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Energy Saving Through Smart Home

By M.VINOD KUMAR 09H71A0256 K.V.P.CHAND 10H75A0206

EEE, B-tech IV year

MIC College of Technology
Kanchikacherla

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Abstract-Energy saving is considered as one of the most important issue affects the consumers, power system quality and the global environment. The high energy demanded by home appliances, air conditioning and lighting makes homes to be considered as one of the most critical area for the impact of energy consumption. Smart home technology is a good choice for people not only care about security, comfort but energy saving as well. In this paper, a smart home energy management technique based on a set of sensors is presented. It minimizes the domestic energy waste and can be adapted according to the user habits. A proposed scenario is reported of daily routine and performed by 16 steps. Three assumptions of varying the time spent in each step according to different behaviour are examined. The effectiveness of the proposed set is shown based on a static correlation between the power consumption and saving. Keywords- Energy saving, smart home, occupancy sensor.

I. INTRODUCTION
Smart home technology started for more than a decade to introduce the concept of device and equipment networking in house. Smart home contains internal network and intelligent control on different home's services. The internal network can be built via wire or wireless communication technique between sensors and actuators .The intelligent control means the entire house is managed or monitored by internet services. Smart home is the integration of technology and services through home networking for a better quality of living. Integrating the home services as shown in Figure 1 allows them to communicate with one another through the home controller, thereby enabling single button to control the various home systems according to preprogramed scenarios or operating modes. Smart homes have the potential to improve home comfort, convenience, security and energy management. Moreover it can be used for elder people and those with disabilities, providing safe and secure environments. A smart home is a good Choice for people caring about security, health, energy saving and convenience. The benefits of smart technology at home could be apparent to everyone if this potential is fulfilled. This is when the system will be able to protect habitant's privacy and having low cost. On the other hand, smart home is somewhere difficult to be implemented due to its high initial cost. Another disadvantage is that elder are more

reluctant to try new things or change their way of thinking about the risk of losing privacy due to their feeling of being monitored all the time.

Figure (1): .Smart home Integration services

In this paper a home energy management technique based on a set of sensors is presented that can be adapted according to habitant's behaviour. Section I is a brief definition of smart home and its benefits. Section II describes how smart home can reduce the energy consumption via managing intelligently the devices by controlling the lighting, air conditioning (HVAC) and other home appliances. In section III, a case study is reported. Section IV shows the proposed scenario. Section V explains the calculations and the numerical results to provide examples of possible advantages achievable with the proposed system in term of energy consumption.
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II. ENERGY MANAGEMENT
One of the major benefits of smart home to consumers is their ability to incorporate energy management features through lighting, air conditioning and home appliances. A. LIGHTING The lights in a smart home can be turned on and off automatically based on occupancy sensor. As example , when a person enters a room in the day time, the system will open the drapes instead of turning on the lights, but at night it would make sure the lights came on and they turned off when no one is in the room hence waste of energy can be preserved. B. AIR CONDITIONING An appropriate placement of temperature sensors and the use of heating and cooling timers can reduce the energy use and hence saving money and also the house can set to turn off air conditionings when no one is in the room. total Kitchen 129 16 Dining Room 16 Bathroom 16 Room 48 Bedroom 25 Entrance hall s Rooms Area/m:

Table 1 simulated home area The sensors are distributed into home in order to provide accurate information about the occupant's location and activities. Occupancy sensors are used instead of motion sensors because last ones are preferred for security rather than for building and lighting control. They respond only to moving objects, so if an individual in a room working at a desk, motion sensors will often cease to see him. Figure 3 describes examples of the two types of occupancy sensors distributed into the model .The first one is a passive infrared (PIR) sensor that automatically control lights by detecting the heat from occupants moving within an area (900 square feet) to determine when the space is occupied with a low cost. The other, is used to adjust the temperature and lighting level accordingly for better energy management with a high cost. Table 2 illustrates the number of sensor used per room and their distribution through the apartment is shown in figure 2.

C. HOME APPLIANCES

Smart homes can even go further in energy management by keeping track of the energy usage of each and every appliance in the house. The smart house controllers could schedule the operation of heavy power consuming appliances (such as dishwashers and electric water heaters) to take maximum advantage of off peak electric rates.

III. CASE STUDY
Figure 2 shows the apartment which consists of 6 rooms. Their dimensions are listed in table 1.

Figure (3) (a): Occupancy sensor for lighting (b): Occupancy sensor for lighting and HVAC
Total Kitchen Dining Bath- Living Bedroom room room room

Hall Target 1 0 Light Control Light +HVAC

Sensor type

3 3

1 0

0 1

1 0

5 1

0 1

Table 2 list of sensor per room

Figure (2): Layout of apartment indicating the distribution of sensors used (Sweet Home 3D software)

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IV. PROPOSED SCINERIO Scenario management allows users to define a set of behaviour rules. A lot of sequences can be implemented according to the habitant's age and its social status. A scenario for an elderly house holder who has a high risk of falling down is proposed in [11]. Furthermore, a scenario for an individual gets back at evening and leaving in the morning is suggested in [12]. In this paper a scenario for a man and his wife getting back home and leaving in morning is presented.
Wrong Behaviours Action Hall is On Hall is left on for 30 minutes Bedroom is On Husband Wife Time At 14 Back home 1 o'clock Enters 14-14:30 2 bedroom At 14:30 Enters 14:30-15 bathroom At 15 o'clock Enters 15-17 4 kitchen At 17 o'clock 6

Actually, the human habit cannot be controlled because it changes according to our life so the change of power consumption is related to the change of numbers of hours spent in each room and thus saving in KWH. The previous scenario listed in table 3 is adjusted three times and table 4 represents the number of hours spent in each room to realize each step of the 16 steps shown before during different habits assumptions.
Hour/step rd 3 assumption 1 0.5 0.5 2 0.5 0.5 0.5 0.5 0.5 1 1 2 1 0.5 0.5 1 Hour/step nd 2 assumption 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1 1 2 0.5 0.5 1.5 0.5 Hour/step st 1 assumption 1 0.5 0.5 l 0.5 0.5 0.5 0.5 0.5 1.5 1 3 0 0 0 0 Scenario Steps 1 2 3 4 s 6 7 8 9 10 11 12 13 14 15 16

Hall is Off Bedroom is left on for Bathroom is On 30 minute s Bedroom still On Bathroom bedroom Off Living room is left on Kitchen is On Living for 2 hours room is On Hall is On Living is On Bedroom is On Hall is Off Bathroom is On Bedroom is left on for Bedroom still On 30 minuets Living is On Bathroom. Bedroom Off Dining room is left on Kitchen is On Dining for 30 minutes is On Living is On Dining is On Dining room is left on Kitchen is On Dining for 1 hour room still On Living room is On Kitchen Dining room Living room are Off Bedroom is On Living is On Kitchen is On Dining is On Living room is On Husband back home He enters bedroom

waiting her husband in 17-17:30 living At 17:30 still He enters waiting her 17:30-18 bathroom husband in living At 18 o'clock He is Enters 18-18:30 waiting kitchen to her into prepare living launch They enter the dining 18:30-19 room for launch waiting her Enters the 19-20 into living kitchen room At 20 o'clock They enter bedroom 20-21 21-23 23-24

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Table 4 Time spent in each step V. NUMERICAL DISCUSSION RESULTS AND

9 10

11 12 13

Dining Room is left on for 1 hour

Dining Room is left on for 30 minutes

They enter living room waiting in Enters the dining kitchen to room prepare dinner Kitchen is Off Dining Enter the dining room room is on for dinner Kitchen is On Dining Back to Enters the still On living room kitchen Dining room Kitchen Living are Off Bedroom is On Back to bedroom to sleep All rooms are Off

24-24:30 24:30-1 At 1 o'clock 1-2 At 2 o'clock

14 15

16

Table 3.Proposed scenario

The whole apartment is simulated using Sweet Home 3D software according to the dimensions shown in table 1. Each room is simulated using DIALux software to calculate the power required for lighting. The air condition capacity was calculated according to the Canadian standards.[12] The home appliances capacity was calculated according to the U.S. Department of Energy's Office for Energy Efficiency and Renewable Energy (EERE).[13]. Figure 4 shows the living room as an example showing the number of lamps and total power required for lighting. Table 5 shows the power consumption of each room for lighting, air conditioning and home appliances in KWH/Day.

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coefficient is a single number that describes the degree of relationship between two variables that ranges from -1 to +1, indicating perfect negative correlation at -1, absence of correlation at zero, and perfect positive correlation at +1. Table 7 indicates the relation calculated between two random variables (X, Y) according to the correlation coefficient (r) ant its meaning. r = +1.0 r = + 0.5 r=0 r = -0.5 r = -1.0 Relationship Between X and Y Strong - Positive As X goes up. Y always also goes up Weak - Positive As X goes up: Y tends to usually also go up -No Correlation - X and Y are not correlated Weak - Negative As X goes up: Y tends to usually go down Strong - Negative As X goes up. Y always goes down

Figure (4): Living room lighting luxes
Total 129 0.896 7.94 16 0.133 0 Kitchen Dining Living Bathroom Bedroom Hall Room Room 2 16 16 48 25 8 Area/m Light 0.088 0.133 0.278 0.176 0.088 KWH/day HVAC 2.11 0 3.56 2.27 0 KHB/day Home 0 0 0.16 0.11 0 Appliances KWH/day

Table 7 Correlation coefficient meaning

Figure 6 shows that the relation between the power consumption (A) and the saving in KWH 1200.2 1.200 (B) is weak positive relation (r=0.55). This means that the great increase in power consumption Table 5 power consumption per room should not be accompanied by a great increase in Table 6 is a comparison between the previous assumptions listed in table 4 showing in each, the power consumption, power saving because the system could be adapted percentage saving in power and consequently in electricity according to occupant's behaviour. bill. The results shown in figure 5 confirm that the proposed energy management smart home can be adapted each time the occupant's habit change and thus the saving target.
3 assumption 2 assumption 1 assumption 1549.08 1858.23 935.31 Total KWH/month 619.2 379.47 224.13 Total saving KWH/month 38.58 % 20.42 % 23.96 % % saving/month 48.82 % 27.44 % 61.36 % Saving in LE % rd nd st

Table 6 Comparison between the different assumptions

Figure (5): The daily consumption, percentage saving in power and in electric bill for the previous trials.

Figure (6): (a): Correlation Coefficient (b): Correlation between monthly power consumption and saving

Figure 7 shows that the relation between the power saving(C) and the saving in cost (D) is One of the case study objectives is to evaluate the strong positive relation (r = 0.99). This means that relation between the power consumption and the the increase in power saving is accompanied by a saving. Thus Correlation statistical method is great increase in cost saving as shown in table 6. suggested for calculation. [14] A correlation
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L.E/year. The sensors cost about 570$ and communication network about 1000$ thus a total cost would be 1570$ which is equivalent to about 8949 L.E. Thus this initial cost would be covered in 3years. For sure, these values depend on occupant's habit as shown through the different assumption.

VI. CONCLUSION
In this paper a home energy management is presented based on a set of sensors to minimize the domestic energy waste according to human habits. A proposed scenario is suggested for daily routine to maximize the occupant's energy saving. The home power consumption is calculated and the rooms lighting are simulated using DIALUX software. The results are satisfactory and indicate that smart home based on a set of sensors could perform energy management which is not only an individual need but economical target. The relation between the power consumption and saving (power/cost) is illustrated using Excel. A strong relation between the saving in power and saving in cost is obtained.

Fig (7): (a): Correlation Coefficient (b): Correlation between saving in power consumption and cost The correlation coefficient between (A &B) and (C&D) are calculated using Excel software and then illustrated in figure 6 and 7. Table8 represents the daily power consumption and the saving in power daily and monthly showing also the saving in electricity bill for the 3rd assumption.
Total KWH/Day Electric Power Consumption Electric Power Saving Percentage Saving 51.636 20.064 38.85% Total Total KWH/Month KWH/Year 1549.08 619.2 39.95% 18588.96 7430.4 39.97% Budget in L.E. 515.08 284.178 44.82%

VII. REFREANCES
[1] B. Kim, S. Hong, Y. Jeong and D. Eom, The Study of Applying Sensor Networks to a Smart Home, Power Systems Computation Conference (PSCC), Sevilla, 2428 June 2002 [2] http://get-fuse.com/home automation.php [3] R. Robles and T. Kim Applications, Systems and Methods in Smart Home Technology: A Review, International Journal of Advanced Science and Technology, Vol. 15, February, 2010. [4] C. Bjorkskog , Human Computer Interaction in Smart Home, available on: http://www.hiit.fi/~oulasvir/58307110/smartho mes.pdf [5] http://www.id.carleton.ca/ web/ student %20 papers %20PDF/ inclusive %20technology / Isaza Shelton_ Diana_ SeminarPaper08.pdf

Table 8 .Third assumption calculations Table 8 records that only one family could save about 620 KWH /month. Taking into consideration that Egypt consist of about 17 million family according to the statistics of 2006,thus a total energy saving could be about 3000 MW which is equivalent to 3 power stations each is formed of 3 units and each unit capacity would be 300 MW. On the individual level, this family saves about 230 L.E/month which is equivalent to 2770.824

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