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Cell phones vs Landlines —Energy & Social Consequences Team # 3983 Feb 6, 2009

Summary The cell phone revolution has had a remarkable impact to the society nowadays, more and more people are likely to choose cell phone as their communicating tool rather than land phone, Cell phone bring people a lot of convenience indeed, but what is the consequence of this change? How will it affect the energy cost? Our goal is to establish appropriate models to present the using condition of cell phones and landlines in a perspective of energy to meet the requirement given. In Requirement 1, we use the model of Logistic Population Growth in predicting the growth of cell phone during the transition state. This idea gains a very satisfying result, which makes our prediction much more accurate. The problem in Requirement 2 is in fact about optimization, and the target is to minimize the total energy cost. Quantifying the social (hidden) consequences to energy cost is the key point. A brand-new method in a perspective of probability is used to solve it, the method offers a rational way to determine a person’s degree of demand to cellphone or landlines in different conditions based on a research report of people’s everyday life, and it is finally connected with energy costs reasonably. In Requirement 3 and 4, large amounts of accurate data and precise calculation contribute a lot to the high accuracy of our result. As for the prediction of the next 50 years in Requirement 5, the conclusion we get is that the tendency of the total energy consumption per year is decreasing mainly because of the decreasing in cell phone manufacturing costs.

Contents 1 Introduction

1

2 Notation

1

3 Model

2

3.1

3.2

3.3

3.4

3.5

Model for Requirement 1

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

3.1.1

Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

3.1.2

Creating the Model

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

3.1.3

Electricity Utilization Now . . . . . . . . . . . . . . . . . . . . . . . . . .

3

3.1.4

The Trend in a Few Years . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

3.1.5

Steady State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

3.1.6

Analysis of the Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

Model for Requirement 2

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

3.2.1

Previous Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

3.2.2

Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8

3.2.3

Special Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8

3.2.4

Creating the Model — A Problem of Linear Programming . . . . . . . . .

8

3.2.5

Analysis of the Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

Model for Requirement 3

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

3.3.1

Previous Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

3.3.2

Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

3.3.3

Special Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

3.3.4

Modeling & Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

3.3.5

Discussion & Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

Model for Requirement 4 — Leaking Electricity in Household Appliances . . . .

11

3.4.1

Previous Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

3.4.2

Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

3.4.3

Special Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

3.4.4

Creating the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

3.4.5

Collecting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

3.4.6

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13

3.4.7

Analysis of the Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13

Model for Requirement 5 — Prediction of the Future . . . . . . . . . . . . . . . .

13

3.5.1

Effect of Population and Economic Growth . . . . . . . . . . . . . . . . .

13

3.5.2

Creating the Model

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13

3.5.3

Conclusion—Cost Comes First . . . . . . . . . . . . . . . . . . . . . . . .

15

3.5.4

Analysis of the Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Further Discussion

16

5 Strengths & Weaknesses

17

6 Conclusion

17

Reference

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Introduction • Cell Phone Revolution What is cell phone revolution? It is a trend that cell phones have begun to contribute to the desertion of the landline service after years of coexistence between cells and landlines. According to an analysis from Mediamark Research Inc, MRI data show that the percentage of households with cell phones is still rising, as is the percentage of households with multiple cell phones. All indicators point to continued increases in the cell-only population. • Energy Waste and Energy Star Nowadays, energy crisis strikes all over the world. Cell phone revolution has some impact on the consumption of energy. What’s more, large amount of energy is wasted when some appliances are left plugged in. To save energy to the fullest extent possible, the United States Environmental Protection Agency created The Energy Star program in 1992 in an attempt to reduce energy consumption. • Our Approach In Requirement 1, we need to model the consequences of the cell phone revolution in the current US, during two periods: transition and steady state. In Requirement 2, our goal is to choose an optimal way of providing phone service to the country from an energy perspective. In Requirement 3, we need to model the energy costs of this wasteful practice for a Pseudo US based upon the best way in Requirement 2. In Requirement 4, our goal is to model the energy wasted by appliances in the current US in terms of barrels of oil per day. In Requirement 5, we need to predict the trend of telephone service of a typical Pseudo US over the next 50 years.

2

Notation • Etotal —the total energy cost • El —the total energy cost from landlines • Ec —the total energy cost from cell phones • Eccharge —the total energy cost from cell phones while charging • Ecchange —the total energy cost from cell phones when changed

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• m—the average number of members in each households • Pc —the power of a common cell phone charger • Pl —the power of a common landline phone • Tc —the average time of charging • Tl —the time of landlines working • Nc —the amount of cell phone in US • Nl —the amount of landlines in US • Ncchange —the amount of cell phone in US that needed to be changed in a period of time • Pe —the average electricity price per kW h • C—the average cost to produce a cell phone • K—the population of US

3

Model

3.1 3.1.1

Model for Requirement 1 Assumptions

• People pull out the plug immediately after the battery is full • Each person has 1 cell phone at most • The population is fixed

3.1.2

Creating the Model

Obviously, the energy consumption comes from two parts: landlines and cell phones, the latter also consist of two parts: energy cost from charging battery and changing cell phone

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(changing normally and because of lost or break are all included) .We can express it as follows: Etotal = El + Ec = El + Eccharge + Ecchange

(1) (2)

where, E l = Pl T l N l

(3)

Eccharge = Pc Tc Nc

(4)

Ecchange =

CNcchange × 3.6 × 106 Pe

(5)

In requirement 1 we consider the energy consumption in the period of one year. So in (3) , Tl should be the whole year because we know that landlines are always plugged in. In (5) , since the data of direct energy consumption of producing cell phone is not easy to gain, we consider the cost of one cell phone and transform it into the equal amount of electricity through the measurement of price (1kW h = 3.6 × 106 J). This transmission is reasonable because the process of producing cell phones can finally be seen as a process of consuming energy. Equation (1) to (5) can be used to calculate the energy cost in both the transition state and the steady state. Next we put the exact value into the formula above to work out the energy costs in recent years.

3.1.3

Electricity Utilization Now

• Pl —2.01W [3] • Tl —As we already said, Tl = 365 × 24 600s = 31 536 000s • Nl —We do not have the accurate number this year, we use the fitting curve in Figure 3 to estimate the number • Pc —2W [4] • Tc —3h every time, one charge every 2 days, so Tc = 1 971 000s • Nc —255 000 000[5] • C—$70 The cost of making a cell phone varies widely, we refer to many websites in order to present an average value[6][7][8] • Ncchange —People change their cell phone normally, meanwhile, they may lose or break the cell phone occasionally, we can look both instance as the same, after collecting lots of information related, we conclude that one cell phone is changed every 3 years. Equally,

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every year there are one third amount of cell phones in US need to be changed. So Ncchange = 255 000 000/3 = 85 000 000. • Pe —$0.1134[9] Through equation (1) to (5) , we work out that Etotal

3.1.4

now

= 1.97 × 1017 J

(6)

The Trend in a Few Years

What will the state be in a few years? First, we try to predict the amount of cell phones and landlines: In predicting cell phones, we use the idea from the model of Logistic Population Growth, because the rising curve of the amount of cell phone will finally come close to the upper limit of the population number, the trend fits this model, and the estimated result of the curve is like the shape of “S”. Here we get the number of cell phone consumers in recent years:[5][10]. We use the least squares method to get the parameters in the expression of the fitting curve. At last, the exact expression is: Nc (t) =

K 1+

K−N0 433.8−0.32t N0 e

(7)

where N0 = 349213 is the amount of cell phone in the earliest year available (1985), K = 305548183 is the population of US. Here is the graph:

Figure 1: The fitting curve of cell phone amount

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And the following is the amount of cell phones predicted (start from 2008) based on the graph above:

Figure 2: The trend of cell phone amount

Similarly, as for landlines, through the data we get[12], we find that the exact amount is increasing, too, but it will surely begin to decline after a few years due to the popularity of cell phones. So we think that the curve of Gaussian Distribution is reasonable to fit the data in order to predict the trend of landlines amount, the exact expression is: Nl (t) = 1.165 × 108 × e−(

x−2022 2 60.69

)

(8)

Figure 3 is the corresponding graph. And Figure 4 is the amount of landline phones predicted (start from 2000) based on the graph above. Now we have the amount of both landlines and cell phones from 2000 to 2030, so we can use (1) to (5) to see the trend of energy consumption, the exact expression is 7.3861 × 1015 e(

t−2020 2 ) 60.69

+

2.276 × 1017 1 + 3051989869e432.8−0.32t

And the corresponding graph is presented in Figure 5. 5

(9)

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Figure 3: The fitting curve of landline phone amount

Figure 4: The fitting curve of landline phone amount

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Figure 5: The trend of energy cost from 2000 to 2030

3.1.5

Steady State

For steady state, i.e. all landlines are replaced by cell phones, • Nc —305 548 183, that is to say every one has a cell phone • Ncchange —305 548 183/3 ≈ 101 850 000 Thus, we work out that Etotal

3.1.6

steady

= 2.28 × 1017 J

(10)

Analysis of the Result

From the result above, we can conclude that, under the circumstance in Requirement 1, the more cell phones, the more energy will cost.

3.2 3.2.1

Model for Requirement 2 Previous Words

First, we get the data of the average time people spend in primary activities per day from the website of United States Department of Labor [13], so we do the following assumptions in 7

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order to model Requirement 2 in a perspective of probability. In accord with Requirement 1, we consider the energy consumption in the period of one year.

3.2.2

Assumptions

• All people in US spend the same time in primary activities listed in [13] • People have the need to use phone only when they are awake (approximately 24 − 8.6 = 15.4h per day) • The so-called “optimal way of providing phone service” is the lowest energy cost can be reached under the precondition that all people are being kept well-informed by being available to use cell phone or landlines • People pull out the plug immediately after the battery is full

3.2.3

Special Notation

• K—The population in US: 305 548 183 • z—The total energy cost from cell phones and landlines per year • x—The amount of landlines • y—The amount of cell phones

3.2.4

Creating the Model — A Problem of Linear Programming

From [13], we can divide the state of a particular person awake into two parts: Being in a fixed condition (at home, in office, and so on) or in the moving state, the following is the time of the two states according to the data in [13]: State

Time

Fixed

11.4 ∼ 13.6h

Moving

1.8 ∼ 4h

We think that people can only use cell phones when they are moving, while they have the same probability to use landlines or cell phones when they are in a fixed condition described above, then we can easily get the probability of whether people use landlines or cell phones when they need to have or answer a call per day: From a perspective of probability, we can draw the following conclusion equally: 8

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Way landline cell

Probability 5.7 15.4 8.6 15.4

∼ ∼

6.8 15.4 9.6 15.4

• There are at least

5.7 K people need to keep well-informed wholly by landlines 15.4

• There are at least

8.6 K people need to keep well-informed wholly by cell phones 15.4

5.7 • The value of proportion of the amount of landlines and cell phones should be between 9.6 6.8 and 8.6 So, it is finally a problem of linear programming, combined with (1) to (5), the target function is z = 0.634 × 108 x + 7.45 × 108 y And the constrained conditions are  5.7   K x>   15.4  8.6 y> K  15.4   5.7 x 6.8   6 6 9.6 y 8.6

(11)

(12)

Our goal is to work out the minimum of z. The consideration above is based on the requirement that the lifestyle of people in “Pseudo US” should be the same as in real US, therefore, the constrained conditions above are materially the result of the so-called “hidden consequences”. By the operation of Lingo, We get the optimal amount of landlines and cell phones: x = 1.13 × 108 y = 1.71 × 108 z = 1.34 × 1017 J

3.2.5

(13)

Analysis of the Result

From the result above, we can see that both cell phone and landlines are needed. For convenience only, we hope more cell phones, but we must consider the energy-saving principle. Compared with the result in (6) and (10), we find that the energy cost in (13) is indeed lower, meanwhile, this way to provide phone service fit the social needs.

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3.3 3.3.1

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Model for Requirement 3 Previous Words

Apparently, it seems that cell phones account for very little piece in the whole consumption of electricity of a family. Many people do not care about leaving the recharger plugged in or charging their cell phones frequently. But in fact, leaking electricity for a long time can lead to a remarkable waste of energy. We are going to create the model to show this fact.In Requirement 3, we consider the energy consumption in a period of one day.

3.3.2

Assumptions

• As for people who charge their phone every night, they pull out the plug when they finish charging • The time people charge their phone every night is 8h

3.3.3

Special Notation

• p—The proportion of people who keep their cell phone rechargers plugged in for 24h • q—The proportion of people who charge their phone every night

3.3.4

Modeling & Calculation

We already said in 3.1.2 that on an average level, people charge their cell phone every 2 days, and 3h every time. Equally saying, only 1.5h charging time every day is effective, so we have the following formula: Ewasted = py × (24 − 1.5) × 3600 × Pc + qy × (8 − 1.5) × 3600 × Pc = 2.77 × 1013 p + 0.80 × 1013 q

3.3.5

(14)

Discussion & Conclusion

In the equation above, only the proportion p and q are not easy to gain. The best way to get relatively accurate values is to do a survey among various people in US. Unfortunately, we haven’t find this kind of data, but it is certain that the quantity class of Ewasted is at 1011 to 1012 J (p and q only occupy a little amount of the whole). We transform the energy in terms of oil (1 barrel crude oil can produce about 6.1178632 × 109 J energy[14]) , at last, the quantity class of oil barrels is at 102 to 103 . 10

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3.4 3.4.1

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Model for Requirement 4 — Leaking Electricity in Household Appliances Previous Words

• What is Standby Power? Large amounts of household electronic equipment continue to consume electricity even after they have been switched off. The electric power consumed by appliances while they are switched off or in a standby mode is called standby power. • Our Approach In fact, the rechargers of most household appliances are left plugged in while not charging the device. So there are quite a lot of energy wasted unconsciously. Our approach is to measure the energy wasted by the current US in terms of barrels of oil per day.

3.4.2

Assumptions

• We mainly consider the most energy-costing appliances in standby condition in households[11] • Apart from the time in use, all the appliances considered are in standby condition every day

3.4.3

Special Notation

• E—total energy wasted in US households per day • Ei —energy cost of a particular kind of appliance in standby condition per day • Pi —power of a particular kind of appliance in standby condition • Ti —time in use per day • Ni —total amount of a particular kind of appliance in US i = 1, 2, · · · , 6

1=TV 2=Set-top/DVR 3=PC 4=Telephony 5=Air-conditioner 6=VCR

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Creating the model

We have the following formula: E=

=

6 X i=1 6 X

Ei + Ewasted Pi (24 × 3600 − Ti )Ni + Ewasted

(15)

i=1

Where Ewasted refers to the energy wasted by cell phones which has been worked out in 3.3.

3.4.5

Collecting Data

• Standby Power Pi It’s not easy to collect the data needed by measuring a variety of appliances in the US household ourselves. Fortunately, this work has already been done by Alan Merier[1999]and J.P. Ross[2000]. Later, the Lawrence Berkeley National Laboratory measured many products during a recent project. So the accurate data of standby power are available. We will list them in Table 1 below. • Time in Use Ti We can get the accurate data from [15], which shows the using time of TV, DVR and PC in US. Since the average using time of telephony, air-conditioners and VCR is very little per day, we can regard them as always being in a standby condition the whole day. • Amount Ni We can also collect the amount of TV, DVR PC and cell phone according to [15] and the average members in each household in US (2.7 on average) [16]. The data of other selected appliances in US can be found from [17][18].

Appliance

Time in use (h)

Average Standby Power (W )

Amount in US (million)

TV

0.00

5.90

104.28

Set-top box/DVR

4.24

2.88

334.26

VCR

0.18

36.68

21.00

Computer

0.88

14.65

222.84

Telephony

0.00

3.50

91.40

Air-conditioner

0.00

4.30

103.14

Table 1: The Data Collected

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3.4.6

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Conclusion

Similarly, by using the data above and the result in the model of Requirement 3, we can work out the total energy consumption by household appliances in US is about 86 000 barrels of crude oil per day.

3.4.7

Analysis of the Result

The Energy Information Administration[20] shows that the total residential electricity use in US is 3.78 billion kW h per day, equals to 2224306.03 barrels of crude oil. According to the result, we can get the conclusion that leaking electricity is responsible for about 3.86% of residential electricity use in US. The percent is lower than before1 . The reason is that an Energy Star Project–a government-backed program has been launched to help businesses and individuals protect the environment through superior energy efficiency[22]. It contributes to the drop of power when a particular household appliance is in standby conditionwhich has an active effect in preventing the waste of energy a lot.

3.5 3.5.1

Model for Requirement 5 — Prediction of the Future Effect of Population and Economic Growth

Our model which is used to predict the development of phone service for the next 50 years is mainly based on the model we have founded in Requirement 2. We conclude the consequences of population and economic growth into two aspects: • Population growth leads to the growth of K, thereby affects the constrained conditions (12), finally affects the minimum of z • Economic growth contributes to the development of technology, consequently cause the drop of the cost to produce cell phones, that is to say, C drops as the economy grows

3.5.2

Creating the Model

We sum equation (1) to (5) up and combine with the result of Ewasted in Requirement 3, then get z = Pc T c N c + Pl T l N l + 1

CNcchange × 3.6 × 106 + Ewasted × 365 Pe

The percentage in former time ranges from 5% to 26%[21]

13

(16)

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We use the population data from 1950 to 2008 in US[19] to draw the fitting curve in order to predict it in the future. After careful comparison, we choose the linear function to draw the fitting curve based on the least square method.

Figure 6: Trend of Population in US from 1950 to 2060

And the exact expression is K(t) = 2.533t − 4785

(17)

As for economic growth, we have already stated its relationship with C, now we want to quantify it. Here we use the average annual per capita GDP growth rate as the average annual declining rate of C. In order to reach our goal, we must predict GDP of the US in future, similarly, by using data in [19], we get the exact expression G(t) = 1777 × 1.0326t−1950

(18)

So the expression of GDP per capita should be g(t) =

G(t) 1777 × 1.0326t−1950 = K(t) 2.533t − 4785

The corresponding graph is

14

(19)

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Figure 7: Trend of GDP per capita in US from 1950 to 2060

Then through simple calculation, we can get the average annual per capita GDP growth rate from 2008 to 2060 r = 1.96%

(20)

So C drop from 2008 to 2060 at an average rate of r, i.e. C(t) = 70 × (1 − 1.96%)t−2008

(21)

Finally, we put (21) and other exact values into (16) (Among which, Nl = x, Nc = y, Ewasted = 5 × 1011 )2 and obtain the following formula 0.634 × 108 x + (7.40 × 108 × (1 − 0.0196)t−2008 + 500925)y The constrained conditions are

3.5.3

 5.7   x> K(t)   15.4  8.6 y> K(t)  15.4   5.7 x 6.8   6 6 9.6 y 8.6

(22)

(23)

Conclusion—Cost Comes First

Again, we find the minimum of z by Lingo, after transforming it into perspective of oil, we obtain the following graph of energy consumption in the next 50 years to provide phone service:

2

Ewaste is the average value of 1011 and 1012 , please refer to 3.3.5

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Figure 8: Trend of energy consumption in the future to provide phone service

Particularly, we list the barrels of oil cost every 10 years for the next 50 years below: Year

2008

2010

2020

2030

2040

2050

2060

Barrels per year (million)

2.157

2.112

1.900

1.702

1.522

1.360

1.216

Table 2: Energy consumption predicted every 10 years for the next 50 years

3.5.4

Analysis of the Result

The result shows that the energy consumption declines as time goes, that is to say the drop of the cost to produce cell phones is the main factor in determining energy consumption to provide phone service in the future, while the energy cost due to the increasing amount of cell phones accounts for less part. It infers that lowering the producing cost is also an effective way to save energy, or in another perspective, keeping your cell phones in a good condition and do not change it frequently can also save a lot of energy.

4

Further Discussion In this article, we compared the whole energy consumption of cell phones and landlines. In

fact, the cell phones have many functions that landlines do not allow. To be fairer, we should compare their energy consumption in the same aspect—Only in telephone service. So we should 16

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modify the measurement of the energy consumption of cell phones and landlines as follows: Assumption • The percentage of the energy used in mobile Internet is a% • The percentage of the energy used in mobile video is b% So the exact energy used in the mobile telephone service is Eccharge = (1 − a% − b%)Pc Nc Tc

5

(24)

Strengths & Weaknesses • Strengths 1. We use the model of Logistic Population Growth to fit the trend of the cell phone growth successfully, therefore predict the amount of cell phone more accurately. 2. Quantify the hidden consequences of cell phones and landlines by studying people’s everyday lifestyle from a perspective of probability to work out an optimal way to provide phone service. 3. Apart from the direct energy cost in charging the cell phone, we also consider the cost in producing cell phone, and this aspect is indeed a large scale of energy cost, which can be proved by the result of our model. • Weaknesses 1. We don’t consider the percentage of people who have the both “bad habits”: charging their cell phones every night and leaving the recharger plugged in every day in Requirement 3. 2. Some data we need is not available directly, sometimes we have to use other related data to estimate it, which may cause error.

6

Conclusion According to the five requirements discussed above, we can draw the conclusion that The Cell

Revolution has a big impact on the energy consumption. And some of the household appliances can also waste the energy. With the development of the technology and economy in US, the total energy consumption is decreasing. With the conclusion, we suggest that the correlative government should first take action to strengthen the public consciousness of saving energy to reduce the amount of energy waste. 17

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References [1] The population of US http://www.census.gov/popest/national/asrh/2007-nat-res.html [2] Households with landlines http://www2.heartland.org/apps/images/imgPics/Wireless.jpg [3] Standby Power Summary Table http://standby.lbl.gov/summary-table.html [4] Average power of cellphone charger http://enviroplug.com/statistics.php [5] Total cellphone in use now in US http://www.clickz.com/3628985 [6] Cost of cell phone http://www.costhelper.com/cost/electronics/cell-phone.html [7] Cost of cell phone http://wiki.answers.com/Q/How much does it cost to make a cell phone [8] Cost of cell phone http://gizmodo.com/gadgets/cellphones/iphone-only-costs-250-to-makerest-of-price-is-fanboy-tax-229664.php [9] Residential Average Retail Price of Electricity in US http://www.eia.doe.gov/cneaf/electricity/epm/table5 6 b.html [10] The number of cell phone customers in US http://www.infoplease.com/ipa/A0933563.html [11] Whole-House Measurements of Standby Power Consumption. J.P. Ross University of California, Berkeley, USA, Alan Meier, Lawrence Berkeley National Laboratory, USA, 2000 [12] The amount of landline phone in US http://www.census.gov/hhes/www/housing/census/historic/phone.html [13] The average time people spent in primary activities per day http://www.bls.gov/news.release/atus.t01.htm [14] The energy of one barrel crude oil http://en.wikipedia.org/wiki/Barrel of oil equivalent [15] Nielsen’s three screen report http://www.nielsen.com/pdf/3 Screen Report May08 FINAL.pdf [16] Number of Households in US http://www.economagic.com/em-cgi/data.exe/cenHVS/table13c01 [17] Amount of appliance in US http://www.eia.doe.gov/emeu/recs/recs2005/c&e/airconditioning/pdf/alltables1-11.pdf [18] Amount of appliance in US http://www.census.gov/prod/2005pubs/p23-208.pdf [19] Trend of population in US http://www.data360.org/dsg.aspx?Data Set Group Id=1385

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[20] Residential electricity use http://www.eia.doe.gov/fuelelectric.html [21] Whole-House Measurements of Standby Power Consumption. J.P. Ross. University of California, Berkeley, USA. Alan Meier. Lawrence Berkeley National Laboratory, USA. [22] Energy star http://en.wikipedia.org/wiki/Energy Star

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