed
controllers is 86% and 70%. In this case, BPSOFSUG consumes
the less energy because Sugeno FIS uses efficient fuzzy
membership functions. Peak formation is avoided in the high
peak hours in order to avoid the blackouts and consumers
high electricity costs. As energy consumption in the scheduled
case is lower than the unscheduled case, it gives the energy
savings; however, our proposed controller BPSOFSUG uses
renewable energy sources during the high demand intervals, so
it does not compromise comfort more. In grid-connected case,
comfort is compromised which is tackled using RESs. Base
case appliances are not considered for scheduling in our case.
We are considering the comfort of shiftable appliances which
is obtained at reasonable improved rate as compared to without
using renewable energy resources case. If all appliances are
scheduled, then the total consumed load from the main grid
will be utilized less and more load from the renewable energy
sources is utilized.
TABLE IV: Simulation Setup.
S.
No.
Appliances Power
Rating
(kWh)
LOT Pre-emptive
1 Space Heater 1.00 9 1
2 Heat Pump 0.11 4 1
3 Portable Heater 1.00 5 0
4 Fan 0.50 11 0
5 Furnace Fan 0.38 8 1
6 Water Heater 4.50 8 1
7 Central AC 2.80 12 1
8 Room AC 3.51 10 1
9 Cloth Washer 0.51 9 1
10 Cloth Dryer 5.00 5 1
11 Dishwasher 1.20 11 1
12 First Refrigerator 0.50 24 0
13 Evap Cooling 0.40 1 1
14 Second Refrigerator 0.50 24 0
15 Freezer 0.29 24 0
16 Indoor Lighting 0.36 5 0
17 Range Burner 0.40 6 0
18 Television 0.20 3 0
19 Microwave 0.25 2 0
20 Personal Computer 0.35 4 0
21 Well Pump 0.90 2 1
After computing the energy consumption of the household
appliances, the total cost of the energy is computed. The
maximum hourly cost of the energy obtained from the unsched-
uled, BPSOFMAM and BPSOFSUG is: $2.1, $1.7 and $1.6,
respectively as shown in the Fig. 7. In this case, BPSOFMAM
and BPSOFSUG cost 80% and 76%. In addition, BPSOFMAM
and BPSOFSUG curtial the load to certain extent as per the
desired comfort levels of the consumers. So, these controllers
cannot reduce more cost because they deal with high power
ratings based appliances. However, the cost in all scheduled
cases is less than the unscheduled case. As the proposed
controllers perform load curtailment using the BPSOFMAM
and BPSOFSUG, so, they reduce more cost as compared to
the unscheduled case. The monthly cost obtained using these
controllers in unscheduled and scheduled BPSOFMAM and
BPSOFSUG cases is: $670, $399 and $350 as demonstrated in
Fig. 8. These controllers are proved effective in cost reduction
by scheduling and curtailing load during the high demand
intervals. The whole hottest region is tested by the similar set
of simulations and they have obtained the similar results which
minmize the total cost.
TABLE V: Occupants’ Data.
Total apartments in a building 10
Residents in apartment 3 residents
Type of apartment Single family
Urban district New estate, Rich
Energy consumption per hour Medium
Fig. 6: Hourly energy consumption using all approaches.
Fig. 7: Hourly cost obtained by both controllers and previous
approach.
Fig. 8: Monthly cost obtained by both controllers and
previous approach.
13
A. Comfort in Scheduled Case Without RES
Appliance usage comfort calculated in scheduled case
without using RES is shown in the Fig. 9 with the help of the
Fanger’s PMV model. The comfort ranges are defined in the
system model which are between 0 to 3. In Fig. 9, the maximum
comfort obtained through these controllers is 3. During the
high peak hours, comfort is little bit compromised; however, it
is not creating discomfort for the consumers. Comfort level is
maintained upto some level. By considering the RES, comfort
level can be enhanced to desired level which is discussed in
next section. Comfort of the unscheduled case is assumed to
be 100%, because in unscheduled case, consumers can use
unlimited electricity from the grid. There is no restriction of
electricity usage for them.
0
0.5
1
1.5
2
2.5
3
Appliance Usage Comfort
0 5 10 15 20 25
Time (hours)
Appliance Usage Comfort
Fig. 9: Appliances comfort obtained by the proposed
controllers.
Appliance thermal comfort is calculated using the Eq. (2)
as displayed in the Fig. 10. This comfort is obtained in the
satisfactory range as described by the Fanger’s comfort PMV
model. It is acheived in the range of the -3 to +3. Appliance
thermal comfort can also be enhanced by installing the local
MGs or RESs to avoid the load curtailment as explained earlier.
0
0.5
1
1.5
2
2.5
3
Thermal Comfort
0 5 10 15 20 25
Time (hours)
Thermal Comfort
Fig. 10: Thermal comfort obtained by the proposed
controllers.
B. Comfort in Scheduled Cases with RES
In this section, both types of the comfort computations
have been done with the help of the normal scheduled and
RES integration scenarios. First comfort is evaluated with the
help of scheduled BPSO controller, then it is evaluated with
the integration of the RESs, i.e., PV system [
48
]. Appliance
usage comfort in RES oriented scenario is achieved better
as compared to the appliance thermal comfort. Appliance
thermal comfort using BPSO in RES and non-RES scenario is
equivalent because BPSO gets stuck during the intensification
process as shown in Fig. 11. Appliance usage comfort is
obtained maximum upto 3 as specified by the Fanger’s PMV
scale, whereas a feasible solution of 1.25 for the HVAC is also
obtained using the proposed system.
0 5 10 15 20 25
Time (hours)
0
0.5
1
1.5
2
2.5
3
Thermal and Appliance Usage Comfort
Thermal Comfort using BPSO
Thermal Comfort using BPSO and RES
Appliance Usage Comfort using BPSO
Appliance Usage Comfort using BPSO and RES
Fig. 11: Thermal and appliance usage comfort obtained using
BPSO and RES.
Fig. 12 shows the BPSOFMAM controller for determining
the comfort in scheduled case with RES and without RES
system. The controller without RES is considered less comfort
oriented, whereas the controller which is integrated with the
RES is more comfort centric. As Mamdani FIS uses the simple
evaluation functions so it can not achieve more comfort without
using RESs. Using RESs, it achieves 3 (higher level comfort)
for the appliance usage comfort as per the mentioned scale by
the PMV index, whereas it has aciheved 2.2 for the HVAC
comfort which is also more effective. As BPSO was not more
efficient because of the local intensification; however, when it
is combined with fuzzy logic, it has achieved the more degree
of truth relative to comfort scale as shown in Fig. 12.
0 5 10 15 20 25
Time (hours)
0.5
1
1.5
2
2.5
3
Thermal Comfort
Thermal Comfort using BPSOFMAM
Thermal Comfort using BPSOFMAM and RES
Appliance Usage Comfort using BPSOFMAM
Appliance Usage Comfort using BPSOFMAM and RES
Fig. 12: Thermal and appliance usage comfort obtained using
BPSOFMAM and RES.
Degree of comfort in BPSOFSUG is more accurately
computed as compared to the previous controller because it is
using efficient defuzzification method which is more intelligent
14
in computation. It uses the weighted average method for the
effective computation of the membership functions based on
the specified rules. It has the same values for appliance usage
comfort as maximum till 3; however, it has achieved 2.3
comfort for HVAC systems which is more efficient then the
previous controllers as shown in Fig. 13. Energy consumption
of BPSOFSUG is less from the main grid; however, it is
using energy from RESs. For maintaining the high energy
consumption from the main grid, it curtails the load during peak
hours and uses the alternative energy resources. As it is shown
from figures 10 and 12, comfort of BPSO and BPSOFSUG is
approximately equal by utilizing the RESs.
0 5 10 15 20 25
Time (hours)
0
0.5
1
1.5
2
2.5
3
Thermal and Appliance Usage Comfort
Thermal Comfort using BPSOFSUG
Thermal Comfort using BPSOFSUG and RES
Appliance Usage Comfort using BPSOFSUG
Appliance Usage Comfort using BPSOFSUG and RES
Fig. 13: Thermal and appliance usage comfort obtained using
BPSOFSUG and RES.
C. Pros and Cons of the proposed controllers
After conducting the simulations of the proposed controllers,
there are following pros and cons of the system which are
deduced from the current scenarios.
1)
In normal scenario, comfort for the HVAC systems or
other appliances is normally found compromised during
the peak hours. Whereas, comfort for both loads is
enhanced using the RESs.
2)
Rules for the FISs are formulated on the human inductive
method. This method is not generic, it is scenario-
oriented. So, these rules are only applicable to the similar
works and these are not generic to all applications, i.e., for
analysing the energy consumption and living standards
for cold regions across the world.
3)
RESs have improved the users’ preferences and their
living standards by organizing their loads on the preferred
time intervals, i.e., 1-24 hours.
V. CONCLUSIONS AND FUTURE RECOMMENDATIONS
In this work, two energy management controllers are
developed and implemented using fuzzy logic and BPSO.
The performance of these controllers is compared with the
unscheduled case of the consumers’ energy consumption
patterns. These controllers are validated by conducting the
simulations. BPSOFSUG outperforms the BPSOFMAM in
terms of energy efficiency upto 16%. Fuzzy rules are developed
on the basis of human intuition method which gave the
optimum results. RESs are integrated in order to improve the
comfort standards of the residential consumers. The comfort is
computed through the PMV method in satisfactory range. In
future, we will also prove the control accuracy of the proposed
controllers. After obtaining the desired outcomes, there are
also following future challenges?
1)
What would be the effective value of the scale factor
except 3, because it shows the maximum satisfactory level
for comfort and if we increase this value, the comfort is
exceeding its normal range, i.e. (above [0, 1]). How this
value can ne normalized?
2)
When the comfort standards are varied to each consumer
except considering the delay, how their activity levels
effects them in any other case?
3)
What would be the effect of applying other artificial in-
telligence and meta-heuristic techniques on the proposed
framework?
4)
Installation and maintenance cost of the system should
be considered in order to compute the total expenditure
cost.
5)
The concept of the zero-energy building should be
investigated while minimizing the greenhouse gases from
the environment.
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...  High complexity. The problems in [22,28,29,32,46,49,51,57,61,62,66,68,74,[78][79][80][81] can be characterized as highly complex, since they have a complex energy system architecture (high number of users); they consider many energy resources, including interruptible and power-adjustable household appliances; they are heavily constrained; and in most cases, the optimization functions involve multiple objectives.  Moderate complexity. ...
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... • Combined models always perform better than a single model, as they showed significant results in reducing energy consumption, maintaining comfort conditions, user preferences, etc. [119,122,151]. ...
... This reflects the interest and importance given to the development and application of numerical optimization methods by the building community around the world. At this point, the focus of this work is not to make a literature review of all these methods, but rather to present the most advanced and adopted optimization techniques in AI-assisted building control, in particular the Genetic Algorithm (GA) [46,49,52,64,65,[115][116][117][118]120,123,125,126,131,148,153,155,161,162,167] and Particle Swarm Optimization (PSO) [43,51,61,61,107,127,151,160]. ...
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... • Combined models always perform better than a single model, as they showed significant results in reducing energy consumption, maintaining comfort conditions, user preferences, etc. [119,122,151]. ...
... This reflects the interest and importance given to the development and application of numerical optimization methods by the building community around the world. At this point, the focus of this work is not to make a literature review of all these methods, but rather to present the most advanced and adopted optimization techniques in AI-assisted building control, in particular the Genetic Algorithm (GA) [46,49,52,64,65,[115][116][117][118]120,123,125,126,131,148,153,155,161,162,167] and Particle Swarm Optimization (PSO) [43,51,61,61,107,127,151,160]. ...
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Thesis
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This thesis examines the privacy preserving energy management issue, taking into account both energy generation units and responsive demand in the smart grids. Firstly, because of the inherent stochastic behavior of the distributed energy resources, an optimal energy management problem is studied. Distributed energy resources are used in the decentralization of energy systems. Large penetration of distributed energy resources without the precise cybersecurity measures, such as privacy, monitoring and trustworthy communication may jeopardize the energy system and cause outages, and reliability problem for consumers. Therefore, a blockchain based decentralized energy system to accelerate electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk is proposed. A case study of sub-Sahara Africa is considered. 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The lower layer performs local credibility that reflects certain feedback of honest users on the accuracy of the forecast data. Lastly, combining blockchain mining and application intensive tasks increases the computational cost for resource constrained energy users. Besides, the anonymity and privacy problems of the users are not completely addressed in the existing literature. Therefore, this thesis proposes an improved sparse neural network to optimize computation offloading cost for resource constrained energy users. Furthermore, a blockchain system based on garlic routing, known as GarliChain, is proposed to solve the problems of anonymity and privacy for energy users during energy trading in the smart grid. Furthermore, a trust method is proposed to enhance the credibility of nodes in the GarliChain network. Simulations evaluate the theoretical results and prove the effectiveness of the proposed solutions. 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It means that about 89.23% energy price reduction is achieved for the proposed demand based pricing policy as compared to 83.46% for multi-parameter pricing scheme, 73.86% for fixed pricing scheme and 53.07% for the time of use pricing scheme. The vehicles minimize their operating costs up to 81.46% for the proposed demand based pricing policy as compared to 80.48% for multi-parameter pricing scheme, 69.75% for fixed pricing scheme and 68.29% for the time of use pricing scheme. Also, the proposed system outperforms an existing work, known as blockchain based secure incentive scheme in terms of low energy prices and high utility. Furthermore, the proposed system achieves an average block transaction cost of 1.66 USD. Besides, after applying the differential privacy, the risk of privacy loss is minimum as compared to existing schemes. Furthermore, higher privacy protection of vehicles is attained with a lower information loss against multiple background knowledge of an attacker. To analyze the efficiency of the proposed system regarding multi-data sharing, an experimental assessment reveals that about 85% of honest users share their data with stringent privacy measures. The remaining 15% share their data without stringent privacy measures. Moreover, the proposed system operates at a low operating cost while the credibility management system is used to detect malicious users in the system. Security analysis shows that the proposed system is robust against 51% attack, transaction hacking attack, impersonation attack and the double spending attack. To evaluate the proposed system regarding energy management of resource constrained blockchain energy users, a Jaya optimization algorithm is used to accelerate the error convergence rate while reducing the number of connections between different layers of the neurons for the proposed improved sparse neural network. Furthermore, the security of the users is ensured using blockchain technology while security analysis shows that the system is robust against the Sybil attack. Moreover, the probability of a successful Sybil attack is zero as the number of attackers’ identities and computational capacities increases. Under different sizes of data to be uploaded, the proposed improved sparse neural network scheme has the least average computational cost and data transmission time as compared to deep reinforcement learning combined with genetic algorithm, and sparse evolutionary training and multi-layer perceptron schemes in the literature. Simulation results of the proposed GarliChain system show that the system remains stable as the number of path requests increases. Also, the proposed trust method is 50.56% efficient in detecting dishonest behavior of nodes in the network as compared to 49.20% of an existing fuzzy trust model. Under different sizes of the blocks, the computational cost of the forwarding nodes is minimum. Security analysis shows that the system is robust against both passive and active attacks. Malicious nodes are detected using the path selection model. Moreover, a comparative study of the proposed system with existing systems in the literature is provided.
... In [51], a system model is presented for residential load scheduling that allows customization and configuration of parameters such as renewable resources and hybrid electrical storage systems (HESS) and improves cost savings by up to 45%. In [52], an advanced HEMS is proposed with non-intrusive load monitoring (NILM) to reduce PAR and improve user comfort. ...
... In [51], a system model is presented for residential load scheduling that allows customization and configuration of parameters such as renewable resources and hybrid electrical storage systems (HESS) and improves cost savings by up to 45%. In [52], an advanced HEMS is proposed with non-intrusive load monitoring (NILM) to reduce PAR and improve user comfort. ...
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