Perspectives and Intensification of Energy Efficiency in Commercial and Residential Buildings Using Strategic Auditing and Demand-Side Management

Abstract

Abstract: With the ever-growing power demand, the energy efficiency in the commercial and residential buildings has become a matter of great concern. Also, the strategic energy auditing (SEA) and demand-side management (DSM) are the cost-effective means to identify the requirements of power components and their operation in the energy management system. In a commercial or residential building, the major components are lighting source and heating, ventilating, and air conditioning. The number of these components, to be installed, depends upon the technical and environmental standards. In this scenario, energy auditing (EA) allows identifying the methods, scope, and time for energy management and helps the costumers to manage their energy consumption wisely to reduce electricity bills. In the literature, most of the traditional strategies employ specific system techniques and algorithms, whereas, in recent years, load shifting based DSM techniques have been used under different operating scenarios. Considering these facts, the energy data in a year is collected under three different seasonal changes, i.e. severe cold, moderate, and severe hot for the variation in load demand under different environmental conditions. In this work, the energy data under three conditions are averaged, and the DSM schemes are developed for the operation of power components before energy auditing and after energy auditing. Moreover, the performance of the proposed DSM techniques is compared with the practical results in both scenarios, and from the results, it has been observed that the energy consumption has reduced significantly in the proposed DSM approach.

Full text

energies
Article
Perspectives and Intensification of Energy Efficiency
in Commercial and Residential Buildings Using
Strategic Auditing and Demand-Side Management
Pawan Kumar 1, * , Gagandeep Singh Brar 1, Surjit Singh 2, Srete Nikolovski 3,
Hamid Reza Baghaee 4and Zoran Balki´c 5
1Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala,
Punjab 147004, India; mailgagan663@gmail.com
2
Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004,
India; surjitmehla@gmail.com
3
Power Engineering Department, Faculty of Electrical Engineering Computing and Information Technology,
31000 Osijek, Croatia; srete.nikolovski@ferit.hr
4Department of Electrical Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran;
hrbaghaee@aut.ac.ir
5Barrage d.o.o., 31000 Osijek, Croatia; zoran.balkic@33barrage.com
*Correspondence: pawanror@gmail.com; Tel.: +91-830-723-7199
Received: 23 October 2019; Accepted: 26 November 2019; Published: 28 November 2019


Abstract:
With the ever-growing power demand, the energy efficiency in commercial and residential
buildings is a matter of great concern. Also, strategic energy auditing (SEA) and demand-side
management (DSM) are cost-effective means to identify the requirements of power components and
their operation in the energy management system. In a commercial or residential building, the major
components are light sources and heating, ventilation, and air conditioning. The number of these
components to be installed depends upon the technical and environmental standards. In this scenario,
energy auditing (EA) allows identifying the methods, scope, and time for energy management, and it
helps the costumers to manage their energy consumption wisely to reduce electricity bills. In the
literature, most of the traditional strategies employed specific system techniques and algorithms,
whereas, in recent years, load shifting-based DSM techniques were used under different operating
scenarios. Considering these facts, the energy data in a year were collected under three different
seasonal changes, i.e., severe cold, moderate, and severe heat for the variation in load demand
under different environmental conditions. In this work, the energy data under three conditions
were averaged, and the DSM schemes were developed for the operation of power components
before energy auditing and after energy auditing. Moreover, the performance of the proposed DSM
techniques was compared with the practical results in both scenarios, and, from the results, it was
observed that the energy consumption reduced significantly in the proposed DSM approach.
Keywords:
energy auditing; energy efficiency; commercial loads; demand-side management;
operating scenario
1. Introduction
Energy demand management, also known as demand-side management (DSM) or demand-side
response (DSR), is the modification of consumer demand for energy through various methods such as
financial incentives and behavioral change through education [
1
]. Usually, the goal of demand-side
management is to encourage the consumer to use less energy during peak hours, or to move the
time of energy use to off-peak times such as nighttime and weekends. Peak demand management
Energies 2019,12, 4539; doi:10.3390/en12234539 www.mdpi.com/journal/energies

Energies 2019,12, 4539 2 of 31
does not necessarily decrease total energy consumption but could be expected to reduce the need for
investments in networks and/or power plants for meeting peak demands [
2
]. An example is the use of
energy storage units to store energy during off-peak hours and discharge them during peak hours [
3
].
A newer application for DSM is to aid grid operators in balancing intermittent generation from wind
and solar units, particularly when the timing and magnitude of energy demand do not coincide with
the renewable generation [4].
The American electric power industry originally relied heavily on foreign energy imports, whether
in the form of consumable electricity or fossil fuels that were then used to produce electricity. During
the time of the energy crises in the 1970s, the federal government passed the public utility regulatory
policies act (PURPA), hoping to reduce dependence on foreign oil and to promote energy efficiency
and alternative energy sources. This act forced utilities to obtain the cheapest possible power from
independent power producers, which in turn promoted renewables and encouraged the utility to
reduce the amount of power they need, thereby pushing forward agendas for energy efficiency and
demand management [
5
]. The term DSM was coined following the time of the 1973 energy crisis and
the 1979 energy crisis. Governments of many countries mandated the performance of various programs
for demand management. An early example is the National Energy Conservation Policy Act of 1978
in the United States (US), preceded by similar actions in California and Wisconsin. Demand-side
management was introduced publicly by the electric power research institute (EPRI) in the 1980s [
6
].
Currently, DSM technologies are becoming increasingly feasible due to the integration of information
and communications technology and the power system, leading to new terms such as integrated
demand-side management (IDSM) or smart grid.
With recent development in DSM technologies in distribution networks, the reduction in energy
consumption is the prime motive of the research due to the depletion of conventional energy resources.
Researchers presented extensive work for improvement in energy efficiency to reduce the operating
cost simultaneously. However, the objective functions developed by different researchers may
differ from each other, depending upon the operating constraints and requirements. The energy
efficiency performance in power delivery is not limited to a reduction in loss; rather, it depends upon
several other parameters and load type or load class [
7
,
8
]. The practical loads can be divided into
residential, commercial, and industrial loads [
9
]. With these load classes, in Reference [
10
], a heuristic
optimization technique was presented for DSM to reduce the peak demand, whereas, in Reference [
11
],
the perspectives of energy efficiency ere studied.
Costanzo et al. [
12
] revealed that the estimated power consumption in worldwide buildings leads
to approximately 40% of global energy consumption. Also, Agnetis et al. [
13
] emphasized the reduction
of the greenhouse effect through dynamic DSM and developed a heuristic algorithm for real-time
application, but it had the limitation of computational power and memory size. Palensky et al. [
14
]
observed that the rise in technology generation is not a problem, but grid capacity is a major concern.
Therefore, DSM helps to overcome this problem. Here, the authors categorized the DSM into energy
efficiency, time of use, demand response (DR), and spinning reserve.
Kuzlu et al. [
15
] forecasted that, in upcoming decades, the world energy consumption is expected
to rise by 53% at the rate of 2.3% per year from now to 2035. This would be the reason for cascading
failure and, hence, blackouts. Here, the authors developed a cost-effective home energy management
(HEM) system. Furthermore, Mondal et al. [
16
] observed that DSM is an important feature in a smart
grid as it allows flexible energy demand. Conversely, Tsagarakis et al. [
17
] revealed that the electricity
cost not only consists of the price of electricity but also includes the environmental costs.
Marcello et al. [
18
] presented operating strategies for resource management of DSM, where energy
efficiency, along with comfort level, was addressed in the problem formulation. In this paper, we
consider the proposed strategies as per the time of the day, which means the energy consumption
needs to be managed with every change in operating conditions. Singh and Jha [
19
] developed a
multi-objective approach for DSM by using several indices. These indices were developed to reduce

Energies 2019,12, 4539 3 of 31
the peak load demand and usability with renewable energy resources using the teaching–learning
process algorithm.
Hao et al. [
20
] revealed that the heating, ventilation, and air-conditioning (HVAC) systems are
important aspects for energy management, and the authors proposed a transactive control approach of
a commercial building through demand response. However, the authors in Reference [
21
] developed
multifrequency agent coordination for DSM in electrical grids with high penetration rates of distributed
and local generation. Facchini et al. [
22
] described the need for operating schedules for energy
management for domestic purposes while satisfying the user’s constraints on the maximum tolerable
delays. Here, the authors tried to minimize the operating cost by reducing the peak demand for a
given duration. Similarly, in Reference [
23
], an energy service model was presented for residential
buildings for energy service for the hourly variation of the demand for energy that realizes the service
in the presence of distributed energy resources.
The authors in Reference [
24
] presented the demand response in three different scenarios by
considering (a) benchmarking without demand response and utility choice, (b) without demand
response and with utility choice, and (c) in the coordination of demand response and utility choice.
However, the ultimate aim was to reduce the peak load and, hence, the energy consumption, whereas
the social and technical standards were not emphasized in these works. Also, in the survey work in
Reference [
25
], several method issues and future perspectives in the DSM-based approaches were
presented, which has a limited scope as most of the aspects were considered by different studies in
their work. In Reference [
26
], an air-conditioning load was considered for demand response and
to identify the variation in demand versus temperature. This analysis revealed that the demand is
not fixed as the rated value of the power component; rather, it needs to consider the environmental
constraints while implementing the demand response approaches for DSM. Moreover, the authors
in Reference [
27
] proposed a pricing schemes to encourage the participation of different consumers
in demand response by providing them with a list of price plans. Here, customers were classified
based upon the level of load adjustment, cost analysis, and the elasticity coefficient. Here also, the load
characteristics and their dependence on different constraints were emphasized. Piette et al. [
28
]
presented an infrastructure model for automated demand response. In this scenario, a new Internet
of things (IoT)-based infrastructure was also developed by the researcher for energy internet in
Reference [29].
In this paper, the authors conferred that the energy demand has a relationship with the
environmental conditions, and several plots were presented showing the relationship between
energy and temperature and humidity. However, in this work, demand response or DSM was not
implemented; rather, direct control of the load was actuated through the internet and smart devices.
Therefore, this work had a limited scope of energy efficiency, which focused on energy consumption by
changing the status of the control switch from OFF to ON and vice versa, and it is best suitable for the
area where a single person is involved, or a defined set of rules are followed. Conversely, the practical
loads were not of any specific type, and the load growth of these loads concerning time was neither
uniform nor did it follow the same pattern as earlier [
30
,
31
]. Considering several aspects, the authors
in Reference [
32
] presented an extensive review on the energy internet for its smart management,
and several issues were discussed, which include cost, reliability, scalability, data access, and weather
as the prime factors for energy internet. However, the real behavior of people in the building was
also presented for energy and cost-saving in Reference [
33
], whereas, in References [
34
–
38
], different
criteria for the installation of various components were suggested.
From the related literature, it can be observed that energy efficiency is a cost-effective means of
energy-saving. Also, saving a single unit of energy is always viewed as an energy resource. In practice,
a commercial building is believed to have several electrical components that operate together but that
have different energy consumption as per the environmental and technical aspects, which include
light intensity, heating, ventilation (air circulator), and air conditioning (i.e., L-HVAC). However,
the requirement of power equipment in a specified area depends upon the number of persons involved

Energies 2019,12, 4539 4 of 31
in a specific area, technical standards, surrounding environmental conditions, and the availability of
the supply. Therefore, considering these aspects, the aims of this work can be summarized as follows:
1.
To determine the energy efficiency of lighting, ventilation, and air conditioning (LVAC), concerning
the task areas and non-task areas in a commercial and residential building.
2.
To recommend consumption levels suitable for various activities under different operating and
environmental conditions.
3.
To determine the overall energy efficiency of LVAC systems using measurements and methods
suitable for field conditions with and without DSM before auditing and after auditing.
4.
To formulate the problem for energy efficiency under the above different objectives with social,
environmental, and technical constraints of DSM, which include the number of persons involved,
surrounding temperature and humidity, the lumens per watt, air circulation in cubic feet per
minute, cooling per unit area or volume, energy consumption in a specified area, etc.
2. Strategic Auditing
In the existing approaches [
9
–
11
], the energy efficiency in residential and commercial buildings
was evaluated based upon the time of operation, energy density, and cost of utilization with and without
environmental constraints. This requires strategies for demand response (DR), and these strategies
may or may not be applicable under different operating constraints. Therefore, it becomes necessary to
identify the other aspects of energy-saving while developing the DSM strategies, particularly when the
above constraints have the least contribution to energy policy.
In this scenario, energy auditing (EA) was found to be a cost-effective means to identify the
requirements of power components and to control their operation. In a commercial and residential
building, the major components are light sources and heating, ventilation, and air conditioning
(L-HVAC). The number of these components to be installed depends upon the area covered and the
operating requirements such as minimum lumens, air circulation in cubic feet per minute, and the
temperature and humidity level. Therefore, EA allows identifying the methods, scope, and time for
energy management and helps the costumers to manage their energy consumption wisely to reduce
their energy bills [38].
2.1. Auditing Parameters
The auditing parameters for power components are different, and they are listed in Table 1.
Table 1. Auditing parameters for lighting, fans, and air conditioning. BTU—British thermal units.
Part-A: Parameters for Lighting Systems
Sl Parameters Symbols Remarks
1 Room index RI
Required to identify the number of illuminance
measuring points in working and
non-working areas
2 Room cavity ratio RCR Required to identify the space to be illuminated
3
Average illuminance in
working and
non-working areas
Eav, task
Eav, non-task
Average illuminance in working and non-working
areas helps to calculate the number of
illuminating points
4 Number of luminaires NL
To decide the uniform distribution in a specified
area, the number of luminaires needs to
be calculated
5 Lumens per unit watt ln/WIt is the illuminance developed by the installed
lighting system

Energies 2019,12, 4539 5 of 31
Table 1. Cont.
Part-A: Parameters for Lighting Systems
6 Load efficacy ratio ILE The efficacy of the luminaires depends upon the
lumens per unit of its rating in W or kW
7 Installed load efficacy ratio ILER
It is the ratio of existing ILE to the recommended
ILE in the specified area; if it is less than 0.5, a
necessary action needs to be taken
8 Diversity factor DF It describes the effective utilization of the light
source in the working area; ideally, it should be 3
9 Maintenance factor MF The lumen developed by the light source
deteriorates with time
10 Utilization factor UF It depends upon the room size and the material
used in the construction for plastering, flooring, etc.
Part-B: Parameters for Fans
1 Cubic feet per minute CFM It is the amount of air circulated by the fan in
one minute
2The energy efficiency of the
fan EEF It is the CFM developed by the fan per unit of its
wattage at a given speed
3 Number of fans NFThe number of fans in a specified area depends
upon the CFM and EEF
Part-C: Parameters for Air-Conditioning Units (ACs)
1 BTU per unit area BTU/m2The criterion for AC selection mainly depends
upon the BTU required in a unit area
2 BTU per unit volume BTU/m3For non-official purposes, the BTU required may
also be evaluated based on the unit volume
3 Number of ACs NAC
The number of ACs to be installed in a unit area or
volume further depends upon the operating
constraints like temperature, humidity, and time
of operation
2.2. Mathematical Expression for Auditing Parameters
2.2.1. Room Index
The room index describes the number of measuring points for light intensity inside the room. It
depends upon the length (L), width (W), and the height of the luminaire above the plane of measurement
(Hm). The room index is calculated as
RI =L∗W
Hm+(L∗W). (1)
Generally, if RI <1, the number of measuring points is taken as eight. If 1<RI <2, the number of
measuring point is 18, and, if 2 <RI <3, the number of measuring points is 32. Similarly, if RI >3,
the number of measuring points is 50 [38].
2.2.2. Room Cavity Ratio
The RI gives the information regarding the number of illuminance measuring points, whereas the
room cavity ratio (RCR) is useful for the identification of the area to be illuminated from the total room
size. Therefore, RCR is calculated as
RCR =5Hm
(L+W)
LW . (2)

Energies 2019,12, 4539 6 of 31
2.2.3. Average and Total Illuminance
The level of illuminance in a specified area may not be uniform at every point of measurement.
Ideally, illuminance should be more in the working area (A
working
) as compared to the non-working
area (A
non-working
). From the room index, the number of illuminance measuring points (IMPs) can be
decided, whereas the IMP in working and non-working areas may be different, which is calculated as:
IMPworking =Aworkin g
Aworking +Anon−working
×Total number of IMPs, (3)
IMPnon−working =Anon−workin g
Aworking +Anon−working
×Total number of IMPs. (4)
After the calculation of IMPs in working and non-working areas, the illuminance is measured.
Therefore, the average illuminance is calculated as follows:
Eav =E1+E2+. . . . . . . . . . . . . . . . . . +EN
N×Correction Factor, (5)
where Nis the total number of IMPs, and the correction factor depends upon the type of instruments
used for the measurement of illuminance. In this work, the correction factor is taken as one for
simplicity. In Equation (5), the average illuminance is given per unit area. If Lis the length and Wis
the width of the room, then the total illuminance in a specified area is calculated as
∅m=Eav ×L×W. (6)
2.2.4. Installed Load Efficacy
The luminous flux developed by different light sources depends upon the circuit wattage and
type of luminaire. Therefore, the installed load efficacy (ILE) is given by
ILE =Average luminous f lux (lumens)on the sur f ace
Circuit watts lm/watts. (7)
2.2.5. Installed Load Efficacy Ratio
In practice, ILE may vary from its required level. In this scenario, the ratio of ILE and the targeted
load efficacy (TLE) is the indicator of the effectiveness of the lighting system for illuminance in a
specified area. This ratio is termed as the installed load efficacy ratio (ILER), and it is calculated as
ILER =Installed load e f ficacy
Target load e f f icacy . (8)
Depending upon the operating scenario, the requirement of illuminance may vary from one type
of building to another type of building or working area. Therefore, if 0.75 <ILER
≤
1, it is considered
good, and, when 0.51 <ILER <0.74, a review of the lighting system is required; however, if
ILER <0.5
,
then serious action needs to be taken for energy efficiency. The low value of ILER may be due to
inefficient lamps, high mounting, poor reflectors, long working hours of the light source, etc.
2.2.6. Diversity Ratio
In practice, the illuminance level of the working and non-working areas is found to be different.
The ratio of average illuminance in the working area to the average illuminance in the non-working
area describes the effectiveness of the utilization of the light system and, hence, the energy efficiency.
This ratio is termed as the diversity ratio (DR) and is calculated using Equation (9).

Energies 2019,12, 4539 7 of 31
DR =Eavg working
Eavg nonworking
. (9)
Ideally, for general lighting purposes, the DR should vary in the ratio of 3:1 for effective lighting
for usual commercial areas. However, the DR range can be even more than this ratio, depending upon
the type of work to be carried out in a specified area.
2.2.7. Utilization Factor
The utilization factor depends upon the ceiling reflection, wall reflection, and floor reflection.
Therefore, the coefficient of utilization is calculated from the zonal cavity method [37] as
Y2=X2+(RCR −X1)(Y3−Y1)
X3−X1
, (10)
where the values of X
1
and X
3
are obtained from the lower and upper bounds of the value of RCR,
whereas X2,Y1, and Y3are obtained from the zonal cavity method corresponding to X1and X3.
2.2.8. Number of Luminaires
The number of luminaires (N
L
) in a specified area depends upon the illuminance required,
maintenance factor, and the lumens produced by the luminaires. Therefore, the number of luminaires
(NL) is calculated as
NL=Illuminance required (lux)×Aream2
Utilization f actor ×Maintenance f actor ×lumens/m2. (11)
2.2.9. Cubic Feet per Minute
Cubic feet per minute (CFM) is an important parameter for the evaluation of the performance of a
fan. It depends upon the volume of the room and the airflow rate per hour in a specified area.
CFM =The volume o f the room (in cubic f eet)
Air change flow per hour . (12)
2.2.10. The Energy Efficiency of the Fan
The energy efficiency of a fan is defined as the ratio of CFM to the wattage of the fan.
EEF =CFM
Wattage. (13)
It varies between 60% and 90% at full speed, and the energy efficiency of the fan in this work was taken
as 70%.
2.2.11. Number of Fans
The number of fans (NF) required to be installed in a room is calculated as
NF=CFM required in unit area
The wattage ∗energ y e f f iciency o f a f an . (14)
2.2.12. Tons of Refrigeration
Here, one ton of refrigeration (TR) is considered equivalent to 3024 kcal/h heat rejected.

Energies 2019,12, 4539 8 of 31
2.2.13. Coefficient of Performance
The coefficient of performance (COP) is a measure of the amount of power input to a system
compared to the amount of power output by that system. The COP is, therefore, a measurement of
efficiency, and a higher value makes the system more efficient.
2.2.14. Energy Efficiency Ratio
The energy efficiency ratio (EER) is the ratio of output cooling energy (in British thermal units,
BTUs) to electrical input energy (in watt-hours).
EER =output cooling energy (in BTU)
input electrical energy (in watt −hour). (15)
2.2.15. Kilowatt per Ton
The efficiencies of large industrial air-conditioning systems (ACs), especially chillers, are given in
kW/ton to specify the amount of electrical power that is required for a certain power of cooling.
Power output in Watts
Power input in Watts =3.517
kW/ton . (16)
For the calculation of the number of ACs required to be installed in a room, two methods are used.
1. Area Method
This method has two criteria that give information about the calculation of the number of ACs
recommended to be installed in a room [36].
Criterion 1:
Tonnage Required/unit area =Area ×25
12000 ±0.5 tons. (17)
Criterion 2:
Tonnage Required/unit area =Area(square root o f square f eet)
10 tons. (18)
2. Volume method
This method has one criterion that gives information about the calculation of the number of ACs
recommended to be installed in a room [36].
Tonnage required/unit volume =Volume (Cubic f eet)
1000 tons. (19)
3. Data Collection and Analysis
The energy data were collected from the D-Block of Thapar Institute of Engineering and Technology,
Patiala. The building data related to the room size were obtained by measurement, and the number
of power components were obtained by observation, whereas the energy data were collected from
Monday to Friday on an hourly basis between 9:00 a.m. and 5:00 p.m. from the meter reading. These
energy data included the consumption due to fans, lighting systems, and air conditioners installed
in the D-Block. Throughout one year, three different seasons were considered according to normal,
moderate, and severe environmental conditions. Because of this issue, the energy data for August
2018, January 2019, and April 2019 were collected on an hourly basis. The academic institution is a
commercial building, and the energy consumption mainly depends upon the temperature, humidity,
and the number of persons present in working hours between 9:00 a.m. to 5:00 p.m. Thus, the energy
data were segregated for lights, fans, and the ACs. Table 2shows the size of the room and the number

Energies 2019,12, 4539 9 of 31
of lighting sources (i.e., tube-light and compact fluorescent lamp (CFL)), ACs, and fans in a sample
building under consideration in this work.
Table 2. Data collection and analysis.
Room
No.
Length
(ft)
Breadth
(ft)
Height
(ft)
Tube Light
(Single)
CFL
(Double)
Tube Light
(Double) Fans ACs Room Load (W)
Light Fan ACs
D106 18.50 8.67 8.42 2 3 3 2 2.5 490 120 5700
D104 38 30 8.83 - - 13 7 3.5 1300 420 7980
D107 18.5 8.67 8.42 2 1 - 2 - 130 120 -
D108 18.5 8.67 8.42 2 1 - 2 - 130 120 -
D109 18.5 8.67 8.42 3 - - 2 - 150 120 -
D110 18.5 8.67 8.42 3 - - 2 – 150 120 –
D101 29.92 24.42 9.08 - - 9 6 2 900 360 4560
D102 29.92 24.42 9.08 - - 8 6 2 800 360 4560
D116 41.25 50.5 11.83 - - 26 16 - 2600 960 -
D115 41.25 50.5 11.83 - - 26 12 - 2600 720 -
R-Lab 35 9.42 9 - - 4 4 1.5 400 240 3420
D201 35.75 29.83 9.42 - 14 6 11 2 1020 660 4560
D202 35.75 29.83 9.42 - 14 6 11 2 1020 660 4560
D203 29.66 18.33 9.42 - 11 - 5 1.5 330 300 3420
D204 29.66 18.33 9.42 - 11 - 5 1.5 330 300 3420
Cabin
S-lab 12.75 12.42 12.42 - - 2 1 1.5 200 60 3420
S-Lab 157 30.75 16.08 4 14 15 36 - 2120 2160 -
D120 13 10 13.83 2 - - 1 - 100 60 -
Cabin-1
30.75 13 13.83 3 - - 2 1 150 120 2280
D112 31.75 28.5 8.92 - - 8 11 2 800 660 4560
T-Lab 49.42 29 12.92 - 20 - 15 - 600 900 -
D117 30.42 12.25 14.25 2 - - 2 - 100 120 -
D114 18.58 8.58 14.42 - 2 - 2 - 60 120 -
D113 18.58 8.58 14.42 - 2 - 2 - 60 120 -
D111 18.75 19.83 9.17 - 4 - 2 - 120 120 -
D123 13 10 13.83 2 - - 1 - 100 60 -
Cabin-2
19.33 8.67 8.42 2 4 - 2 - 220 120 -
D103 13 10 13.83 2 - - 1 - 100 60 -
D118 30.42 22.67 9.33 - 6 8 4 1.5 980 240 3420
Room
stairs 12.75 12.42 12.42 - - 4 1 - 400 60 -
D119 13 10 13.83 - - 4 1 1 400 60 2280
D121 12.83 12.25 9.42 - - 4 1 - 400 60 -
3.1. Energy Data Analysis
For the analysis of energy data, three months of energy consumption were recorded based on
the weekday average and the monthly average. This energy data analysis was recorded under the
variation in environmental conditions in summer (April), rainy (August), and winter (January) seasons
in the Indian scenario.
Figures 1–3represent the variation of average energy consumption for all Mondays, Tuesdays,
Wednesdays, Thursdays, and Fridays in summer, rainy, and winter seasons, respectively. From the
data, it can be observed that there is a significant variation in energy consumption from 9:00 a.m. to
5:00 p.m., and it is different on different days of the week.

Energies 2019,12, 4539 10 of 31
Energies 2019, 12, 4539 10 of 31
For the analysis of energy data, three months of energy consumption were recorded based on
the weekday average and the monthly average. This energy data analysis was recorded under the
variation in environmental conditions in summer (April), rainy (August), and winter (January)
seasons in the Indian scenario.
Figures 1–3 represent the variation of average energy consumption for all Mondays, Tuesdays,
Wednesdays, Thursdays, and Fridays in summer, rainy, and winter seasons, respectively. From the
data, it can be observed that there is a significant variation in energy consumption from 9:00 a.m. to
5:00 p.m., and it is different on different days of the week.
Figure 1. Average energy consumption (EC) on weekdays in the rainy season.
Figure 2. Average EC on weekdays in the winter season.
Figure 3. Average EC on weekdays in the summer season.
In addition to the above analysis, the monthly variation in energy consumption is shown in
Table 3. Also, temperature, humidity, and luminous intensity due to sunlight were evaluated in rainy,
0
10
20
30
40
50
60
70
9:00AM-10:00AM 10:00AM-11:00AM11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
EC in rainy days
Monday Tuesday Wednesday Thursday Friday
0
2
4
6
8
10
12
14
9:00AM-10:00AM 10:00AM-11:00AM 11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
EC in winter days
Monday Tuesday Wednesday Thursday Friday
0
10
20
30
40
50
60
9:00AM-10:00AM 10:00AM-11:00AM 11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
EC in summer days
Monday Tuesday Wednesday Thursday Friday
Figure 1. Average energy consumption (EC) on weekdays in the rainy season.
Energies 2019, 12, 4539 10 of 31
For the analysis of energy data, three months of energy consumption were recorded based on
the weekday average and the monthly average. This energy data analysis was recorded under the
variation in environmental conditions in summer (April), rainy (August), and winter (January)
seasons in the Indian scenario.
Figures 1–3 represent the variation of average energy consumption for all Mondays, Tuesdays,
Wednesdays, Thursdays, and Fridays in summer, rainy, and winter seasons, respectively. From the
data, it can be observed that there is a significant variation in energy consumption from 9:00 a.m. to
5:00 p.m., and it is different on different days of the week.
Figure 1. Average energy consumption (EC) on weekdays in the rainy season.
Figure 2. Average EC on weekdays in the winter season.
Figure 3. Average EC on weekdays in the summer season.
In addition to the above analysis, the monthly variation in energy consumption is shown in
Table 3. Also, temperature, humidity, and luminous intensity due to sunlight were evaluated in rainy,
0
10
20
30
40
50
60
70
9:00AM-10:00AM 10:00AM-11:00AM11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
EC in rainy days
Monday Tuesday Wednesday Thursday Friday
0
2
4
6
8
10
12
14
9:00AM-10:00AM 10:00AM-11:00AM 11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
EC in winter days
Monday Tuesday Wednesday Thursday Friday
0
10
20
30
40
50
60
9:00AM-10:00AM 10:00AM-11:00AM 11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
EC in summer days
Monday Tuesday Wednesday Thursday Friday
Figure 2. Average EC on weekdays in the winter season.
Energies 2019, 12, 4539 10 of 31
For the analysis of energy data, three months of energy consumption were recorded based on
the weekday average and the monthly average. This energy data analysis was recorded under the
variation in environmental conditions in summer (April), rainy (August), and winter (January)
seasons in the Indian scenario.
Figures 1–3 represent the variation of average energy consumption for all Mondays, Tuesdays,
Wednesdays, Thursdays, and Fridays in summer, rainy, and winter seasons, respectively. From the
data, it can be observed that there is a significant variation in energy consumption from 9:00 a.m. to
5:00 p.m., and it is different on different days of the week.
Figure 1. Average energy consumption (EC) on weekdays in the rainy season.
Figure 2. Average EC on weekdays in the winter season.
Figure 3. Average EC on weekdays in the summer season.
In addition to the above analysis, the monthly variation in energy consumption is shown in
Table 3. Also, temperature, humidity, and luminous intensity due to sunlight were evaluated in rainy,
0
10
20
30
40
50
60
70
9:00AM-10:00AM 10:00AM-11:00AM11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
EC in rainy days
Monday Tuesday Wednesday Thursday Friday
0
2
4
6
8
10
12
14
9:00AM-10:00AM 10:00AM-11:00AM 11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
EC in winter days
Monday Tuesday Wednesday Thursday Friday
0
10
20
30
40
50
60
9:00AM-10:00AM 10:00AM-11:00AM 11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
EC in summer days
Monday Tuesday Wednesday Thursday Friday
Figure 3. Average EC on weekdays in the summer season.
In addition to the above analysis, the monthly variation in energy consumption is shown in
Table 3. Also, temperature, humidity, and luminous intensity due to sunlight were evaluated in rainy,
winter, and summer seasons, as shown in Figures 4–6. Here also, these data were taken on an hourly
basis, i.e., from 9:00 a.m. to 5:00 p.m. These data give the information of load variation of a complete
day, which further helps in load shifting at the time of peak loads.

Energies 2019,12, 4539 11 of 31
Table 3. The average value of energy consumption (EC) in different seasons on an hourly basis.
Parameters and
Time
Average EC in Rainy Season Average EC in Winter Season Average EC in Summer Season
EC_FnT
(kWh)
EC_AC
(kWh)
EC_Total
(kWh)
EC_FnT
(kWh)
EC_AC
(kWh)
EC_Total
(kWh)
EC_FnT
(kWh)
EC_AC
(kWh)
EC_Total
(kWh)
9:00–10:00 a.m. 40 7 47 10 0 10 10 4 14
10:00–11:00 a.m. 40 0 40 10 0 10 10 3 13
11:00 a.m.
–12:00 p.m. 50 0 50 10 0 10 20 4 24
12:00–1:00 p.m. 30 0 30 0 0 0 20 4 24
1:00–2:00 p.m. 0 6 6 10 0 10 20 3 23
2:00–3:00 p.m. 10 4 14 10 0 10 10 5 15
3:00–4:00 p.m. 30 4 34 10 0 10 10 5 15
4:00–5:00 p.m. 0 4 4 0 0 0 10 5 15
Energies 2019, 12, 4539 11 of 31
winter, and summer seasons, as shown in Figures 4–6. Here also, these data were taken on an hourly
basis, i.e., from 9:00 a.m. to 5:00 p.m. These data give the information of load variation of a complete
day, which further helps in load shifting at the time of peak loads.
Table 3. The average value of energy consumption (EC) in different seasons on an hourly basis.
Parameters
and
Time
Average EC in Rainy
Season
Average EC in Winter
Season
Average EC in Summer
Season
EC_Fn
T
(kWh)
EC_A
C
(kWh)
EC_Tot
al
(kWh)
EC_FnT
(kWh)
EC_A
C
(kWh)
EC_Tot
al
(kWh)
EC_FnT
(kWh)
EC_A
C
(kWh)
EC_Tot
al
(kWh)
9:00–10:00
a.m. 40 7 47 10 0 10 10 4 14
10:00–11:00
a.m. 40 0 40 10 0 10 10 3 13
11:00 a.m.–
12:00 p.m. 50 0 50 10 0 10 20 4 24
12:00–1:00
p.m. 30 0 30 0 0 0 20 4 24
1:00–2:00
p.m. 0 6 6 10 0 10 20 3 23
2:00–3:00
p.m. 10 4 14 10 0 10 10 5 15
3:00–4:00
p.m. 30 4 34 10 0 10 10 5 15
4:00–5:00
p.m. 0 4 4 0 0 0 10 5 15
Figure 4. Variation in temperature in different seasons.
Figure 5. Variation in humidity in different seasons.
0
10
20
30
40
9:00AM-10:00AM 10:00AM-11:00AM11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
Temperature ( deg. C)
Rainy Winter Summer
0
50
100
Humidity, (%age)
Rainy Winter Summer
Figure 4. Variation in temperature in different seasons.
Energies 2019, 12, 4539 11 of 31
winter, and summer seasons, as shown in Figures 4–6. Here also, these data were taken on an hourly
basis, i.e., from 9:00 a.m. to 5:00 p.m. These data give the information of load variation of a complete
day, which further helps in load shifting at the time of peak loads.
Table 3. The average value of energy consumption (EC) in different seasons on an hourly basis.
Parameters
and
Time
Average EC in Rainy
Season
Average EC in Winter
Season
Average EC in Summer
Season
EC_Fn
T
(kWh)
EC_A
C
(kWh)
EC_Tot
al
(kWh)
EC_FnT
(kWh)
EC_A
C
(kWh)
EC_Tot
al
(kWh)
EC_FnT
(kWh)
EC_A
C
(kWh)
EC_Tot
al
(kWh)
9:00–10:00
a.m. 40 7 47 10 0 10 10 4 14
10:00–11:00
a.m. 40 0 40 10 0 10 10 3 13
11:00 a.m.–
12:00 p.m. 50 0 50 10 0 10 20 4 24
12:00–1:00
p.m. 30 0 30 0 0 0 20 4 24
1:00–2:00
p.m. 0 6 6 10 0 10 20 3 23
2:00–3:00
p.m. 10 4 14 10 0 10 10 5 15
3:00–4:00
p.m. 30 4 34 10 0 10 10 5 15
4:00–5:00
p.m. 0 4 4 0 0 0 10 5 15
Figure 4. Variation in temperature in different seasons.
Figure 5. Variation in humidity in different seasons.
0
10
20
30
40
9:00AM-10:00AM 10:00AM-11:00AM11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
Temperature ( deg. C)
Rainy Winter Summer
0
50
100
Humidity, (%age)
Rainy Winter Summer
Figure 5. Variation in humidity in different seasons.
Energies 2019, 12, 4539 12 of 31
Figure 6. Variation in light intensity in different seasons.
3.2. Energy Auditing of the Lighting System
In practice, all of the light emitted by the lamp does not reach the work area. Some light is
absorbed by the luminaire, walls, floors, roof, etc. The illuminance measured in lumens/m2, i.e., lux,
indicates how much light, i.e., lumens, is available per square meter of the measurement plane. Target
luminous efficacy (lm/watts) of the light source is the ratio of lumens that can be made available at
the work plane under the best luminous efficacy of source, room reflectance, mounting height, and
the power consumption of the lamp circuit. Over time, the light output from the lamp is reduced, the
room surfaces become dull, and luminaries become dirty; hence, the light available on the work plane
deviates from the target value. The ratio of the actual luminous efficacy on the work plane and the
target luminous efficacy at the work plane is defined as the installed load efficacy ratio (ILER).
Table A1 (Appendix A1) shows the variation in the coefficient of utilization when effective floor
cavity reflectance is taken as 20%. Tables 4–6 show the readings of monthly data, which include the
average light intensity and its value in task and non-task areas, lumens, ILE and ILER, and the
diversity factor for each room of the sample building in rainy, winter, and summer seasons
respectively.
Table 4. Energy efficiency evaluation of the lighting system of D-block in the rainy season.
Room No. Eav Lumens
ILE ILER
Eav task Eav, non-task DF
D106 493.56 7355 15.01 0.33 422.50 510.10 0.83
D104 1231.42 130,485 100.37 2.18 1259.57 786.50 1.60
D107 814.10 12,133 93.33 2.03 643.83 537.50 1.20
D108 585.79 8730 67.16 1.46 558.17 383.50 1.46
D109 693.79 10,340 68.93 1.46 629.83 286.50 2.20
D110 693.79 10,340 68.93 1.46 629.83 286.50 2.20
D101 108.10 7332 8.15 0.18 102.55 68.86 1.49
D102 254.02 17,248 21.56 0.47 242.36 189.72 1.28
D116 621.43 120,326 46.28 1.07 781.36 448.25 1.74
D115 805.68 156,002 60.00 1.30 1059.57 613.32 1.73
Research Lab 236.95 7258 18.15 0.39 219.40 194.67 1.13
D201 1511.56 149,848 146.91 32.65 1361.07 247.25 5.50
D202 988.63 98,007 96.09 2.09 1361.29 825.50 1.65
D203 2478.17 125,252 379.55 8.25 2665.57 650.22 4.10
D204 1797.55 90,852 275.31 5.99 2399.93 1433.75 1.67
Cabin (S-
Lab) 317.74 4675 23.38 0.51 294.20 261.13 1.13
Structure
Lab 224.21 100,611 47.46 1.03 248.18 260.29 0.95
9:00AM-10:00AM 10:00AM-11:00AM 11:00AM-12:00AM 12:00PM-1:00PM 1:00PM-2:00PM 2:00PM-3:00PM 3:00PM-4:00PM 4:00PM-5:00PM
Light Intensity (Lux)
Rainy Winter Summer
Figure 6. Variation in light intensity in different seasons.

Energies 2019,12, 4539 12 of 31
3.2. Energy Auditing of the Lighting System
In practice, all of the light emitted by the lamp does not reach the work area. Some light is
absorbed by the luminaire, walls, floors, roof, etc. The illuminance measured in lumens/m
2
, i.e., lux,
indicates how much light, i.e., lumens, is available per square meter of the measurement plane. Target
luminous efficacy (lm/watts) of the light source is the ratio of lumens that can be made available at the
work plane under the best luminous efficacy of source, room reflectance, mounting height, and the
power consumption of the lamp circuit. Over time, the light output from the lamp is reduced, the room
surfaces become dull, and luminaries become dirty; hence, the light available on the work plane
deviates from the target value. The ratio of the actual luminous efficacy on the work plane and the
target luminous efficacy at the work plane is defined as the installed load efficacy ratio (ILER).
Table A1 (Appendix A) shows the variation in the coefficient of utilization when effective floor
cavity reflectance is taken as 20%. Tables 4–6show the readings of monthly data, which include the
average light intensity and its value in task and non-task areas, lumens, ILE and ILER, and the diversity
factor for each room of the sample building in rainy, winter, and summer seasons respectively.
Table 4. Energy efficiency evaluation of the lighting system of D-block in the rainy season.
Room No. Eav Lumens ILE ILER Eav task Eav, non-task DF
D106 493.56 7355 15.01 0.33 422.50 510.10 0.83
D104 1231.42 130,485 100.37 2.18 1259.57 786.50 1.60
D107 814.10 12,133 93.33 2.03 643.83 537.50 1.20
D108 585.79 8730 67.16 1.46 558.17 383.50 1.46
D109 693.79 10,340 68.93 1.46 629.83 286.50 2.20
D110 693.79 10,340 68.93 1.46 629.83 286.50 2.20
D101 108.10 7332 8.15 0.18 102.55 68.86 1.49
D102 254.02 17,248 21.56 0.47 242.36 189.72 1.28
D116 621.43 120,326 46.28 1.07 781.36 448.25 1.74
D115 805.68 156,002 60.00 1.30 1059.57 613.32 1.73
Research
Lab 236.95 7258 18.15 0.39 219.40 194.67 1.13
D201 1511.56 149,848 146.91 32.65 1361.07 247.25 5.50
D202 988.63 98,007 96.09 2.09 1361.29 825.50 1.65
D203 2478.17 125,252 379.55 8.25 2665.57 650.22 4.10
D204 1797.55 90,852 275.31 5.99 2399.93 1433.75 1.67
Cabin
(S-Lab) 317.74 4675 23.38 0.51 294.20 261.13 1.13
Structure
Lab 224.21 100,611 47.46 1.03 248.18 260.29 0.95
D120 108.43 1310 13.10 0.28 104.10 105.11 0.99
Cabin-1 1093.18 40,619 270.79 5.89 1142.83 2231.20 0.51
D112 611.71 51,450 64.31 1.39 707.36 585.20 1.21
Trans. Lab 291.82 38,871 64.79 1.41 253.36 263.57 0.96
D117 1036.37 35,897 358.97 7.80 959.60 1103.67 0.87
D114 358.99 5322 88.70 1.93 338.50 363.20 0.93

Energies 2019,12, 4539 13 of 31
Table 4. Cont.
Room No. Eav Lumens ILE ILER Eav task Eav, non-task DF
D113 358.99 5321 88.69 1.93 338.50 376.30 0.90
D111 440.86 15,238 126.99 2.76 616.07 604.67 1.02
D123 141.05 1704 17.04 0.37 125.00 142.50 0.88
Cabin-2 788.62 12,279 55.81 1.21 827.67 1766.50 0.47
D103 30.24 365 3.65 0.08 28.33 36.20 0.79
D118 252.07 16,155 16.48 0.36 216.14 236.25 0.91
Stair Room
181.87 2676 6.69 0.15 153.50 107.50 1.43
D119 273.89 3309 11.03 0.24 250.00 169.10 1.48
D121 309.74 4524 11.31 0.25 272.33 223.20 1.22
Corridor 1 620.57 281,189 562.38 12.23 566.43 448.25 1.26
Corridor 2 527.26 60,937 406.25 8.83 522.86 214.25 2.44
Table 5. Energy efficiency evaluation of the lighting system of D-block in the winter season.
Room No. Eav Lumens ILE ILER Eav task Eav, non-task DF
D106 70.88 1056 2.16 0.05 57.50 90.10 0.64
D104 492.96 52,235 40.18 0.87 417.57 592.50 0.71
D107 80.19 1195 9.19 0.19 66.83 96.50 0.69
D108 67.51 1005 7.74 0.17 66.50 50.50 1.32
D109 215.73 3215 21.43 0.47 210.33 168.12 1.25
D110 215.73 3215 21.43 0.47 210.33 168.12 1.25
D101 118.20 8025 8.92 0.19 91.36 137.86 0.66
D102 132.72 9012 11.27 0.24 121.18 125.57 0.97
D116 749.22 145,069 55.79 1.21 688.14 713.25 0.96
D115 518.52 100,399 38.62 0.84 463.64 537.75 0.86
Research
Lab 232.47 7121 17.80 0.39 239.40 175.11 1.37
D201 807.66 80,067 78.49 17.44 783.93 621.50 1.26
D202 910.14 90,226 88.46 1.92 790.07 1027.10 0.77
D203 257.70 13,024 39.47 0.86 241.29 229.25 1.05
D204 800.10 40,439 122.54 2.66 734.64 762.50 0.96
Cabin
(S-lab) 365.18 5375 26.88 0.58 338.13 325.67 1.04
Structure
Lab 958.98 430,335 202.99 4.41 1032.7 660.43 1.56
D120 105.17 1270 12.71 0.28 96.50 100.12 0.97
Cabin-1 96.26 3576 23.84 0.52 98 62.50 1.57
D112 270.06 22,714 28.39 0.62 228.27 284.29 0.80
Trans. Lab 737.52 98,241 163.74 3.56 587 833.57 0.70

Energies 2019,12, 4539 14 of 31
Table 5. Cont.
Room No. Eav Lumens ILE ILER Eav task Eav, non-task DF
D117 602.78 20,878 208.79 4.54 506.40 644.33 0.79
D114 83.70 1240 20.68 0.45 67.50 87.50 0.77
D113 81.95 1214 20.24 0.44 68.25 83.50 0.82
D111 220.86 7634 63.62 1.38 197.67 181.12 1.09
D123 116.10 1402 14.03 0.31 103.67 119.21 0.87
Cabin-2 71.82 1118 5.08 0.11 59.50 87.50 0.68
D103 166.73 2014 20.15 0.44 138 203.50 0.68
D118 177.84 11,397 11.63 0.25 158.29 187.22 0.85
Stair Room
1004.4 14,779 36.95 0.80 928.67 934.21 0.99
D119 162.81 1967 4.92 0.11 146.33 164.10 0.89
D121 131.35 1918 4.79 0.10 120.83 112.11 1.11
Corridor 1 527.31 238,933 477.87 10.39 355.57 314.75 1.13
Corridor 2 83.70 9673 64.49 1.40 72.50 74.25 0.98
Table 6. Energy efficiency evaluation of the lighting system of D-block in the summer season.
Room No. Eav Lumens ILE ILER Eav, task Eav, non-task DF
D106 868.19 12,939 26.41 0.57 764.67 921.50 0.82
D104 1601.4 169,692 130.53 2.84 1273.21 1735.11 0.73
D107 1616.1 24,085 185.28 4.02 1562.33 1724.50 0.91
D108 224.42 3344 25.73 0.56 216.67 326.12 0.66
D109 475.85 7091 47.28 1.03 508.33 654.50 0.78
D110 475.85 7091 47.28 1.03 508.33 654.50 0.78
D101 1139.18 77,343 85.94 1.87 1245.73 1630.57 0.76
D102 618.84 42,021 52.53 1.14 653.64 555.86 1.18
D116 2723.33 527,311 202.81 4.41 2314.64 2639.21 0.88
D115 1840.32 356,337 137.05 2.98 2110.07 2286.20 0.92
Research
Lab 1055.10 32,322 80.81 1.76 977 928.67 1.05
D201 1469.66 145,694 142.84 31.74 964.64 1100.10 0.88
D202 1383.69 137,172 134.48 2.92 847 654.75 1.29
D203 1017.36 51,419 155.82 3.38 885.21 610.75 1.45
D204 1127.52 56,987 172.69 3.75 1530.64 1261.25 1.21
Cabin
(S-lab) 317.74 376 1.88 0.04 281.88 61.11 4.61
Structure
Lab 1419.55 637,013 300.47 6.53 1350.09 1412.14 0.96
D120 830.30 10,033 100.33 2.18 739.67 676.50 1.09
Cabin-1 400.25 14,872 99.15 2.16 349.67 383.12 0.91
D112 1251.72 105,280 131.60 2.86 1074.64 1091.14 0.98

Energies 2019,12, 4539 15 of 31
Table 6. Cont.
Room No. Eav Lumens ILE ILER Eav, task Eav, non-task DF
Trans. Lab 6978.7 929,609 1549.30 33.68 3600 5730.29 0.63
D117 1378.5 47,748 477.48 10.38 1276.4 659.67 1.93
D114 510.41 7567 126.12 2.74 416.25 518.25 0.80
D113 472.82 7008 116.81 2.54 371 537.25 0.69
D111 1234.8 42,683 355.69 7.73 1209.27 1208.10 1.00
D123 207.14 2503 25.03 0.54 203 153.50 1.32
Cabin-2 304.34 4738 21.54 0.47 293.33 353.50 0.83
D103 308.88 3732 37.32 0.81 263.33 172.50 1.53
D118 1187.3 76,096 77.65 1.69 1047.57 811.25 1.29
Stair Room
1235.5 18,180 45.45 0.99 1034 772.50 1.34
D119 989.71 11,959 29.89 0.65 889.33 944.50 0.94
D121 1070.49 15,638 39.09 0.85 970 809.50 1.19
Corridor 1 1423.66 645,082 1290.17 28.05 1199.71 86.25 1.40
Corridor 2 893.60 103,277 688.52 14.97 1304.64 382.50 3.41
From the results shown in Tables 4–6, it can be observed that the value of average illuminance is
different in different seasons for the same room. It further tends to vary the ILE, ILER, and DF. A lower
value of ILER indicates that the luminous efficacy of the installed luminaires is poor, whereas a low
value of DF is the indication of uniform distribution of light intensity in the specified area irrespective
of the task and non-task areas.
Table 7shows the various parameters of room index (RI) and room cavity ratio (RCR), as well as
IMPs for task and non-task areas, which were obtained based upon the size of the room. Based upon the
data available in Tables 4–6the number of luminaires (N
L
) was calculated with a poor value of average
light intensity so that the minimum illuminance could be maintained even under the worst conditions.
Table 7. Room index and cavity ratio, IMP, and the number of light sources.
Room No. RI RCR IMPtask IMPnon-task NL
D106 0.70 0.47 5.6 2.4 5
D104 1.89 0.18 14.4 3.6 32
D107 0.70 0.47 5.6 2.4 5
D108 0.70 0.47 5.6 2.4 5
D109 0.70 0.47 5.6 2.4 5
D110 0.70 0.47 5.6 2.4 5
D101 1.48 0.23 10.8 7.2 20
D102 1.48 0.19 10.8 7.2 20
D116 1.92 0.19 13.5 4.5 58
D115 1.92 0.41 13.5 4.5 58
Research Lab 0.82 0.20 4.8 3.2 9

Energies 2019,12, 4539 16 of 31
Table 7. Cont.
Room No. RI RCR IMPtask IMPnon-task NL
D201 1.73 0.20 13.5 4.5 30
D202 1.73 0.28 13.5 4.5 30
D203 1.20 0.28 13.5 4.5 15
D204 1.20 0.73 13.5 4.5 15
Cabin (S-lab) 0.51 0.73 4.8 3.2 5
Structure Lab 1.59 0.25 10.8 7.2 136
D120 0.41 0.93 5.6 2.4 4
Cabin-1 0.66 0.58 5.6 2.4 12
D112 1.68 0.20 10.8 7.2 25
Trans. Lab 1.41 0.26 10.8 7.2 40
D117 0.61 0.63 5.2 2.8 11
D114 0.41 0.94 4 4 5
D113 0.41 0.94 4 4 5
D111 1.05 0.32 15.3 2.7 11
D123 0.41 0.93 5.6 2.4 4
Cabin-2 0.71 0.46 5.6 2.4 5
D103 0.41 0.93 5.6 2.4 4
D118 1.39 0.24 14.4 3.6 19
Stair Room 0.51 0.73 5.6 2.4 5
D119 0.41 0.93 5.6 2.4 4
D121 0.67 0.51 5.6 2.4 5
Corridor 1 1.28 0.26 13.5 4.5 10
Corridor 2 1.20 0.31 13.5 4.5 3
3.3. Energy Auditing of Ventilation System
Linearization of CFM for Fan Requirement
Table 8shows the recommended level of cubic feet per minute (CFM) of air circulation in a specified
area. These data were available for the limited size of the rooms, whereas, in the analysis, the room size
was a little big and, therefore, a linear aggression function was derived using Equations (20)–(22). These
data give information about the number of fans that should be installed as per the recommendation in
a specified area. The linearized function is derived as
ˆ
y=mx +b(20)
where mrepresents the slope of the line, and brepresents the y-intercept (y-value for which xis 0).
m=nPxy −(Px)( y)
nP(x2)−n(Px)2. (21)
b=Py
n−mPx
n. (22)
In Equation (20),
ˆ
y
is the predicted value of y. The linear regression line is the representation of
Equation (20) in which mand bare obtained using Equations (21) and (22), and, from the calculation,

Energies 2019,12, 4539 17 of 31
their values were found to be 23.72 and 2841.94, respectively. Figure 7shows the graph between the
area of a room (in square feet) and the CFM (cubic feet/min).
Table 8. Mathematical calculations for the analysis of fans.
S. No. Room Area (x)CFM
Recommended
Average CFM
(y)x×y(x2)
Linearized Value of
New CFM
^
y=23.72x+2841.94
1 36 3000–4500 3750 135,000 1296 3695.86
2 100 4000–5500 4750 475,000 10,000 5213.94
3 144 6200–7500 6850 986,400 20,736 6257.62
4 225 7000–9000 8000 1,800,000 50,625 8178.94
Sum 505 - 23,350 3,396,400 82,657 -
Energies 2019, 12, 4539 17 of 31
Figure 7. Cubic feet per minute (CFM) regression curve.
Table 9 shows the room area and volume for the calculation of CFM required in a specified area,
which further helps in the calculation of the number of fans. Here, it can be observed that the number
of fans which should be installed in each room was different, which was obtained using linear
regression. As a result, two rooms having the same area would also have the same number of fans
according to this criterion.
Table 9. Detailed information and data analysis of fans.
Room No. Area Volume Fan Size (37 to 48 inches)
ft2 m
2 ft3 M
3 CFM Fan
D106 160.34 14.89 1349.51 38.23 6645.19 1.58
D104 1140 105.91 10,069.62 285.21 29,882.74 7.11
D107 160.34 14.89 1349.51 38.23 6645.19 1.58
D108 160.34 14.89 1349.51 38.23 6645.19 1.58
D109 160.34 14.89 1349.57 38.23 6645.19 1.58
D110 160.34 14.89 1349.57 38.23 6645.19 1.58
D101 730.43 67.86 6634.49 187.91 20,167.72 4.80
D102 730.53 67.87 6635.37 187.94 20,170.03 4.80
D116 2083.13 193.53 24,649.62 698.17 52,253.67 12.44
D115 2083.13 193.53 24,649.62 698.17 52,253.67 12.44
Research Lab 329.56 30.62 2966.04 84.01 10,659.10 2.54
D201 1066.53 99.08 10,043.51 284.47 28,140.03 6.70
D202 1066.53 99.08 10,043.51 284.47 28,140.03 6.70
D203 543.76 50.52 5120.56 145.03 15,739.85 3.75
D204 543.76 50.52 5120.56 145.03 15,739.85 3.75
Cabin (S-lab) 158.31 14.71 1965.76 55.68 6597.09 2
Structure Lab 4827.75 448.51 77,646.15 2199.24 117,356.20 27.94
D120 130 12.077 1798.29 50.93 5925.54 1.41
Cabin-1 399.75 37.14 5529.74 156.62 12,324.01 2.93
D112 904.88 84.06 8068.41 228.53 24,305.58 5.79
Trans. Lab 1433.08 133.14 18,510.54 524.29 36,834.63 8.77
D117 372.65 34.62 5310.19 150.41 11,681.08 2.78
D114 159.49 14.82 2299.48 65.13 6625.23 1.58
D113 159.47 14.82 2299.11 65.12 6624.62 1.58
D111 371.87 34.55 3408.77 96.55 11,662.67 2.78
D123 130 12.08 1798.29 50.93 5925.54 1.41
3000
4000
5000
6000
7000
8000
9000
1234
CFM (Cubic feet per minute)
Room area (square feet)
Linearized value of new CFM y=23.72x+2841.94
Figure 7. Cubic feet per minute (CFM) regression curve.
Table 9shows the room area and volume for the calculation of CFM required in a specified area,
which further helps in the calculation of the number of fans. Here, it can be observed that the number of
fans which should be installed in each room was different, which was obtained using linear regression.
As a result, two rooms having the same area would also have the same number of fans according to
this criterion.
Table 9. Detailed information and data analysis of fans.
Room No. Area Volume Fan Size (37 to 48 inches)
ft2m2ft3M3CFM Fan
D106 160.34 14.89 1349.51 38.23 6645.19 1.58
D104 1140 105.91 10,069.62 285.21 29,882.74 7.11
D107 160.34 14.89 1349.51 38.23 6645.19 1.58
D108 160.34 14.89 1349.51 38.23 6645.19 1.58
D109 160.34 14.89 1349.57 38.23 6645.19 1.58
D110 160.34 14.89 1349.57 38.23 6645.19 1.58
D101 730.43 67.86 6634.49 187.91 20,167.72 4.80

Energies 2019,12, 4539 18 of 31
Table 9. Cont.
Room No. Area Volume Fan Size (37 to 48 inches)
ft2m2ft3M3CFM Fan
D102 730.53 67.87 6635.37 187.94 20,170.03 4.80
D116 2083.13 193.53 24,649.62 698.17 52,253.67 12.44
D115 2083.13 193.53 24,649.62 698.17 52,253.67 12.44
Research Lab 329.56 30.62 2966.04 84.01 10,659.10 2.54
D201 1066.53 99.08 10,043.51 284.47 28,140.03 6.70
D202 1066.53 99.08 10,043.51 284.47 28,140.03 6.70
D203 543.76 50.52 5120.56 145.03 15,739.85 3.75
D204 543.76 50.52 5120.56 145.03 15,739.85 3.75
Cabin (S-lab) 158.31 14.71 1965.76 55.68 6597.09 2
Structure Lab 4827.75 448.51 77,646.15 2199.24 117,356.20 27.94
D120 130 12.077 1798.29 50.93 5925.54 1.41
Cabin-1 399.75 37.14 5529.74 156.62 12,324.01 2.93
D112 904.88 84.06 8068.41 228.53 24,305.58 5.79
Trans. Lab 1433.08 133.14 18,510.54 524.29 36,834.63 8.77
D117 372.65 34.62 5310.19 150.41 11,681.08 2.78
D114 159.49 14.82 2299.48 65.13 6625.23 1.58
D113 159.47 14.82 2299.11 65.12 6624.62 1.58
D111 371.87 34.55 3408.77 96.55 11,662.67 2.78
D123 130 12.08 1798.29 50.93 5925.54 1.41
Cabin-2 167.51 15.56 1409.96 39.94 6815.37 1.62
D103 130 12.08 1797.90 50.92 5925.54 0.68
D118 689.49 64.06 6435.10 182.27 19,196.87 4.57
Stair Room 158.31 14.71 1965.76 55.68 6597.09 1.57
D119 130 12.08 1798.29 50.93 5925.54 1.41
D121 157.17 14.60 1480.05 41.92 6569.95 1.56
3.4. Energy Auditing of the Air Conditioning System
Table 10 shows the auditing details of the AC installation. Here, the rooms equipped with ACs
were taken into consideration, whereas the other rooms in which ACs are not installed, were ignored.

Energies 2019,12, 4539 19 of 31
Table 10. Detailed information about the AC system.
Room No.
Recommended BTU/kW in the Different
Room [27]
AC Size in Tons Using Different Methods Based on
Area Volume
BTU Recommended kW BTU [28] Criterion 1 [29] Criterion 2 [29] Criterion 3 [29]
D106 6000 1.76 4008.49 0.33 1.27 1.35
D104 17,700 5.19 28,500.00 2.38 3.38 10.07
D101 15,000 4.40 18,260.73 1.52 2.70 6.63
D102 15,000 4.40 18,263.17 1.52 2.70 6.64
Research Lab 7250 2.12 8239.00 0.69 1.82 2.97
D201 17,700 5.19 26,663.24 2.22 3.26 10.04
D202 17,700 5.19 26,663.24 2.22 3.27 10.04
D203 10,500 3.08 13,593.92 1.13 2.33 5.12
D204 10,500 3.08 13,593.92 1.13 2.33 5.12
Cabin (S-lab) 6000 1.76 3957.79 0.33 1.26 1.97
D112 15,000 4.40 22,621.88 1.89 3.01 8.07
Cabin-2 6000 1.76 4187.84 0.35 1.29 1.41
D118 12,500 3.66 17,237.49 1.44 2.63 6.44
D119 5000 1.47 3250.00 0.27 1.14 1.79

Energies 2019,12, 4539 20 of 31
For AC recommendations, four different criteria were studied as shown in Table 10. From the
analysis, it can be observed that the number or size of ACs was different in each specified area. In
the second column of Table 10, the BTU recommendation is shown, which was decided based upon
the room size. The equivalent representation of the BTU was also converted into kW of its rating
for comparison with other criteria. Figure 8shows the physical layout of the power components in
a sample room in the commercial building. Before auditing, the power components were placed at
random, whereas, after auditing, they were placed uniformly such that the ILER, DF, CFM, and BTU
requirements could be maximized in the task area.
Energies 2019, 12, 4539 20 of 31
For AC recommendations, four different criteria were studied as shown in Table 10. From the
analysis, it can be observed that the number or size of ACs was different in each specified area. In the
second column of Table 10, the BTU recommendation is shown, which was decided based upon the
room size. The equivalent representation of the BTU was also converted into kW of its rating for
comparison with other criteria. Figure 8 shows the physical layout of the power components in a
sample room in the commercial building. Before auditing, the power components were placed at
random, whereas, after auditing, they were placed uniformly such that the ILER, DF, CFM, and BTU
requirements could be maximized in the task area.
AC-1
2T(split)
AC-2
2T(split)
AC-3
1.5T(split)
D104
AC-1
2T(split)
AC-2
2T(split )
Entrance
(a) (b) (c)
Figure 8. Sample room (D104): (a) actual image; (b) layout before auditing; (c) layout after auditing.
4. Energy Efficiency with DSM
DSM is a cost-effective means to reduce peak load demand by reshaping the load profile [10].
However, in commercial and residential buildings, load shifting is rare; rather, it needs to be
managed according to the requirement. This allows operating the number of power components
based on the framework designed for their operating schedule. The operating schedule of various
components may vary with application and their recommendation for specific purposes. In this
scenario, operating schedules applicable under some circumstances, at the same time, may be
different from other systems of the same size. Therefore, the implementation of DSM schemes should
have the flexibility to meet the objective of power-saving and, hence, to improve energy efficiency
without affecting the comfort level [12].
The objective function for DSM was formulated for energy-saving to minimize the cost of the
customer electricity bill. Therefore, the DSM problem for energy-saving is described in Equation (23).
𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒
𝑓

𝑓
 =𝑃× 𝑇



 × 𝐸𝑃 (23)
In the proposed work, the load demand (P
rt
) was calculated for each room; therefore, r varied
from one to NR, i.e., the number of rooms in a sample building, and energy efficiency was evaluated
for 𝑇, i.e., the duration in a day where t varies from 1–8. Conversely, 𝐸𝑃 is the energy price at the
time t, which in this case was the same for all loads; however, in practice, it may be different in the
case of DR-based DSM approaches. The objective function was subject to the following constraints:
1. Energy consumption: The new energy consumption (EC) should be less than the existing
consumption, which is represented in Equation (24).
𝐸𝐶,  𝐸𝐶,. (24)
2. Illuminance level: The light intensity of the light source is different, and it is also affected by the
power rating. Therefore, the illuminance level should be maintained between the minimum and
the maximum level, which is represented in Equation (25).
Figure 8. Sample room (D104): (a) actual image; (b) layout before auditing; (c) layout after auditing.
4. Energy Efficiency with DSM
DSM is a cost-effective means to reduce peak load demand by reshaping the load profile [
10
].
However, in commercial and residential buildings, load shifting is rare; rather, it needs to be managed
according to the requirement. This allows operating the number of power components based on the
framework designed for their operating schedule. The operating schedule of various components may
vary with application and their recommendation for specific purposes. In this scenario, operating
schedules applicable under some circumstances, at the same time, may be different from other systems
of the same size. Therefore, the implementation of DSM schemes should have the flexibility to meet
the objective of power-saving and, hence, to improve energy efficiency without affecting the comfort
level [12].
The objective function for DSM was formulated for energy-saving to minimize the cost of the
customer electricity bill. Therefore, the DSM problem for energy-saving is described in Equation (23).









Minimize fx
fx=NR
P
r=1
8
P
t=1
Prt ×Trt ×EPrt (23)
In the proposed work, the load demand (P
rt
) was calculated for each room; therefore, rvaried
from one to NR, i.e., the number of rooms in a sample building, and energy efficiency was evaluated
for
Trt
, i.e., the duration in a day where tvaries from 1–8. Conversely,
EPrt
is the energy price at the
time t, which in this case was the same for all loads; however, in practice, it may be different in the case
of DR-based DSM approaches. The objective function was subject to the following constraints:
1.
Energy consumption: The new energy consumption (EC) should be less than the existing
consumption, which is represented in Equation (24).
XECnew,rt ≤XECold,rt.(24)

Energies 2019,12, 4539 21 of 31
2.
Illuminance level: The light intensity of the light source is different, and it is also affected by the
power rating. Therefore, the illuminance level should be maintained between the minimum and
the maximum level, which is represented in Equation (25).
Eavg,min <Eavg <Eavg,max. (25)
3.
Persons involved: The number of persons (N
p
) involved in a specified area varies throughout
the day. Therefore, the minimum and maximum numbers need to be defined before the DSM
implementation, which is represented in Equation (26).
Np, min <Np<Np, max.(26)
4.
Temperature and humidity: The number of fans and ACs to be operated can be affected by
the surrounding temperature (ST) and the humidity (H). However, the humidity level in both
winter and rainy seasons is usually high; however, the operation of fans and ACs can only be
affected on rainy days due to high temperatures. Therefore, before imposing the constraint
of humidity, the surrounding temperature needs to be considered, which is represented in
Equations (27) and (28).
STmin <ST <STmax.(27)
Hmin <H<Hmax.(28)
Equations (24) and (25) represent the technical constraints where energy consumption affects
the objective function, whereas Equation (26) represents the social constraint, which mainly depends
upon the human being involved in a specified area. However, Equations (27) and (28) represent the
environmental constraints, as temperature varies from severe cold to severe heat with different levels
of humidity.
4.1. Operating Scenario for DSM
In commercial and residential buildings, power consumption mainly depends upon the number
of persons involved, surrounding temperature and humidity, and the luminous intensity due to
sunlight [
23
]. Therefore, the energy data of three months in a year were collected under three different
seasonal changes, i.e., January (severe cold), April (moderate), and August (severe heat), as described in
Section 3. This was done to show the significant variation in load demand under moderate, comfortable,
and severe environmental conditions. The energy data under these three conditions were averaged,
and the operating scenario for DSM schemes was developed, as shown in Table 11, for the operation of
power components before energy auditing and after energy auditing.
Table 11. The operating scenario for demand-side management (DSM).
Components/
Schedule 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Strength 0 5 10 20 30 40 50 60 70 80 90
100 110 120
Light 0 4 8 12 16 20 24 28 32 36 40 44 48 52
Fan 0 2 3 4 5 6 8 10 11 12 13 14 15 16
AC 01111122233344
The operating scenario, described in Table 11, represents the initial solution for the maximum
number of lights, fans, and ACs that can be operated for the predefined strength, i.e., number of
persons in a specified area. However, during operation, the above scenario can be adjusted with a
minimum step size of ±1 to limit the violation of the constraints.

Energies 2019,12, 4539 22 of 31
4.2. Proposed DSM-Based Algorithm and its Flowchart
In Section 3, strategic auditing was presented for different seasons, and the results in Tables 4–10
show that there is significant variation in different auditing parameters. Therefore, auditing before
DSM does not only tell us about the immediate scope of energy-saving; rather, it helps to develop a
framework of the operating scenario and constraints in the DSM approach. Based upon the findings
from strategic auditing and the operating scenario for DSM, an algorithm was developed, and Figure 9
shows the flowchart of the proposed DSM approach.
Energies 2019, 12, 4539 22 of 31
from strategic auditing and the operating scenario for DSM, an algorithm was developed, and Figure
9 shows the flowchart of the proposed DSM approach.
Figure 9. The flowchart of the proposed demand-side management (DSM) algorithm.
The steps involved in the proposed DSM-based algorithm are described below.
1. Read the actual data before auditing the sample building, including the size of the rooms,
number of persons involved in each room, number of power components and their
specifications, types of rooms, and the time of operation.
2. Read the measurement data for energy consumption and the surrounding environment in
different seasons for the analysis.
3. Calculate the various auditing parameters using Equations (1)–(19).
4. Identify the recommendations of strategic auditing and enlist the number of power
components in each room with their power ratings.
5. Enlist the above data before auditing and after auditing separately.
6. Set the limits for operating constraints, the maximum number of rooms (Rmax), and working
hours (hmax).
7. Initialize the operating scenario for fans, lights, and ACs, as shown in Table 11.
8. Initialize the number of rooms and set R = 1.
9. Initialize the working hours and set h = 1.
10. Define the maximum and the minimum number of components to be operated in a specific
room.
11. Calculate the number of lights, fans, and ACs to be operating for a given scenario.
Figure 9. The flowchart of the proposed demand-side management (DSM) algorithm.
The steps involved in the proposed DSM-based algorithm are described below.
1.
Read the actual data before auditing the sample building, including the size of the rooms, number
of persons involved in each room, number of power components and their specifications, types
of rooms, and the time of operation.
2.
Read the measurement data for energy consumption and the surrounding environment in different
seasons for the analysis.
3. Calculate the various auditing parameters using Equations (1)–(19).
4.
Identify the recommendations of strategic auditing and enlist the number of power components
in each room with their power ratings.
5. Enlist the above data before auditing and after auditing separately.
6.
Set the limits for operating constraints, the maximum number of rooms (R
max
), and working
hours (hmax).

Energies 2019,12, 4539 23 of 31
7. Initialize the operating scenario for fans, lights, and ACs, as shown in Table 11.
8. Initialize the number of rooms and set R=1.
9. Initialize the working hours and set h=1.
10.
Define the maximum and the minimum number of components to be operated in a specific room.
11.
Calculate the number of lights, fans, and ACs to be operating for a given scenario.
12.
Compare the components in steps 10 and 11; then, impose the conditions as per step 10 for
maximum and minimum limits accordingly.
13.
Check operating constraints; if violated, adjust the operating scenario and repeat steps 9–12;
otherwise, go to the next step.
14.
Calculate the energy consumption due to fans, lights, and ACs, as well as the energy-saving for
this hour.
15.
Check for h≤hmax; if yes, set h=h+1and repeat steps 10–14; otherwise, go to the next step.
16.
Check for R≤Rmax; if yes, set R=R+1and repeat steps 9–15; otherwise, go to the next step.
17.
Calculate cumulative energy-saving in a day and a year using Equation (23) and compare with
the measured value.
18.
Calculate the cost of saving.
19.
Stop.
5. Result Analysis for DSM Before Auditing
The energy efficiency was evaluated before and after auditing. Tables 12–14 show the results for
the DSM scheme before auditing for three different seasons in a year. The various parameters like
strength ratio (SR), temperature in
◦
C, relative humidity, luminous intensity (LI), energy consumption
of light (ECLT), energy consumption of ACs (ECAT), energy consumption of fans (ECFT), total energy
consumption (ECTT) which is the sum of ECLT, ECAT and ECFT, energy consumption with DSM
(ECWDSM) and the savings as the difference of ECTT and ECWDSM.
Table 12. DSM before auditing in the rainy season.
Parameter/Time
(a.m./p.m.)
9:00–10:00
a.m.
10:00–11:00
a.m.
11:00 a.m.–12:00
p.m.
12:00–1:00
p.m.
1:00–2:00
p.m.
2:00–3:00
p.m.
3:00–4:00
p.m.
4:00–5:00
p.m.
SR 0.4 0.4 0.5 0.4 0.3 0.6 0.3 0.3
Temp (◦C) 27.95 29.00 29.75 30.875 30.875 31.17 30.83 29.88
Humidity (%) 79.29 80.67 77.83 71.25 72.08 70.54 72.04 75.38
LI (Lux) 18,378 21,245 22,926 26,550 25,945 26,424 24,033 21,999
ECLT (kWh) 11.85 11.90 12.65 11.55 11.55 11.75 11.75 11.15
ECAT (kWh) 11.40 36.48 11.40 6.84 13.68 13.68 13.68 11.40
ECFT (kWh) 3.84 3.96 3.96 4.92 5.34 5.46 5.28 3.90
ECTT (kWh) 27.09 52.34 28.01 23.31 30.57 30.89 30.71 26.45
ECWDSM (kWh) 35.67 33.25 38.54 31.96 31.00 37.58 35.08 37.63
Savings (kWh) 8.58 −19.09 10.53 8.65 0.43 6.69 4.37 11.18
The cumulative energy-saving, shown in the last row of Tables 12–14, in rainy and summer
seasons was found to be 31.34 and 36.67 kWh, respectively, whereas, in the winter, it was
−
29.24 kWh.
This issue indicates that, with DSM, the energy consumption reduced in rainy and summer seasons,
whereas it increased in the winter season. This is because, in the winter season, the weather conditions
are very cold, and the maximum light source requires operation to maintain the recommended level of
illuminance. Also, at the same time there is no scope of energy-saving due to fans and ACs because
they remain OFF in the winter season.

Energies 2019,12, 4539 24 of 31
Table 13. DSM before auditing in the winter season.
Parameter/Time
(a.m./p.m.)
9:00–10:00
a.m.
10:00–11:00
a.m.
11:00 a.m.–12:00
p.m.
12:00–1:00
p.m.
1:00–2:00
p.m.
2:00–3:00
p.m.
3:00–4:00
p.m.
4:00–5:00
p.m.
SR 0.4 0.3 0.2 0.1 0.0 0.2 0.3 0.0
Temp (◦C) 9.58 11.50 13.75 15.25 17.21 18.42 18.42 18.46
Humidity (%) 85.21 77.54 71.25 66.88 61.08 58.04 57.54 58.38
LI (Lux) 1160 16,717 29,437 34,026 43,801 52,450 52,602 46,745
ECLT (kWh) 15.93 15.80 14.90 12.55 12.50 11.95 11.15 11.55
ECAT (kWh) 00 0 00000
ECFT (kWh) 00 0 00000
ECTT (kWh) 15.93 15.80 14.90 12.55 12.50 11.95 11.15 11.55
ECWDSM (kWh) 10.83 10.42 10.83 9.58 9.58 9.17 8.75 7.92
Savings (kWh) −5.09 −5.38 −4.07 −2.97 −2.92 −2.78 −2.40 −3.63
Table 14. DSM before auditing in the summer season.
Parameter/Time
(a.m./p.m.)
9:00–10:00
a.m.
10:00–11:00
a.m.
11:00 a.m.–12:00
p.m.
12:00–1:00
p.m.
1:00–2:00
p.m.
2:00–3:00
p.m.
3:00–4:00
p.m.
4:00–5:00
p.m.
SR 0.2 0.3 0.4 0.1 0.1 0.2 0.3 0.0
Temp (◦C) 29.78 31.96 33.35 34.65 36.26 37.39 37.78 37.22
Humidity (%) 47.04 43.00 37.30 33.13 28.39 26.91 25.22 24.96
LI (Lux) 31,926 36,961 43,475 5477 67,048 73,032 74,209 70,848
ECLT (kWh) 12.20 12.60 12.50 11.75 0 0 0 0
ECAT (kWh) 13.68 13.68 11.40 13.68 13.68 11.40 13.68 11.40
ECFT (kWh) 3.84 5.34 5.10 5.46 5.28 5.28 5.28 5.34
ECTT (kWh) 29.72 31.62 29.00 30.89 18.96 16.68 18.96 16.74
ECWDSM (kWh) 27.91 28.32 29.32 28.32 29.77 32.55 27.55 25.50
Savings (kWh) −1.81 −3.30 0.32 −2.57 10.81 15.87 8.59 8.76
Figure 10 represents the variation of hourly energy-saving in different seasons, as shown in the
last row of Tables 12–14. Here, it can be noted that the energy consumption reduced from 9:00 a.m. to
10:00 a.m. in the rainy season, whereas it increased in the summer and winter seasons, even after DSM.
Furthermore, the energy-saving (in blue color) in different hours in the rainy season was found to be
different. Also, from 10:00 a.m. to 11:00 a.m., the energy-saving was found to be negative, which means
that consumption is higher even after DSM for this duration. Similarly, in summer, the energy-saving,
shown in gray color, varied differently throughout the day and, the energy-saving (in orange color) in
winter was negative concerning the consumption before DSM. However, the cumulative saving in
three different seasons was found to be positive for the proposed DSM approach.
Energies 2019, 12, 4539 24 of 31
ECWDSM
(kWh) 10.83 10.42 10.83 9.58 9.58 9.17 8.75 7.92
Savings (kWh) −5.09 −5.38 −4.07 −2.97 −2.92 −2.78 −2.40 −3.63
The cumulative energy-saving, shown in the last row of Tables 12–14, in rainy and summer
seasons was found to be 31.34 and 36.67 kWh, respectively, whereas, in the winter, it was −29.24 kWh.
This issue indicates that, with DSM, the energy consumption reduced in rainy and summer seasons,
whereas it increased in the winter season. This is because, in the winter season, the weather
conditions are very cold, and the maximum light source requires operation to maintain the
recommended level of illuminance. Also, at the same time there is no scope of energy-saving due to
fans and ACs because they remain OFF in the winter season.
Table 14. DSM before auditing in the summer season.
Parameter/
Time
(a.m./p.m.)
9:00–
10:00
a.m.
10:00–
11:00
a.m.
11:00
a.m.–
12:00
p.m.
12:00–
1:00
p.m.
1:00–
2:00
p.m.
2:00–
3:00
p.m.
3:00–
4:00
p.m.
4:00–
5:00
p.m.
SR 0.2 0.3 0.4 0.1 0.1 0.2 0.3 0.0
Temp (°C) 29.78 31.96 33.35 34.65 36.26 37.39 37.78 37.22
Humidity (%) 47.04 43.00 37.30 33.13 28.39 26.91 25.22 24.96
LI (Lux) 31,926 36,961 43,475 5477 67,048 73,032 74,209 70,848
ECLT (kWh) 12.20 12.60 12.50 11.75 0 0 0 0
ECAT (kWh) 13.68 13.68 11.40 13.68 13.68 11.40 13.68 11.40
ECFT (kWh) 3.84 5.34 5.10 5.46 5.28 5.28 5.28 5.34
ECTT (kWh) 29.72 31.62 29.00 30.89 18.96 16.68 18.96 16.74
ECWDSM
(kWh) 27.91 28.32 29.32 28.32 29.77 32.55 27.55 25.50
Savings (kWh) −1.81 −3.30 0.32 −2.57 10.81 15.87 8.59 8.76
Figure 10 represents the variation of hourly energy-saving in different seasons, as shown in the
last row of Tables 12–14. Here, it can be noted that the energy consumption reduced from 9:00 a.m.
to 10:00 a.m. in the rainy season, whereas it increased in the summer and winter seasons, even after
DSM. Furthermore, the energy-saving (in blue color) in different hours in the rainy season was found
to be different. Also, from 10:00 a.m. to 11:00 a.m., the energy-saving was found to be negative, which
means that consumption is higher even after DSM for this duration. Similarly, in summer, the energy-
saving, shown in gray color, varied differently throughout the day and, the energy-saving (in orange
color) in winter was negative concerning the consumption before DSM. However, the cumulative
saving in three different seasons was found to be positive for the proposed DSM approach.
-20
-15
-10
-5
0
5
10
15
20
9:00-10:00AM 10:00-11:00AM 11:00-12:00NN 12:00-1:00PM 1:00:200PM 2:00:300PM 3:00:400PM 4:00:500PM
Hourly savin (kWh)
Rainy Winter Summer
Figure 10. Savings in different seasons before auditing.

Energies 2019,12, 4539 25 of 31
6. Results and Discussion for DSM after Auditing
In this section, the results of DSM after auditing are discussed. In the proposed work, after
auditing, the DSM was implemented in two ways: (a) when light sources were LEDs having a power
rating of 13 W, and (b) when light sources were CFLs having a power rating of 22 W.
6.1. DSM after Auditing When the Light Source Is LED
Table 15 shows the result of DSM after auditing in rainy, winter, and summer seasons when the
light source is LED. The cumulative energy-saving in rainy and summer seasons was found to be 127.09
and 108.83 kWh, respectively, whereas, in the winter season, it was 49.52 kWh. This indicates that,
with DSM, the energy consumption reduced significantly in the rainy and summer seasons, whereas,
unlike DSM before auditing, the energy consumption also reduced in the winter season.
Table 15. Results of DSM for energy efficiency after auditing with LED.
Part-A DSM with Auditing in the Rainy Season
Parameter/Time
(a.m./p.m.)
9:00–10:00
a.m.
10:00–11:00
a.m.
11:00 a.m.–12:00
p.m.
12:00–1:00
p.m.
1:00–2:00
p.m.
2:00–3:00
p.m.
3:00–4:00
p.m.
4:00–5:00
p.m.
ECLT (kWh) 3.12 2.82 3.23 3.06 3.09 3.00 3.03 2.97
ECAT (kWh) 9.12 34.20 9.12 6.84 9.12 9.12 9.12 9.12
ECFT (kWh) 3.42 3.36 3.54 4.86 4.98 4.98 4.86 3.54
ECTT (kWh) 15.66 40.38 15.89 14.76 17.19 17.10 17.01 15.63
ECWDSM (kWh) 35.67 33.25 38.54 31.96 31 37.58 35.08 37.63
Savings (kWh) 20.00 -7.13 22.65 17.20 13.81 20.48 18.08 21.99
Part-B DSM with Auditing in the Winter Season
ECLT (kWh) 3.96 4.16 3.92 3.38 3.36 3.00 2.84 2.96
ECAT (kWh) 0 0 0 0 0 0 0 0
ECFT (kWh) 0 0 0 0 0 0 0 0
ECTT (kWh) 3.96 4.16 3.92 3.38 3.36 3.00 2.84 2.96
ECWDSM (kWh) 10.83 10.42 10.83 9.58 9.58 9.17 8.75 7.92
Savings (kWh) 6.87 6.26 6.92 6.21 6.23 6.66 5.91 4.96
Part-C DSM with Auditing in Summer Season
ECLT (kWh) 3.14 3.31 3.14 3.06 0 0 0 0
ECAT (kWh) 9.12 9.12 6.84 9.12 9.12 9.12 9.12 9.12
ECFT (kWh) 3.48 4.80 4.74 4.80 4.80 4.86 4.68 4.92
ECTT (kWh) 15.74 17.23 14.72 16.98 13.92 13.98 13.80 14.04
ECWDSM (kWh) 27.91 28.32 29.32 28.32 29.77 32.55 27.55 25.50
Savings (kWh) 12.17 11.09 14.60 11.34 15.85 18.57 13.75 11.46
Figure 11 represents the variation in energy-saving, shown under “savings” of each part of
Table 15, for the different seasons in a year. From the results, unlike savings in Tables 12–14, it can be
observed that the reduction in energy consumption in winter was positive throughout the day except
at 10:00–11:00 a.m.; however, it was less compared to rainy and summer seasons, because in winter the
weather conditions are very cold, and the maximum light source needs to be operated to maintain
the desired level of illuminance. As a result, more light sources are operated, and energy-saving at
10:00–11:00 a.m. becomes negative, as shown in Figure 11.

Energies 2019,12, 4539 26 of 31
Energies 2019, 12, 4539 26 of 31
the desired level of illuminance. As a result, more light sources are operated, and energy-saving at
10:00–11:00 a.m. becomes negative, as shown in Figure 11.
Figure 11. Hourly energy-saving after DSM with LED.
6.2. DSM after Auditing When the Light Source Is CFL
Table 16 shows the result of DSM after auditing in the different seasons when the light source is
CFL.
Table 16. Results of DSM for energy efficiency after auditing with CFL.
Part-A DSM with Auditing in the Rainy Season
Parameter/
Time
(a.m./p.m.)
9:00–
10:00
a.m.
10:00–
11:00
a.m.
11:00
a.m.–12:00
p.m.
12:00–
1:00 p.m.
1:00–
2:00
p.m.
2:00–
3:00
p.m.
3:00–
4:00
p.m.
4:00–
5:00
p.m.
ECLT (kWh) 6.14 5.96 6.29 5.76 6.18 5.92 6.01 5.92
ECAT (kWh) 6.84 34.20 9.12 6.84 9.12 9.12 9.12 9.12
ECFT (kWh) 3.48 3.36 3.48 4.74 4.92 4.86 4.74 3.54
ECTT (kWh) 16.46 43.52 18.89 17.34 20.22 19.89 19.87 18.58
ECWDSM
(kWh) 35.67 33.25 38.54 31.96 31.00 37.58 35.08 37.63
Savings
(kWh) 19.21 −10.27 19.65 11.41 10.78 17.69 15.22 19.05
Part-B DSM with Auditing in Winter Season
ECLT (kWh) 8.01 7.99 7.59 6.60 6.69 5.94 5.85 6.09
ECAT (kWh) 0 0 0 0 0 0 0 0
ECFT (kWh) 0 0 0 0 0 0 0 0
ECTT (kWh) 8.01 7.99 7.59 6.60 6.69 5.94 5.85 6.09
ECWDSM
(kWh) 10.83 10.42 10.83 9.58 9.58 9.17 8.75 7.92
Savings
(kWh) 2.83 2.43 3.24 2.98 2.89 3.23 2.89 1.82
Part-C DSM with Auditing in Summer Season
ECLT (kWh) 6.09 6.62 6.34 6.12 0 0 0 0
ECAT (kWh) 9.12 9.12 4.56 9.12 9.12 9.12 9.12 6.84
ECFT (kWh) 3.42 4.74 4.92 4.92 4.86 4.86 4.62 4.98
ECTT (kWh) 18.63 20.48 15.82 20.16 13.98 13.98 13.74 11.82
-10
-5
0
5
10
15
20
25
9:00-10:00AM 10:00-11:00AM 11:00-12:00NN 12:00-1:00PM 1:00:200PM 2:00:300PM 3:00:400PM 4:00:500PM
Hourly savings (kWh)
Rainy Winter Summer
Figure 11. Hourly energy-saving after DSM with LED.
6.2. DSM after Auditing When the Light Source Is CFL
Table 16 shows the result of DSM after auditing in the different seasons when the light source
is CFL.
Table 16. Results of DSM for energy efficiency after auditing with CFL.
Part-A DSM with Auditing in the Rainy Season
Parameter/Time
(a.m./p.m.)
9:00–10:00
a.m.
10:00–11:00
a.m.
11:00 a.m.–12:00
p.m.
12:00–1:00
p.m.
1:00–2:00
p.m.
2:00–3:00
p.m.
3:00–4:00
p.m.
4:00–5:00
p.m.
ECLT (kWh) 6.14 5.96 6.29 5.76 6.18 5.92 6.01 5.92
ECAT (kWh) 6.84 34.20 9.12 6.84 9.12 9.12 9.12 9.12
ECFT (kWh) 3.48 3.36 3.48 4.74 4.92 4.86 4.74 3.54
ECTT (kWh) 16.46 43.52 18.89 17.34 20.22 19.89 19.87 18.58
ECWDSM (kWh) 35.67 33.25 38.54 31.96 31.00 37.58 35.08 37.63
Savings (kWh) 19.21 −10.27 19.65 11.41 10.78 17.69 15.22 19.05
Part-B DSM with Auditing in Winter Season
ECLT (kWh) 8.01 7.99 7.59 6.60 6.69 5.94 5.85 6.09
ECAT (kWh) 00 0 00000
ECFT (kWh) 00 0 00000
ECTT (kWh) 8.01 7.99 7.59 6.60 6.69 5.94 5.85 6.09
ECWDSM (kWh) 10.83 10.42 10.83 9.58 9.58 9.17 8.75 7.92
Savings (kWh) 2.83 2.43 3.24 2.98 2.89 3.23 2.89 1.82
Part-C DSM with Auditing in Summer Season
ECLT (kWh) 6.09 6.62 6.34 6.12 0 0 0 0
ECAT (kWh) 9.12 9.12 4.56 9.12 9.12 9.12 9.12 6.84
ECFT (kWh) 3.42 4.74 4.92 4.92 4.86 4.86 4.62 4.98
ECTT (kWh) 18.63 20.48 15.82 20.16 13.98 13.98 13.74 11.82
ECWDSM (kWh) 27.91 28.32 29.32 28.32 29.77 32.55 27.55 25.50
Savings (kWh) 9.28 7.84 13.50 8.16 15.79 18.57 13.81 13.68
In this case, also, the cumulative energy-saving in rainy and summer seasons was found to be
105.93 and 100.62 kWh, respectively, whereas, in the winter, it was 22.33 kWh. This indicates that,
with DSM, the energy consumption reduced significantly in the rainy and summer seasons, whereas,
unlike DSM before auditing, the energy consumption also reduced in the winter. However, the overall
reduction in energy consumption was less in this case as compared to the previous case when the light
source is LED. However, the energy-saving was still negative at 10:00–11:00 a.m. with CFL, as shown
in Figure 12.

Energies 2019,12, 4539 27 of 31
Energies 2019, 12, 4539 27 of 31
ECWDSM
(kWh) 27.91 28.32 29.32 28.32 29.77 32.55 27.55 25.50
Savings
(kWh) 9.28 7.84 13.50 8.16 15.79 18.57 13.81 13.68
In this case, also, the cumulative energy-saving in rainy and summer seasons was found to be
105.93 and 100.62 kWh, respectively, whereas, in the winter, it was 22.33 kWh. This indicates that,
with DSM, the energy consumption reduced significantly in the rainy and summer seasons, whereas,
unlike DSM before auditing, the energy consumption also reduced in the winter. However, the
overall reduction in energy consumption was less in this case as compared to the previous case when
the light source is LED. However, the energy-saving was still negative at 10:00–11:00 a.m. with CFL,
as shown in Figure 12.
Figure 12. Hourly energy-saving after DSM with CFL.
7. Cost Analysis and Recommendations
Table 17 shows the number of existing and proposed components before and after auditing.
Table 17. Room size and components before and after auditing.
S. No. Room No Room Size in Feet Before Auditing After Auditing
Length Breadth Height Fan Light AC Fan Light AC
1 D106 18.5 8.67 8.42 2 14 2 2 5 1
2 D104 38 30 8.83 7 26 3 7 32 3
3 D107 18.5 8.67 8.42 2 4 0 2 5 0
4 D108 18.5 8.67 8.42 2 4 0 2 5 0
5 D109 18.5 8.67 8.42 2 3 0 2 5 0
6 D110 18.5 8.67 8.42 2 3 0 2 5 0
7 D101 29.92 24.42 9.08 6 18 4 5 20 3
8 D102 29.92 24.42 9.08 6 16 4 5 20 3
9 D116 41.25 50.5 11.83 16 52 0 12 58 0
10 D115 41.25 50.5 11.83 12 52 0 12 58 0
11 Research Lab 35 9.42 9 4 8 2 3 9 2
12 D201 35.75 29.83 9.42 11 40 4 7 30 3
13 D202 35.75 29.83 9.42 11 40 4 7 30 3
14 D203 29.66 18.33 9.42 5 22 2 4 15 2
15 D204 29.66 18.33 9.42 5 23 2 4 15 2
16 Cabin (S-lab) 12.75 12.42 12.42 1 4 1 2 5 1
17 Structure Lab 157 30.75 16.08 36 46 0 28 136 0
18 D120 13 10 13.83 1 2 0 1 4 0
-12
-7
-2
3
8
13
18
23
9:00-10:00AM 10:00-11:00AM 11:00-12:00NN 12:00-1:00PM 1:00:200PM 2:00:300PM 3:00:400PM 4:00:500PM
Hourly savings (kWh)
Rainy Winter Summer
Figure 12. Hourly energy-saving after DSM with CFL.
7. Cost Analysis and Recommendations
Table 17 shows the number of existing and proposed components before and after auditing.
Table 17. Room size and components before and after auditing.
S. No. Room No Room Size in Feet Before Auditing After Auditing
Length Breadth Height Fan Light AC Fan Light AC
1 D106 18.5 8.67 8.42 2 14 2 2 5 1
2 D104 38 30 8.83 7 26 3 7 32 3
3 D107 18.5 8.67 8.42 2 4 0 2 5 0
4 D108 18.5 8.67 8.42 2 4 0 2 5 0
5 D109 18.5 8.67 8.42 2 3 0 2 5 0
6 D110 18.5 8.67 8.42 2 3 0 2 5 0
7 D101 29.92 24.42 9.08 6 18 4 5 20 3
8 D102 29.92 24.42 9.08 6 16 4 5 20 3
9 D116 41.25 50.5 11.83 16 52 0 12 58 0
10 D115 41.25 50.5 11.83 12 52 0 12 58 0
11 Research Lab 35 9.42 9 4 8 2 3 9 2
12 D201 35.75 29.83 9.42 11 40 4 7 30 3
13 D202 35.75 29.83 9.42 11 40 4 7 30 3
14 D203 29.66 18.33 9.42 5 22 2 4 15 2
15 D204 29.66 18.33 9.42 5 23 2 4 15 2
16 Cabin (S-lab) 12.75 12.42 12.42 1 4 1 2 5 1
17 Structure Lab 157 30.75 16.08 36 46 0 28 136 0
18 D120 13 10 13.83 1 2 0 1 4 0
19 Cabin-1 30.75 13 13.83 2 3 0 3 12 0
20 D112 31.75 28.5 8.92 11 16 4 6 25 3
21 Trans. Lab 49.42 29 12.92 15 40 0 9 40 0
22 D117 30.42 12.25 14.25 2 2 0 3 11 0
23 D114 18.58 8.58 14.42 2 4 0 2 5 0
24 D113 18.58 8.58 14.42 2 4 0 2 5 0

Energies 2019,12, 4539 28 of 31
Table 17. Cont.
S. No. Room No Room Size in Feet Before Auditing After Auditing
Length Breadth Height Fan Light AC Fan Light AC
25 D111 18.75 19.83 9.17 2 8 0 3 11 0
26 D123 13 10 13.83 1 2 0 1 4 0
27 Cabin-2 19.33 8.67 8.42 2 10 1 2 5 1
28 D103 13 10 13.83 1 2 0 1 4 0
29 D118 30.42 22.67 9.33 4 28 4 5 19 3
30 Stair Room 12.75 12.42 12.42 1 8 0 2 5 0
31 D119 13 10 13.83 1 8 1 1 4 1
32 D121 12.83 12.25 9.42 1 8 0 2 5 0
33 Corridor-1 418.08 11.67 8.83 0 10 0 0 10 0
34 Corridor-2 62.42 19.92 12.58 0 3 0 0 3 0
The light sources were calculated when all the components were LED and CFL, as shown in Part
A and Part B of Table 18, respectively. In Part A, there were no existing LEDs, and all the light sources
needed to be replaced; therefore, the difference was equal to the proposed components.
Table 18. Cost of components, savings, and the payback period.
Part A: When the Light Source is LED
Items Existing
Component
Proposed
component
Extra
Component
Required
Labor Cost
(Rs)
Accessories
Cost (Rs)
Component
Cost
(Rs/unit)
Total Cost
(Rs)
Energy
Saving
(kWh)
Energy
Cost@7/-
Payback
Period
(Year)
LED 0 610 610 60 50 130 79,410
95.15
133,210
0.60
FAN 178 142 −36 Not applicable, since the numbers of fans and ACs are
greater than the recommendations
AC1 39 17 −22
AC2 39 32 −7
Part B: When the Light Source is CFL
CFL 107 610 503 60 50 75 37,835
76.29
106,806
0.35
FAN 178 142 −36 Not applicable, since the numbers of fans and ACs are
greater than the recommendations
AC1 39 17 −22
AC2 39 32 −7
On the other hand, in the proposed auditing, the numbers of fans and ACs were found to be
greater than required. In the analysis, it was believed that the cost of the extra components could not
be recovered and, therefore, their labor cost, accessories cost, and component cost were not applicable
as shown in Table 18 in parts A and B. Furthermore, average energy-saving per day was taken, and the
cost of energy-saving was calculated at the rate of 7 Rs per kWh for 200 days in a year. Furthermore,
from the results shown in Table 18, it can be observed that the simple payback period of the proposed
approach was seven months (approximately) when the light source is LED, whereas it was only four
months when the light source is CFL.
8. Conclusions
This paper presented the energy efficiency evaluation of a commercial building with strategic
energy auditing and demand-side management (DSM) under different environmental conditions. The
environmental conditions were divided into three seasons in a year. For energy efficiency evaluation,
a strategic energy auditing was performed to identify various parameters such as lumens per watt,
illuminance in work and non-working areas, and load efficacy, thereby leading to the development of
operating strategies for power components. Here, the DSM was implemented before and after strategic
auditing, and the comparison of the results was also presented. From the results, it was observed
that, in the commercial building, the scope of energy-saving in different seasons varied with operating

Energies 2019,12, 4539 29 of 31
constraints including temperature, humidity, number of persons, and the technical standards. The
scope of energy-saving before auditing and after auditing was also found to be different. The variation
in energy-saving is not only seasonal; rather, it may also vary with the time of operation and the state
of the economy throughout the day. This requires conducting regular auditing of the energy-intensive
building and constraining the implementation of the recommendations. Results also showed that
the total number of components required reduced in the proposed approach, which reduces energy
consumption and, hence, improves energy efficiency without affecting the desired level of comfort.
However, in the proposed approach, the system voltage profile was considered as fixed, whereas, in
practice, the operation of various power components may change with the change in system voltage
profile, which can also affect energy efficiency. Therefore, a comprehensive analysis of energy efficiency
with voltage variation in different seasons needs to be evaluated in the future.
Author Contributions:
Conceptualization, P.K., G.S.B., and S.S.; methodology, P.K and G.S.B.; audit parameters,
P.K., G.S.B., and S.S.; validation, S.N., Z.B., and H.R.B.; formal analysis, P.K., G.S.B., S.S., and Z.B.; investigation,
P.K., S.S., and G.S.B.; resources, P.K. and S.S.; writing—original draft preparation, P.K. and G.S.B.; writing—review
and editing, H.R.B.; supervision, H.R.B. and S.N.
Funding: This research received no external funding.
Acknowledgments:
This research was sponsored by the Thapar Institute of Engineering and Technology, Patiala,
under the seed money project grant scheme with Ref. TU/DORSP/57/3975.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Coefficient of utilization using the zonal cavity method [37].
RCC% 80 70 50 30 10 0
RW% 70 50 30 0 70 50 30 0 50 30 20 50 30 20 50 30 20 0
RCR:0
1.19 1.19 1.19 1.19 1.16 1.16 1.16 1.00 1.11 1.11 1.11 1.06 1.06 1.06 1.02 1.02 1.02 1.00
1
1.10 1.06 1.02 0.98 1.07 1.03 1.00 0.87 0.99 0.96 0.94 0.95 0.93 0.91 0.92 0.90 0.88 0.86
2
1.01 0.94 0.88 0.83 0.99 0.92 0.86 0.75 0.89 0.84 0.80 0.85 0.81 0.78 0.82 0.79 0.76 0.74
3
0.93 0.84 0.77 0.71 0.91 0.82 0.76 066 0.79 0.74 0.69 0.77 0.72 0.68 0.74 0.70 0.67 0.65
4
0.86 0.75 0.68 0.61 0.84 0.74 0.67 0.58 0.72 0.65 0.60 0.70 0.64 0.59 0.67 0.63 0.59 0.57
5
0.80 0.68 0.60 0.54 0.78 0.67 0.60 0.52 0.65 0.58 0.53 0.63 0.57 0.53 0.62 0.56 0.52 0.50
6
0.74 0.62 0.54 0.48 0.73 0.61 0.54 0.46 0.60 0.53 0.47 0.58 0.52 0.47 0.56 0.51 0.47 0.45
7
0.69 0.57 0.49 0.43 0.68 0.56 0.48 0.42 0.55 0.48 0.43 0.53 0.47 0.42 0.52 0.46 0.42 0.40
8
0.65 0.52 0.44 0.39 0.63 0.52 0.44 0.38 0.50 0.44 0.39 0.49 0.43 0.38 0.48 0.42 0.38 0.36
9
0.61 0.48 0.41 0.35 0.60 0.48 0.40 0.35 0.47 0.40 0.35 0.46 0.39 0.35 0.45 0.39 0.35 0.33
10
0.57 0.45 0.37 0.32 0.56 0.44 0.37 0.32 0.43 0.37 0.32 0.42 0.36 0.32 0.42 0.36 0.32 0.30
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