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Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic

Author

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  • Muhammad Fayaz

    (Department of Computer Engineering, Jeju National University, Jeju City 63243, Korea)

  • DoHyeun Kim

    (Department of Computer Engineering, Jeju National University, Jeju City 63243, Korea)

Abstract

Energy management in residential buildings has grabbed the attention of many scientists for the last few years due to the fact that the residential sector consumes the highest amount of total energy produced by different energy resources. To manage the energy in residential buildings effectively, an efficient energy control system is required, capable of decreasing the total energy consumption without compromising the user-preferred environment inside the building. In the literature, many approaches have been proposed to achieve the goals of minimizing the energy consumption and maximizing the user preferred comfort by keeping different parameters under consideration, but all these methods face some problems in resolving the issue properly. The bat algorithm is one of the most recently introduced optimization approaches that has drawn the attention of researchers to apply it for solving different types of optimization problems. In this paper, the bat algorithm is applied for energy optimization in residential buildings, which is one of the most focused optimization problems in recent years. Three environmental parameters, namely temperature, illumination and air quality are bat algorithm inputs and optimized values of these parameters are the outputs. The error difference between the environmental parameters and optimized parameters are inputs of the fuzzy controllers which give energy as output which in turn change the status of the concerned actuators. It is proven from the experimental results that the proposed approach has been effectively successful in managing the whole energy consumption management system.

Suggested Citation

  • Muhammad Fayaz & DoHyeun Kim, 2018. "Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic," Energies, MDPI, vol. 11(1), pages 1-22, January.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:161-:d:126124
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    References listed on IDEAS

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    Cited by:

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    3. Lei Zhang & Ying Yang, 2023. "Towards Sustainable Energy Systems Considering Unexpected Sports Event Management: Integrating Machine Learning and Optimization Algorithms," Sustainability, MDPI, vol. 15(9), pages 1-16, April.
    4. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    5. Sooyoun Cho & Jeehang Lee & Jumi Baek & Gi-Seok Kim & Seung-Bok Leigh, 2019. "Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach," Energies, MDPI, vol. 12(21), pages 1-23, October.
    6. Thang Trung Nguyen & Dieu Ngoc Vo & Hai Van Tran & Le Van Dai, 2019. "Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm," Complexity, Hindawi, vol. 2019, pages 1-28, May.
    7. Sana Iqbal & Mohammad Sarfraz & Mohammad Ayyub & Mohd Tariq & Ripon K. Chakrabortty & Michael J. Ryan & Basem Alamri, 2021. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment," Sustainability, MDPI, vol. 13(13), pages 1-23, June.
    8. Fazli Wahid & Muhammad Fayaz & Ayman Aljarbouh & Masood Mir & Muhammad Aamir & Imran, 2020. "Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms," Energies, MDPI, vol. 13(17), pages 1-26, August.

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