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Optimized Power Control Methodology Using Genetic Algorithm

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Abstract

Providing an energy efficient environment to the occupants of the residential buildings is an interesting area of research. In the literature a number of techniques have been proposed for energy management, but the trade-off between users comfort index and energy consumption is still a challenge and unsolved. Previously we have proposed PSO based power control methodology. Our technique achieved good performance up-to some extent. In this paper, we propose an improved optimized power control methodology for occupants comfort index, energy saving and energy prediction using genetic algorithm (GA). Our proposed GA based optimized technique improved the occupants comfort index and consumed minimum power as compare to our previous work. Here our focus is to increase occupants comfort index, minimize energy consumption and comparison of power consumption using GA and PSO based predicted systems. GA based predicted system consumed less power as compare to its counterpart PSO based predicted system. The output and comparative results show the efficiency of the proposed method in increasing the occupant’s comfort index and minimizing energy consumption.

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Abbreviations

T :

Temperature

L :

Illumination

A :

Air-quality

USP :

User set parameters

AP :

Adjusted power

CP :

Consume power

RP :

Required power

Ω :

No of generations

µ :

Few successive generations

β 1 , β 2 , β 3 :

User defined factors [0, 1]

e T , ce T , e L , e A :

Error in (temperature, change in temperature, illumination, air-quality)

T set , L set , A set :

(User set parameters for temperature, illumination and air-quality)

J 1 , J 2 , J 3 :

Small values between ‘0’ and ‘1’

P (k):

Sum of required power

P available (K):

Total energy source (external and internal power sources)

P max (k):

Maximum power provided by the external or internal power sources

NH, NM, NL, ZE, PL, PM, PH :

Negative/positive (high, medium, and little), zero

HS, MS, BS, SH, LH, MH :

Small (high, medium, basic), high (little, medium)

OMS, OBS, OOK, OSH, OH, OL, OLH :

Output (MS, BS, OK, SH, H, L, LH)

P :

Consume power

PCP :

Predicted consumed power

γ T , γ L , γ A :

Temperature, illumination and air-quality increment relationship with consume power P

K :

Time

\( \vartheta \) :

The weight factor

d :

Operation power of ventilator

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Acknowledgments

This work was supported by ETRI through Maritime Safety & Maritime Traffic Management R&D Program of the MLTM/KIMST (Development of u-VTS for Maritime Safety). “This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0015009)”.

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Authors and Affiliations

  1. Department of Computer Engineering, Jeju National University, Jeju, Republic of South Korea

    Safdar Ali & Do-Hyeun Kim

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  1. Safdar Ali
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Correspondence to Do-Hyeun Kim.

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Ali, S., Kim, DH. Optimized Power Control Methodology Using Genetic Algorithm. Wireless Pers Commun 83, 493–505 (2015). https://doi.org/10.1007/s11277-015-2405-3

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