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TUTA/IOE/PCU
Journal of the Institute of Engineering
October 2019, Vol 15 (No. 3): 275-284
© TUTA/IOE/PCU, Printed in Nepal
Proceedings of 4th International Conference on Renewable Energy Technology for Rural and Urban Development (RETRUD-18)
Demand Characteristics of Electricity in Residential Sector of
Kathmandu Valley
Utsav Shree Rajbhandari 1, *, Laxman Poudel 1, Nawraj Bhattarai 1
1 Department of Mechanical Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal
Corresponding Email: utsavshree@hotmail.com
Abstract:
Planning for electricity demand is a vital as the characteristics of different electricity generation systems vary temporally – both hourly
in a day as well as seasonally in a year. Thus, this study focuses on evaluating the demand characteristic of electricity in residential
sector within urban boundaries of three districts in Kathmandu Valley. It has addressed variations in demand in form of load curve
based on hourly peak demand during a day. The demand characteristics have been affected in recent years are primarily influenced
by two factors – the trade debacle in 2015 and the end of load-shedding. A typical family with owned household would have highest
demand with loads spread over various time of the day. While the one in rented family would have least demand level with most
characteristic peaks. But in overall, there are peculiar morning and evening peaks, in addition to small early morning peak. The current
technology interventions and electricity consumption pattern with reference in earlier years depicts the change in energy technology
preference. The reduction in daily demand pattern as well as total electricity demand are majorly due to replacement of older
technologies with more efficient appliances as well as reduced use of inverters for battery charging. Thus, it can be said that a stringent
condition can enforce people to change to efficient technologies as well as proper supply can reduce unnecessary demand in battery
charging. On other hand, similar trend, and hence the increase in electricity demand, can be anticipated in other flourishing urban
areas of the country. Additionally, it is beneficial to have the demand characteristics of each sector separately - which can be useful
to design the decentralized systems for specific sector.
Keywords: Urban, Nepal, Electricity, Statistical Prediction, Load Curve
1 Introduction
Electricity is versatile form of energy – it can be easily
transformed into any form of energy in most of the cases.
Thus, electricity has been the major demanded form of
energy, not only in stationary use, but also in for mobile
form of technologies – like in transportation. The
development of countries is scaled by consumption of
energy. Thus the sustainable development goals has
focused on access to modern energy to every households
[1], while world bank has set 5-tier targets based on
electricity consumption per household [2].
The load profile development has been used as an
efficient, organized and systematic tool for energy
planning design and load management [3]. Proper analysis
and subsequent implications of accurate load profiles can
not only be used for balancing load for increasing system
stability and reliability but also design and develop the
new supply systems and strategies for load diversification
such as by grid connected solar PVs.
1.1 National context
Figure 1: System load curve of Nepal in 2017 peak day
The electricity demand in Nepal has been facing deficit
for past decade. Nepal has faced load shedding due to
supply deficit. Nepal has been importing electricity from
India for a long time now and in 2017 alone, Nepal
Electricity Authority (NEA) purchased 2,175 GWh of
electricity in 2017 which is 35% of total demand [4].
According to NEA’s report, it is expected to reach 2,580
GWh in 2018 which totals to 37%. Figure 1 shows a daily
load curve for Nepal in 2017. It can be seen that there is
an abrupt peak in the evening. Five years back, two
prominent peaks could be observed [5]. The demand in
morning peak has not decreased. But demand in other time
Demand Characteristics of Electricity in Residential Sector of Kathmandu Valley
276
have increased. The current smoothing in load curve
could be attributed to two main factors – first the bloom
of commercial institutions and secondly load distribution
in industries to off peak hours.
1.2 Kathmandu Valley
Kathmandu Valley – that constitute three districts is the
most densely populated area in one hand [6] and fastest
growing urban agglomerate in Nepal [7]. Thus, not only
the demand for electricity is increasing in whole nation,
but in Kathmandu valley as well. Kathmandu valley is a
major focal point in terms of electricity load management.
From Figure 2 it can be seen that in 2017, more than one
fourth of peak demand can be attributed to Kathmandu
valley alone [8]. It is also noticeable that the load pattern
of Nepal and Kathmandu looks very similar. Thus, it can
be seen that the demand pattern of Kathmandu valley
highly determines the load pattern of whole nation.
Figure 2: Daily load curves for supply and demand of Nepal
and Kathmandu in 2017
Figure 3: Demand curve of a household in Kathmandu
Valley in 2013
Now taking one more step into root level of demand side,
if we look at the pattern of residential sector as studied by
Rajbhandari et al. presented in Figure 3 [9] and compare
it to that of Figure 2 we can see another striking similarity
in the pattern. In addition to that, an estimation derived by
Pudasaini and Bhattarai showed that the residential peak
demand of Kathmandu valley was about 250 MW in 2015
[10]. Thus, it can be deduced that the peak load pattern of
the valley is primarily determined by residential sector.
The objective of this paper is to develop realistic
electricity profiles for local resident of Kathmandu valley.
The load profiles for the domestic customers of
Kathmandu Valley are generated through statistical
prediction put upon by Rahman and Arnob [11]. It is
aimed at increasing knowledge and understanding of load
usage of urban context.
2 Methodology
Figure 4 represents the general methodological approach
from developing the load profiles of residential sector
through consumer survey. It is important to have as much
comprehensive and detailed electricity usage data for
accurate analysis if demand. Bottom-up approach is
applied in this study – which builds up the total load for
each type of house, considering every major appliance, in
a statistical average manner. The number of statistically
significant sample size was calculated using methodology
given by Krejcie and Morgan [12]. With confidence level
of 95% and degree of accuracy 10%, a total of 96
household samples were taken.
Figure 4: Methodological flowchart
The questionnaire would give appliance saturation that
represents usage of each appliances and hourly usage of
each appliance. In addition to that, it would also give
information on frequency of use and nominal wattage. The
analysis was done and the load profiles are plotted in
Excel on an hourly basis to generate daily electrical load
profiles for each of the household types. Microsoft Excel
has been used to plot the profile due to its acceptance as
powerful and widely used Spreadsheet software
developed by Microsoft Corporation.
Rajbhandari et al.
277
2.1 Household categories
The pattern of electricity demand could be affected by
various factors ranging from income, family size, house
built up and so on. For this study, four family types have
been considered based on categorical differentiation given
by Michalik et al. [13].
Table 1: Household Categories
Cate-
gory Description
H1
At least one adult at home, children at home or at
school (typical family with small children, working
father, mother at home)
H2
Two or more working adults, children at school
and/or university during business hours (both
parents have jobs outside the household)
H3
One, two or three working adults, no children at
home (single person, working couple without
children, or a household shared by a few young
working persons)
H4
One or two adults, at least one at home during
business hours, no children at home (couple/single
pensioner, couple/single unemployed
2.2 Electric appliances saturation and use
The findings of the survey only represent the number of
users of each appliance regardless of ownership. It also
includes the nominal power rating of each appliance
commonly used and their working frequency each day as
well as weekly to consider the variability of usage in
monthly or annual basis. For refrigerator, which is usually
left connected to load, it is taken that it runs for 12 minutes
with frequency of 40 times per day [14]. In addition to
these, following assumptions were made:
• Differentiation between weekdays and weekends is
not considered,
• Different daily load profiles are not simulated; rather
single day is simulated. The mean daily use frequency
factor is assumed to incorporate the dynamics.
• Use of an appliance is affected by users' behaviors.
However, it is very difficult to model such stochastic
behavior. The average usage time for an appliance is
assumed to cover up that.
• The variations in economic conditions, influencing
the appliance type and usage patterns are not
considered.
The list of appliances, there usage saturation along with
their nominal wattage and usage details are given in
Annex. These data represent average of all four household
categories.
2.3 Time of use probability profile
The time of use probability profiles gives probability of
the particular activity being commenced or undertaken as
a function of time of day. The probability profiles
represent the probability of a household carrying out a
specified activity during a 24 hour period. Rahman and
Arnob used this method to develop load profile for Dhaka
[11]. The relative probability of each appliance use is
given by
=
(1)
Where PR is the relative probability, Nx is the number of
times an event x occurred, and Nt is the total number of
trials. These data are obtained from the questionnaire
survey and the probability of appliance use was developed
using equation (1), which was used generate load profiles.
The probability of each appliance being used in each hour
are given in Annex.
3 Findings and Discussions
Figure 5 shows the load profiles for four different type of
households considered for study. The household type H1,
which represents a typical household – especially with
more than two generations residing in a house – shows two
peculiar peaks at morning and evening between 7 and 8 in
both morning and evening. A tiny peak at around 5 am
represents the use of appliance by early risers like students
or elderlies.
The household type H2 – which mostly represents small
family with parents and children, shows similar pattern of
H1 however being smaller size as well as very few
activities at business hours, the maximum demands at
each hour are lower than that of H1.
The household type H3 – which generally represents a
group of adults such as working people or students in rents
shows a very different pattern. There are two peaks in
morning. The first peaks are due to early rising students
while the second rise are majorly due to working adults or
day scholars. The evening peak is at as other earlier
families but not as wide spread as in category with majorly
owned house. In addition to that, the total electricity
demand is also much lower in this category due to lower
saturation of appliances.
The household type H4 – that majorly includes a family
with very less chance of early risers shows similar pattern
to that of type H1 but the early morning peak is not as
prominent as in prior types. There is another small peak
later in daytime near around when cooking or heating of
food is generally required.
Demand Characteristics of Electricity in Residential Sector of Kathmandu Valley
278
(a)
(b)
(c)
(d)
(e)
Figure 5: Load profile for different households (a) type H1
(b) type H2 (c) type H3 (d) type H4 and (e) cumulative
average of types of households.
Thus, it can be seen that the behavioral types of each
household can differentiate the demand profile as well as
overall demand of each type of family.
The overall load pattern in Figure 5(e) when compared to
Figure 2 and Figure 3 shows similarities. Thus, the
outcomes of this study can represent the load pattern of
the urban household in Kathmandu valley. Going further
into the total demand, the average electricity consumption
per annum totals to around 182 kWh per capita per annum.
This is in between to the mark of 208 kWh per capita in
2013 [15] and 140 kWh in 2016 [16-18]. The difference
in the annual consumption arises from few assumptions
and limitations of the study. Firstly, it is hugely affected
by randomness of samples taken. Secondly, the former
study was limited within only five municipalities of the
valley in that time. Secondly, the study does not consider
winter load like room heater or added water heating or
cooking time. Also, in course of time, the users of CRT
TV, CFL and tube lights, desktops have drastically
reduced and got replaced by efficient LED TV, LED lights
and laptops. In addition to that, due to removal of load-
shedding the use of inverters for battery charging has also
reduced. The effect of these can also be seen when
compared to Figure 3. One of the main parts of electricity
demand in 2103 was other uses, which included inverters
as well as larger number of less efficient appliances [9].
With these findings following insights can be deduced:
• The pattern and total demand of the household can be
affected by the type of household composition. The
more the members with some economically inactive
member have higher electricity demand.
• The peculiar morning an evening peaks are easily
visible during period of 7 AM to 8 AM in the morning
and evening peak at 7 PM to 8 PM. However, the
demand pattern can spread to varying duration
usually with household with someone at home.
• The early morning peaks can be seen in households
with early risers.
• The saturation of appliances is also dependent of
family type – usually the ownership.
• The overall demand in electricity might have reduced
due to penetration of efficient appliances. But the
total demand in the valley remains high due to
increasing number of populations.
4 Conclusion
The objective of this study was to generate load profile of
electricity demand of residential sector in Kathmandu
valley for various household types. The households were
classified into four types my composition of family
members. Bottom-up approach was applied to analyze
appliance usage profile.
Rajbhandari et al.
279
The study considers typical electrical appliances, its
relative saturation depending on the household type, the
power rating and the utilization pattern in terms of
frequency of operation, probability of being on at
particular hour. The overall load pattern and total demand
was checked against the load profile of Kathmandu valley
and total electricity consumption.
The current technology interventions and electricity
consumption pattern with reference in earlier years depicts
the change in energy technology preference, which has
been brought about by accessibility to technologies
reducing prices and also some externalities. The reduction
in daily demand pattern as well as total electricity demand
are majorly due to replacement of older technologies with
more efficient appliances as well as reduced use of
inverters for battery charging. Thus, it can be said that a
stringent polices can enforce people to change to efficient
technologies as well as proper supply can reduce
unnecessary demand in battery charging. On other hand,
taking similar trend of the valley, the increase in electricity
demand can be anticipated in other flourishing urban areas
of the country as well.
The more detailed modeling of electricity usage pattern
could help in future research for not only demand side
management but also operation and design of supply side
such as solar PV and smart grid systems.
References
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UNDESA, "Sustainable Development Goal 7," [Online].
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M. Bhatia and N. Angelou, "Beyond Connections :
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Union of the Electricity Industry, "Metering Load,
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Markets," 2000.
[4]
NEA, "Annual Report 2018," Nepal Electricty Authority,
Kathmandu, Nepal, 2018.
[5]
NEA, "A Year in Review, Fiscal Year 2011/12,"
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[6]
CBS, "National Population and Housing Census 2011,"
Central Bureau of Statistic, National Planning
Commission Secretariat, Government of Nepal,
Kathmandu, 2012.
[7]
E. Muzzini and G. Aparicio, Urban Growth and Spatial
Transition in Nepal: An Initial Assessment, World Bank
Publications, 2013.
[8]
NEA, "Load curve complied by Bikal Adhikari," Nepal
Electricity Authority, Kathmandu, Nepal, 2018.
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U. S. Rajbhandari, L. Poudel, N. Bhattrai and S. R.
Shakya, "Development of Demand Load Curve for
Electricity Consumption of the Residential Sector in
Kathmandu Valley," in
1st TU-NUAA Joint Academic
Workshop, Kathmandu, Nepal, 2018.
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N. Pudasaini and B. Nawraj, "Impact Study of
Decentralised PV Generation on Peak Load Reduction in
Residential Sector of Kathmandu Valley," in
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Graduate Conference 2015
, Lalitpur, Nepal, 2015.
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A. Rahman and S. I. C. Arnob, "Developing Load Profile
for Domest
ic Customers of Dhaka City through
Statistical Prediction," in
2016 3rd International
Conference on Electrical Engineering and Information
Communication Technology (ICEEICT)
, Dhaka,
Bangladesh, 2016.
[12]
R. V. Krejcie and D. W. Morgan, "Determining Sample
Size for Research,"
Educational and Psychological
Measurement,
pp. 607-610, 1970.
[13]
G. Michalik, M. E. Khan, W. J. Bonwlck and W.
Mielczarski, "Structural Modelling Of Energy Demand
In The Residential Sector: 1. Development Of Structural
Models,"
pp. 937-947, 1997.
[14]
L. Chuan and A. Ukil, "Modeling and Validation of
Electrical Load Profiling in Residential Buildings in
Singapore,"
IEEE Transactions on Power Systems, vol.
30, no. 5, pp. 2800 - 2809, 2014.
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U. S. Rajbhandari and A. M. Nakarmi, "Energy
Consumption and Scenario Analysis of Residential
Sector Using Optimization Model
– A Case of
Kathmandu Valley," in
Proceedings of IOE Graduate
Conference
, Kathmandu, Nepal, 2014.
[16]
AEPC, "District Climate and Energy Plan - Kathmandu
District," Alternative Energy Promotion Center, Lalitpur,
Nepal, 2017.
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AEPC, "District Climate and Energy Plan - Lalitpur
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Nepal, 2017.
Appendix
Demand Characteristics of Electricity in Residential Sector of Kathmandu Valley
280
Table 2: The questionnaire (Lifestyle Matrix)
Table 3: Residential appliances: list, saturation level, nominal wattage average daily use frequency, and time per use.
Appliances Saturation Nominal
wattage
Average days
of use per week
Averaged times
of use per day
Mean daily
use frequency
Average time
per usage (hours)
LED 0.65 10 7 2 2.00 3.00
CFL 0.61 20 7 2 2.00 3.00
TFT 0.33 40 7 2 2.00 3.00
Bulb 0.06 25 7 1.75 1.75 3.00
Rice cooker 0.51 500 7 2 2.00 0.50
Coil heater/hot plate 0.03 1000 0.25 0.25 0.01 0.50
Induction Cooktop 0.48 2000 5.5 1.75 1.38 0.17
Microwave 0.32 1000 4.75 1.5 1.02 0.08
Mixer blender 0.61 400 2.75 1 0.39 0.02
Refrigerator 0.56 200 7 40.5 40.50 0.20
Water pump 0.69 750 2.25 1 0.32 1.00
Washing Machine 0.43 300 1 1 0.14 0.50
Cloth Iron 0.54 1000 1.75 1 0.25 0.10
Fan 0.69 40 4.5 1 0.64 2.00
TV - CRT 0.48 120 6.25 1.25 1.12 2.00
TV - LCD 0.16 150 6.25 1 0.89 2.00
TV - LED 0.54 30 6.75 1.25 1.21 2.00
Desktop 0.15 150 3.25 1 0.46 2.00
Laptop 0.59 50 6.75 1 0.96 4.00
Rajbhandari et al.
281
Table 4: Time of use probability profile for household H1
Time of day
AM
12
1
2
3
4
5
6
7
8
9
10
11
LED
0.0491
0.0119
-
-
0.0138
0.0459
0.0459
0.0278
-
-
-
-
CFL
0.0491
0.0119
-
-
0.0138
0.0459
0.0459
0.0278
-
-
-
-
TFT
0.0491
0.0119
-
-
0.0138
0.0459
0.0459
0.0278
-
-
-
-
Bulb
0.0491
0.0119
-
-
0.0138
0.0459
0.0459
0.0278
-
-
-
-
Rice cooker
-
-
-
-
-
-
-
0.4000
0.1000
-
-
-
Coil heater/hot plate
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Induction Cooktop
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Microwave
-
-
-
-
-
0.0909
-
0.1818
0.1818
-
-
-
Mixer blender
-
-
-
-
-
-
-
0.4615
0.1846
0.0769
-
-
Refrigerator
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
Water pump
-
-
-
-
0.1667
0.3333
-
0.0833
0.0833
0.0833
-
-
Washing Machine
-
-
-
-
-
-
-
-
-
0.0645
0.1613
0.3226
Cloth Iron
-
-
-
-
-
-
0.0800
0.2000
0.1200
-
-
-
Fan
0.0339
-
-
-
-
-
-
-
-
-
-
-
TV - CRT
-
-
-
-
-
-
-
0.1429
0.1429
-
-
-
TV - LCD
-
-
-
-
-
-
-
0.0588
0.0588
0.0588
-
-
TV - LED
0.0250
-
-
-
0.0125
0.0125
0.0375
0.0375
0.0375
0.0125
0.0125
-
Desktop
-
-
-
-
-
-
-
-
-
-
-
-
Laptop
0.0357
0.0357
-
-
-
0.0714
0.0714
0.0714
0.0714
-
-
-
Time of day
PM
12
1
2
3
4
5
6
7
8
9
10
11
LED
-
-
-
-
-
0.0388
0.0558
0.1625
0.1741
0.1920
0.1489
0.0335
CFL
-
-
-
-
-
0.0388
0.0558
0.1625
0.1741
0.1920
0.1489
0.0335
TFT
-
-
-
-
-
0.0388
0.0558
0.1625
0.1741
0.1920
0.1489
0.0335
Bulb
-
-
-
-
-
0.0388
0.0558
0.1625
0.1741
0.1920
0.1489
0.0335
Rice cooker
-
-
-
-
-
-
-
0.4000
0.1000
-
-
-
Coil heater/hot plate
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Induction Cooktop
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Microwave
-
-
0.0909
-
-
0.0909
-
-
0.1818
0.1818
-
-
Mixer blender
-
-
-
-
-
-
-
0.0769
0.2000
-
-
-
Refrigerator
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
Water pump
-
0.0833
0.0833
0.0833
-
-
-
-
-
-
-
-
Washing Machine
0.3226
0.1290
-
-
-
-
-
-
-
-
-
-
Cloth Iron
-
-
-
-
-
-
-
-
0.2000
0.4000
-
-
Fan
-
0.1017
0.1017
0.1017
0.1017
0.1017
0.1186
0.0847
0.0847
0.0847
0.0847
-
TV - CRT
-
0.1429
0.1429
-
-
-
0.1429
0.1429
0.1429
-
-
-
TV - LCD
-
-
-
-
-
-
0.0588
0.1765
0.1765
0.1765
0.1176
-
TV - LED
-
0.0750
0.0750
0.0750
0.0500
0.0625
0.0875
0.1000
0.1000
0.1000
0.0625
0.0250
Desktop
-
-
-
-
-
-
-
0.1429
0.5714
0.2857
-
-
Laptop
-
-
0.0179
0.0179
0.0179
0.0179
0.0179
0.1250
0.1250
0.1250
0.1250
0.0536
Demand Characteristics of Electricity in Residential Sector of Kathmandu Valley
282
Table 5: Time of use probability profile for household H2
Time of day
AM
12
1
2
3
4
5
6
7
8
9
10
11
LED
0.0655
0.0159
-
-
0.0184
0.0555
0.0555
0.0371
-
-
-
-
CFL
0.0655
0.0159
-
-
0.0184
0.0555
0.0555
0.0371
-
-
-
-
TFT
0.0655
0.0159
-
-
0.0184
0.0555
0.0555
0.0371
-
-
-
-
Bulb
0.0655
0.0159
-
-
0.0184
0.0555
0.0555
0.0371
-
-
-
-
Rice cooker
-
-
-
-
-
-
-
0.4000
0.1000
-
-
-
Coil heater/hot plate
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Induction Cooktop
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Microwave
-
-
-
-
-
0.0909
-
0.1818
0.1818
-
-
-
Mixer blender
-
-
-
-
-
-
-
0.0476
-
-
-
-
Refrigerator
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
Water pump
-
-
-
-
0.1667
0.3333
-
0.0833
0.0833
0.0833
-
-
Washing Machine
-
-
-
-
-
-
-
-
-
0.0645
0.1613
0.3226
Cloth Iron
-
-
-
-
-
-
0.0800
0.2000
0.1200
-
-
-
Fan
0.0339
-
-
-
-
-
-
-
-
-
-
-
TV - CRT
-
-
-
-
-
-
-
-
0.1429
0.1429
-
-
TV - LCD
0.1176
-
-
-
-
-
-
-
0.0588
0.0588
0.0588
-
TV - LED
0.0250
-
-
-
0.0125
0.0125
0.0375
0.0375
0.0375
0.0125
0.0125
-
Desktop
-
-
-
-
-
-
-
-
-
-
-
-
Laptop
0.0357
0.0357
-
-
-
0.0714
0.0714
0.0714
0.0714
-
-
-
Time of day
PM
12
1
2
3
4
5
6
7
8
9
10
11
LED
-
-
-
-
-
0.0406
0.0576
0.1489
0.1586
0.1824
0.1364
0.0278
CFL
-
-
-
-
-
0.0406
0.0576
0.1489
0.1586
0.1824
0.1364
0.0278
TFT
-
-
-
-
-
0.0406
0.0576
0.1489
0.1586
0.1824
0.1364
0.0278
Bulb
-
-
-
-
-
0.0406
0.0576
0.1489
0.1586
0.1824
0.1364
0.0278
Rice cooker
-
-
-
-
-
-
-
0.4000
0.1000
-
-
-
Coil heater/hot plate
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Induction Cooktop
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Microwave
-
-
0.0909
-
-
0.0909
-
-
0.1818
0.1818
-
-
Mixer blender
-
-
-
-
-
-
0.1190
0.3571
0.4762
-
-
-
Refrigerator
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
Water pump
-
0.0833
0.0833
0.0833
-
-
-
-
-
-
-
-
Washing Machine
0.3226
0.1290
-
-
-
-
-
-
-
-
-
-
Cloth Iron
-
-
-
-
-
-
-
-
0.2000
0.4000
-
-
Fan
-
0.1017
0.1017
0.1017
0.1017
0.1017
0.1186
0.0847
0.0847
0.0847
0.0847
-
TV - CRT
-
-
0.1429
0.1429
-
-
-
0.1429
0.1429
0.1429
-
-
TV - LCD
-
-
-
-
-
-
-
0.0588
0.1765
0.1765
0.1765
0.1176
TV - LED
-
0.0750
0.0750
0.0750
0.0500
0.0625
0.0875
0.1000
0.1000
0.1000
0.0625
0.0250
Desktop
-
-
-
-
-
-
-
0.1429
0.5714
0.2857
-
-
Laptop
-
-
0.0179
0.0179
0.0179
0.0179
0.0179
0.1250
0.1250
0.1250
0.1250
0.0536
Rajbhandari et al.
283
Table 6: Time of use probability profile for household H3
Time of day
AM
12
1
2
3
4
5
6
7
8
9
10
11
LED
0.0052
0.0052
0.0243
0.1231
0.1127
0.0119
-
-
-
-
-
-
CFL
0.0052
0.0052
0.0243
0.1231
0.1127
0.0119
-
-
-
-
-
-
TFT
0.0052
0.0052
0.0243
0.1231
0.1127
0.0119
-
-
-
-
-
-
Bulb
0.0052
0.0052
0.0243
0.1231
0.1127
0.0119
-
-
-
-
-
-
Rice cooker
-
-
-
-
-
0.1111
0.3333
0.0556
-
-
-
-
Coil heater/hot plate
-
-
-
0.5000
-
-
-
-
-
-
-
-
Induction Cooktop
-
-
-
0.5000
-
-
-
-
-
-
-
-
Microwave
-
-
-
-
-
-
0.5000
-
-
-
-
-
Mixer blender
-
-
-
-
-
0.4615
0.1846
0.0769
-
-
-
-
Refrigerator
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
Water pump
-
-
0.1000
0.1000
0.1000
0.1000
-
-
-
-
0.1000
0.1000
Washing Machine
-
-
-
-
-
-
-
0.0645
0.1613
0.3226
0.3226
0.1290
Cloth Iron
-
-
-
-
0.0800
0.2000
0.1200
-
-
-
-
-
Fan
-
-
-
-
-
-
-
-
-
-
-
-
TV - CRT
-
-
-
-
-
-
-
-
-
-
0.2500
0.2500
TV - LCD
-
-
-
-
0.1667
0.1667
-
-
-
-
-
-
TV - LED
-
-
-
-
0.0400
0.0400
0.0400
0.0400
0.0600
0.0600
0.0400
0.0400
Desktop
-
0.0323
0.0323
0.0323
0.0323
0.1290
0.0968
0.0323
-
-
-
-
Laptop
0.0200
0.0200
0.0100
0.0400
0.0800
0.0500
0.0500
0.0400
-
-
-
-
Time of day
PM
12
1
2
3
4
5
6
7
8
9
10
11
LED
-
-
-
-
0.0171
0.0965
0.1839
0.1561
0.1130
0.0679
0.0052
0.0052
CFL
-
-
-
-
0.0171
0.0965
0.1839
0.1561
0.1130
0.0679
0.0052
0.0052
TFT
-
-
-
-
0.0171
0.0965
0.1839
0.1561
0.1130
0.0679
0.0052
0.0052
Bulb
-
-
-
-
0.0171
0.0965
0.1839
0.1561
0.1130
0.0679
0.0052
0.0052
Rice cooker
-
-
-
-
-
0.3333
0.1111
0.0556
-
-
-
-
Coil heater/hot plate
-
-
-
-
-
0.5000
-
-
-
-
-
-
Induction Cooktop
-
-
-
-
-
0.5000
-
-
-
-
-
-
Microwave
-
-
-
-
-
0.5000
-
-
-
-
-
-
Mixer blender
-
-
-
-
-
0.0769
0.2000
-
-
-
-
-
Refrigerator
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
Water pump
0.1000
-
0.1000
0.1000
0.1000
-
-
-
-
-
-
-
Washing Machine
-
-
-
-
-
-
-
-
-
-
-
-
Cloth Iron
-
-
-
-
-
-
0.2000
0.4000
-
-
-
-
Fan
-
-
-
-
-
0.3333
0.3333
0.3333
-
-
-
-
TV - CRT
-
-
-
-
-
-
0.2500
0.2500
-
-
-
-
TV - LCD
-
-
-
-
0.1667
0.1667
0.1667
0.1667
-
-
-
-
TV - LED
0.0400
0.0400
0.0400
0.0400
0.0600
0.0800
0.1000
0.1000
0.1000
0.0400
-
-
Desktop
0.0645
0.0645
0.0645
0.1290
0.0645
0.0645
0.0645
0.0323
0.0323
0.0323
-
0.0323
Laptop
-
-
-
0.0700
0.0800
0.1000
0.1000
0.1000
0.0900
0.0700
0.0200
0.0200
Demand Characteristics of Electricity in Residential Sector of Kathmandu Valley
284
Table 7: Time of use probability profile for household H4
Time of day
AM
12
1
2
3
4
5
6
7
8
9
10
11
LED
0.0143
0.0143
0.0074
0.0042
0.0083
0.0693
0.0499
0.0301
0.0030
0.0030
0.0030
0.0030
CFL
0.0143
0.0143
0.0074
0.0042
0.0083
0.0693
0.0499
0.0301
0.0030
0.0030
0.0030
0.0030
TFT
0.0143
0.0143
0.0074
0.0042
0.0083
0.0693
0.0499
0.0301
0.0030
0.0030
0.0030
0.0030
Bulb
0.0143
0.0143
0.0074
0.0042
0.0083
0.0693
0.0499
0.0301
0.0030
0.0030
0.0030
0.0030
Rice cooker
-
-
-
-
-
-
-
0.2143
0.2857
-
-
-
Coil heater/hot plate
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Induction Cooktop
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Microwave
-
-
-
-
-
0.0909
-
0.1818
0.1818
-
-
-
Mixer blender
-
-
-
-
-
-
-
0.4615
0.1846
0.0769
-
-
Refrigerator
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
Water pump
-
-
-
0.0645
-
0.1290
0.2581
0.0323
0.0968
0.0323
-
-
Washing Machine
-
-
-
-
-
-
-
-
-
0.0645
0.1613
0.3226
Cloth Iron
-
-
-
-
-
-
-
0.4286
0.4286
0.1429
-
-
Fan
0.0206
-
-
-
-
-
-
-
-
-
-
-
TV - CRT
-
-
-
-
-
-
-
-
0.1429
0.1429
-
-
TV - LCD
0.0345
-
-
-
-
-
-
0.0690
0.0690
0.0690
0.0690
0.0345
TV - LED
0.0210
-
-
-
-
0.0210
0.0378
0.0462
0.0546
0.0420
0.0210
0.0168
Desktop
-
-
-
-
-
0.0278
0.0278
0.0278
0.0278
0.0556
0.0556
-
Laptop
0.0667
0.0667
0.0242
-
-
0.0121
0.0303
0.0424
0.0364
0.0242
0.0303
0.0182
Time of day
PM
12
1
2
3
4
5
6
7
8
9
10
11
LED
0.0030
0.0030
0.0030
0.0030
0.0030
0.0030
0.0369
0.1986
0.1859
0.1836
0.1073
0.0601
CFL
0.0030
0.0030
0.0030
0.0030
0.0030
0.0030
0.0369
0.1986
0.1859
0.1836
0.1073
0.0601
TFT
0.0030
0.0030
0.0030
0.0030
0.0030
0.0030
0.0369
0.1986
0.1859
0.1836
0.1073
0.0601
Bulb
0.0030
0.0030
0.0030
0.0030
0.0030
0.0030
0.0369
0.1986
0.1859
0.1836
0.1073
0.0601
Rice cooker
-
-
-
-
-
-
-
0.3214
0.1786
-
-
-
Coil heater/hot plate
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Induction Cooktop
-
-
-
-
-
-
-
0.2500
0.2500
-
-
-
Microwave
-
-
0.0909
-
-
0.0909
-
-
0.1818
0.1818
-
-
Mixer blender
-
-
-
-
-
-
-
0.0769
0.2000
-
-
-
Refrigerator
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
Water pump
0.0323
0.0323
0.0323
-
-
0.0645
0.2258
-
-
-
-
-
Washing Machine
0.3226
0.1290
-
-
-
-
-
-
-
-
-
-
Cloth Iron
-
-
-
-
-
-
-
-
-
-
-
-
Fan
0.0206
0.0825
0.1134
0.1134
0.0928
0.0825
0.0928
0.0928
0.1031
0.0825
0.0825
0.0206
TV - CRT
-
-
0.1429
0.1429
-
-
-
0.1429
0.1429
0.1429
-
-
TV - LCD
-
0.0345
0.0345
0.0345
0.0345
0.0690
0.0862
0.0862
0.0862
0.0862
0.0690
0.0345
TV - LED
0.0294
0.0672
0.0714
0.0588
0.0462
0.0420
0.0546
0.0840
0.0924
0.1008
0.0630
0.0294
Desktop
-
-
-
0.0278
0.0278
0.0833
0.0833
0.1111
0.1389
0.1389
0.1389
0.0278
Laptop
0.0121
0.0242
0.0121
-
-
-
0.0364
0.0909
0.1091
0.1273
0.1273
0.1091
