Fuzzy Logic Controller Design for Intelligent Lighting and Air-Conditioning Management Systems

Authors

  • Nanda Novita Department of Informatics Engineering, Universitas Medan Area, Medan, Indonesia
  • Nurul Khairina Department of Informatics Engineering, Universitas Medan Area, Medan, Indonesia
  • Suendri . Department of Information System, Universitas Islam Negeri Sumatera Utara, Medan, Indonesia
  • Amir Saleh Department of Informatics Engineering, Universitas Prima Indonesia, Medan, Indonesia
  • Fadhillah Azmi Department of Electrical Engineering, Universitas Medan Area, Medan, Indonesia

Keywords:

Energy Efficiency, Fuzzy Logic Control, Lighting and AC Management, and Intelligent Systems

Abstract

In an effort to conserve energy and optimize the use of resources, this research explores the application of fuzzy logic control techniques to improve energy efficiency in intelligent lighting and air conditioning (AC) management systems. This research aims to investigate how fuzzy logic control strategies can be incorporated into intelligent systems to regulate lighting and air conditioning operations with greater precision, adaptability, and energy efficiency. By utilizing a fuzzy logic algorithm, this research develops a model that is able to dynamically adjust lighting levels and AC settings based on environmental conditions, housing patterns, and user preferences. Fuzzy logic control has proven useful for maintaining the desired level of comfort and optimizing electrical energy consumption through trials. The research results show that the integration of fuzzy logic control methodology offers significant potential to improve energy efficiency in lighting and air conditioning management systems, leading to reduced energy consumption and operational costs. These findings highlight the importance of intelligent control in the sustainable management of electrical equipment and provide valuable insights for the design and implementation of energy-efficient systems in a variety of contexts.

 

References

I. Dwisaputra, S. F. Sahita, and M. D. Rizky, “Energy Efficiency In Lighting Systems Using Fuzzy Logic Control,” Proc. Int. Conf. Sustain. Environ. Agric. Tour. (ICOSEAT 2022), vol. 26, pp. 465–469, 2023, doi: 10.2991/978-94-6463-086-2_63.

A. Chojecki, A. Ambroziak, and P. Borkowski, “Fuzzy Controllers Instead of Classical PIDs in HVAC Equipment: Dusting off a Well-Known Technology and Today’s Implementation for Better Energy Efficiency and User Comfort,” Energies, vol. 16, no. 7, 2023, doi: 10.3390/en16072967.

M. J. E. Salami, M. Rashid, and N. Mohammad, “Design and implementation of an intelligent fuzzy logic controller (FLC) for air handling unit (AHU) for smart house,” Aust. J. Basic Appl. Sci., vol. 5, no. 3, pp. 641–652, 2011.

G. Halhoul, M. Essaaidi, M. Ben, and B. Qolomany, “2104.02214”.

M. Chae, K. Kang, D. Koo, S. Oh, and J. Y. Chun, “Fuzzy Controller Algorithm for Automated HVAC Control,” Proc. 37th Int. Symp. Autom. Robot. Constr. ISARC 2020 From Demonstr. to Pract. Use - To New Stage Constr. Robot, no. Isarc, pp. 566–570, 2020, doi: 10.22260/isarc2020/0078.

R. R. Ramadhani, M. Yuliana, and A. Pratiarso, “Smart Room Lighting System for Energy Efficiency in Indoor Environment,” Int. J. Artif. Intell. Robot., vol. 4, no. 2, pp. 48–58, 2022, doi: 10.25139/ijair.v4i2.5266.

I. Soesanti and R. Syahputra, “A Fuzzy Logic Controller Approach for Controlling Heat Exchanger Temperature,” J. Electr. Technol. UMY, vol. 3, no. 4, pp. 117–124, 2019, doi: 10.18196/jet.3462.

Q. U. Ain, S. Iqbal, and H. Mukhtar, “Improving Quality of Experience Using Fuzzy Controller for Smart Homes,” IEEE Access, vol. 10, pp. 11892–11908, 2022, doi: 10.1109/ACCESS.2021.3096208.

K. N. Fauziah and F. Arifin, “Decision Support System for Major Selection in Higher Education for Multimedia Graduate Students using Fuzzy Mamdani Logic,” Elinvo (Electronics, Informatics, Vocat. Educ., vol. 8, no. 2, pp. 231–240, 2024, doi: 10.21831/elinvo.v8i2.57643.

Y. Barzegar, I. Gorelova, F. Bellini, and F. D’Ascenzo, “Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy),” Int. J. Environ. Res. Public Health, vol. 20, no. 15, 2023, doi: 10.3390/ijerph20156522.

R. Bhattacharyya and S. Mukherjee, “Fuzzy Membership Function Evaluation by Non-Linear Regression: An Algorithmic Approach,” Fuzzy Inf. Eng., vol. 12, no. 4, pp. 412–434, 2020, doi: 10.1080/16168658.2021.1911567.

C. Dumitrescu, P. Ciotirnae, and C. Vizitiu, “Fuzzy logic for intelligent control system using soft computing applications,” Sensors, vol. 21, no. 8, pp. 1–33, 2021, doi: 10.3390/s21082617.

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Published

2024-05-01

How to Cite

[1]
N. Novita, N. Khairina, S. ., A. Saleh, and F. Azmi, “Fuzzy Logic Controller Design for Intelligent Lighting and Air-Conditioning Management Systems”, IJIRCST, vol. 12, no. 3, pp. 81–86, May 2024.

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