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Simulation and Energy Management in Smart Environment Using Ensemble of GA and PSO

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Abstract

Smart, intelligent and sustainable power consumption model in residential sector received attraction of the researchers in last couple of years. Numerous techniques have been implemented for green and smart power management but the problem of minimum power consumption without compromising user comfort in green buildings is a big challenge to the researchers. In the past, we have presented power consumption optimization models for green buildings which are constructed on principles of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Up-to some extent, the previous intelligent models accomplished better output results, but the results shows that there is space to improve the results furthermore. In this paper, we propose an advanced energy optimization and energy control model based on multiprocessing, ensemble of PSO and GA named Advanced Energy Optimization (AEO) to provide better occupants comfort index and efficient power utilization of the energy sources. The focus of the proposed AEO model is to maximize occupant’s indoor comfort and minimize power consumption. The paper also emphases on the application of a simulator to control the actuators and update the indoor environment. The proposed AEO intelligent building model delivers power efficient green environment by minimizing power utilization and enhancing occupant’s comfort as opposed to GA based power consumption model (GAP). The proposed AEO model also provides better comfort index as compared to GAP, Single Optimization with Hybrid Prediction (SOHP), PSO and Ant Bee Colony with Knowledge Base (ABCKB) models. The results shows the usefulness of the proposed AEO model in reducing consumed power and improving the user’s comfort as compared to existing models. The model also control the building actuators based on the control information’s provided by the model.

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Abbreviations

T:

Temperature

L:

Illumination

A:

Air-quality

CP:

Consumed power

AP:

Adjusted power

USP:

User set parameters

SCP:

Smooth consumed power

\(\mathbb{G}\) :

Total number of generations

η:

Number of successive generations

β1, β2, β3 :

Consumer defined elements [0, 1]

eT :

Error difference in temperature

ceT :

Change in error difference in temperature

eL :

Error difference in illumination

eA :

Error difference in air-quality)

Tset, Lset, Aset :

User set parameters

AE:

Power source in total (outside and inside energy sources)

RP:

Required power to building

ME:

Maximum energy provided by the outside or inside energy sources

PCP:

Predicted consumed power

OF:

Objective function

R:

Measurement noise covariance

Q:

Process noise covariance

j:

Time

θ:

Weight element

d:

Operation energy for air-quality

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Acknowledgements

This resarch was supported by Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M3F2A1073387), and this research was supported by Institute for Information & Communication Technology Planning and Evaluation (IITP) grant funded by the Korea Government (MSIT) (No.2019-0-01456. AutoMaTa: Autonomous Management framework based on Artificial Intelligent Technology for adaptive and disposible IoT). Correspondence: Do-Hyeun Kim.

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Authors and Affiliations

  1. Department of Software Engineering, The University of Lahore, Lahore, Pakistan

    Safdar Ali

  2. Department of Computer Engineering, College of Engineering, Jeju National University, Jeju City, Republic of Korea

    Do-Hyun Kim

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Correspondence to Safdar Ali.

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Ali, S., Kim, DH. Simulation and Energy Management in Smart Environment Using Ensemble of GA and PSO. Wireless Pers Commun 114, 49–67 (2020). https://doi.org/10.1007/s11277-020-07349-4

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