Optimization Control PID With QHBM (Queen Honey Bee Migration) Algorithm in Heating Ventilating and Air Conditioning (HVAC) System

Wisnu Wahyu Nugroho; Aripriharta Aripriharta; Sujito Sujito

doi:10.61132/ijmecie.v3i2.409

Authors

Wisnu Wahyu Nugroho Universitas Negeri Malang
Aripriharta Aripriharta Universitas Negeri Malang
Sujito Sujito Universitas Negeri Malang

DOI:

https://doi.org/10.61132/ijmecie.v3i2.409

Keywords:

Energy Optimization, HVAC System, Metaheuristic Algorithm, PID Controller, Rise Time

Abstract

Heating, Ventilating, and Air Conditioning (HVAC) systems often suffer from significant energy wastage due to their inability to adapt to real-time environmental changes, leading to high operational costs. Although Proportional-Integral-Derivative (PID) controllers are widely used for their simplicity and reliability, they struggle to handle the complex dynamics of modern environments, requiring advanced optimization to enhance efficiency. This study aims to optimize PID controllers by integrating the Queen Honey Bee Migration (QHBM) algorithm to improve HVAC performance, energy efficiency, and adaptability. The research method employs an experimental approach that compares the performance of conventional PID controllers with PID controllers optimized using the QHBM algorithm under dynamic environmental conditions. The results show that the PID-QHBM system significantly outperforms the conventional PID system, achieving a rise time of 0.2649 seconds and a settling time of 1.6874 seconds with an almost negligible steady-state error of 9.4991e-08. Although it experiences a slight overshoot of 16.3810%, the system stabilizes quickly and maintains the target temperature efficiently. In contrast, the conventional PID controller exhibits slower response characteristics, with a rise time of 1.3730 seconds, a settling time of 2.5144 seconds, and a larger steady-state error of 0.0361. This study demonstrates that integrating the QHBM algorithm into PID controllers provides a more effective solution for real-time temperature control, offering substantial improvements in energy efficiency and system performance. The findings contribute to advancing intelligent HVAC control systems that can better adapt to environmental variations while minimizing operational costs.

References

Abid, A., Barrak, A., Bakayoko, I., Jaafar, F., & Rancourt, D. (2025). Dependable software-optimized energy management for IoT-enabled smart buildings. In 2025 IEEE/ACM 7th International Workshop on Software Engineering Research & Practices for the IoT (SERP4IoT) (pp. 60–67). https://doi.org/10.1109/SERP4IoT66600.2025.00014

Adesanya, M. A., Obasekore, H., Rabiu, A., Na, W. H., Ogunlowo, Q. O., Akpenpuun, T. D., Kim, M. H., Kim, H. T., Kang, B. Y., & Lee, H. W. (2024). Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy optimization: A TRNSYS-Python cosimulation approach. Expert Systems with Applications, 252, Article 124126. https://doi.org/10.1016/j.eswa.2024.124126

Afroz, Z., Shafiullah, G. M., Urmee, T., Shoeb, M. A., & Higgins, G. (2022). Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm. Building and Environment, 209, Article 108681. https://doi.org/10.1016/j.buildenv.2021.108681

Águila-León, J., Vargas-Salgado, C., Vega-Gómez, C., & Lucero-Tenorio, M. (2025). Advanced optimization of artificial neural networks for a syngas power plant using bio-inspired optimization algorithms: Comparison between PSO, GA and a novel mosquito-inspired optimization algorithm. In 2025 IEEE Conference on Technologies for Sustainability (SusTech) (pp. 1–6). https://doi.org/10.1109/SusTech63138.2025.11025641

Akhtar, T., Khatoon, S., & Mahmood, A. (2024). Occupancy detection for HVAC systems using IoT edge computing and vision-based image processing. In Proceedings of the 2024 IEEE/ACM 17th International Conference on Utility and Cloud Computing (UCC 2024) (pp. 477–482). https://doi.org/10.1109/UCC63386.2024.00074

Alfatmi, K., Dhakad, H., Suryawanshi, K., Patil, K., & Patil, S. (2025). AI-driven smart HVAC management: An IoT and machine learning-based approach to energy-efficient building automation. In 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 207–211). https://doi.org/10.1109/ISPCC66872.2025.11039529

Ali, G., Aly, H., & Little, T. (2024). Automatic generation control of a multi-area hybrid renewable energy system using a proposed novel GA-fuzzy logic self-tuning PID controller. Energies, 17(9), Article 2000. https://doi.org/10.3390/en17092000

Cengiz, K. (2024). Optimizing energy efficiency in smart buildings through intelligent HVAC control systems. In Proceedings of the 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT 2024). https://doi.org/10.1109/ISMSIT63511.2024.10757235

Coccia, G., Mugnini, A., Polonara, F., & Arteconi, A. (2021). Artificial neural network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling. Energy, 222, Article 119958. https://doi.org/10.1016/j.energy.2021.119958

Du, H., Liu, P., Cui, Q., Ma, X., & Wang, H. (2022). PID controller parameter optimized by reformative artificial bee colony algorithm. Journal of Mathematics, 2022, Article 3826702. https://doi.org/10.1155/2022/3826702

Garces-Jimenez, A., Gomez-Pulido, J. M., Gallego-Salvador, N., & Garcia-Tejedor, A. J. (2021). Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: A comparative study. Mathematics, 9(18), Article 2181. https://doi.org/10.3390/math9182181

GhasemiGarpachi, M., Dehghani, M., Aly, M., & Rodriguez, J. (2025). Multi-objective improved differential evolution algorithm-based smart home energy management system considering energy storage system, photovoltaic, and electric vehicle. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3567888

Ghith, E. S., & Tolba, F. A. A. (2022). Design and optimization of PID controller using various algorithms for micro-robotics system. Journal of Robotics and Control (JRC), 3(3), 244–256. https://doi.org/10.18196/jrc.v3i3.14827

Ghoneim, R. S., Arabasy, M., & Abdulhadi, A. N. (2025). AI-driven optimization of indoor environmental quality and energy consumption in smart buildings: A bio-inspired algorithmic approach. Journal of Asian Architecture and Building Engineering. https://doi.org/10.1080/13467581.2025.2472742

Hu, Y., Liu, X., & Chen, H. (2024). Optimal tuning of multi-PID controller using improved CMOCSO algorithm. PeerJ Computer Science, 10, Article e2453. https://doi.org/10.7717/peerj-cs.2453

Huang, Y., & Li, C. (2021). Accurate heating, ventilation and air conditioning system load prediction for residential buildings using improved ant colony optimization and wavelet neural network. Journal of Building Engineering, 35, Article 101972. https://doi.org/10.1016/j.jobe.2020.101972

Janprom, K., Permpoonsinsup, W., & Wangnipparnto, S. (2020). Intelligent tuning of PID using metaheuristic optimization for temperature and relative humidity control of comfortable rooms. Journal of Control Science and Engineering, 2020, Article 2596549. https://doi.org/10.1155/2020/2596549

Khan, F. A., Ullah, K., ur Rahman, A., & Anwar, S. (2023). Energy optimization in smart urban buildings using bio-inspired ant colony optimization. Soft Computing, 27(2), 973–989. https://doi.org/10.1007/s00500-022-07537-3

Khan, O., Parvez, M., Seraj, M., Yahya, Z., Devarajan, Y., & Nagappan, B. (2024). Optimising building heat load prediction using advanced control strategies and artificial intelligence for HVAC system. Thermal Science and Engineering Progress, 49, Article 102484. https://doi.org/10.1016/j.tsep.2024.102484

Kumar Gautam, A., Kant Pareek, R., Jessie Rani R, H., & Solanki, V. (2024). Hybrid intelligent optimization techniques for grid integration with renewable systems: Review. E3S Web of Conferences, 540, Article 10009. https://doi.org/10.1051/e3sconf/202454010009

Lorenzini, G., Kamarposhti, M. A., & Ahmed Solyman, A. A. (2021). Optimization of PID controller parameters for automatic generation control in two-area heating system using firefly algorithm. International Journal of Safety and Security Engineering, 11(3), 213–222. https://doi.org/10.18280/ijsse.110301

Ma’Arif, A., Cahyadi, A. I., Herdjunanto, S., & Wahyunggoro, O. (2020). Tracking control of high order input reference using integral state feedback and coefficient diagram method tuning. IEEE Access, 8, 182731–182741. https://doi.org/10.1109/ACCESS.2020.3029115

Martinez, L., De, U., Rico, P. A. M., & Rico, P. (2022). Sustainable HVAC systems: Combining energy efficiency with renewable energy integration. Journal of Innovative Technologies, 5.

Nguyen, T. H., Nguyen, L. V., Jung, J. J., Agbehadji, I. E., Frimpong, S. O., & Millham, R. C. (2020). Bio-inspired approaches for smart energy management: State of the art and challenges. Sustainability, 12(20), 8495. https://doi.org/10.3390/su12208495

Patil, R. S., Jadhav, S. P., & Patil, M. D. (2024). Review of intelligent and nature-inspired algorithms-based methods for tuning PID controllers in industrial applications. Journal of Robotics and Control (JRC), 5(2), 336–358. https://doi.org/10.18196/jrc.v5i2.20850

Sarathkumar, D., Srinivasan, M., Stonier, A. A., & Vanaja, D. S. (2021). A brief review on optimization techniques for smart grid operation and control. In 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). https://doi.org/10.1109/ICAECA52838.2021.9675618

Sendra-Arranz, R., & Gutiérrez, A. (2020). A long short-term memory artificial neural network to predict daily HVAC consumption in buildings. Energy and Buildings, 216, Article 109952. https://doi.org/10.1016/j.enbuild.2020.109952

Shahir, A., Mokhtar, B., & Oktaviandri, M. (2022). PID controller for optimum energy efficiency in air-conditioner. International Journal of Electrical, Energy and Power System Engineering, 5(1).

Shaik, M. B., Reddy, K. S., Chokkanathan, K., Biabani, S. A. A., Shanmugaraja, P., & Denslin Brabin, D. R. (2024). A hybrid particle swarm optimization and simulated annealing with load balancing mechanism for resource allocation in fog-cloud environments. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3489960

Shashwat, S., Zingre, K. T., Thurairajah, N., Kumar, D. K., Panicker, K., Anand, P., & Wan, M. P. (2023). A review on bioinspired strategies for an energy-efficient built environment. Energy and Buildings, 296, Article 113382. https://doi.org/10.1016/j.enbuild.2023.113382

Swari, M. H. P., Handika, I. P. S., Satwika, I. K. S., & Wahani, H. E. (2022). Optimization of single exponential smoothing using particle swarm optimization and modified particle swarm optimization in sales forecast. In Proceedings of the IEEE 8th Information Technology International Seminar (ITIS 2022) (pp. 292–296). https://doi.org/10.1109/ITIS57155.2022.10010034

Tan, K. M., Babu, T. S., Ramachandaramurthy, V. K., Kasinathan, P., Solanki, S. G., & Raveendran, S. K. (2021). Empowering smart grid: A comprehensive review of energy storage technology and application with renewable energy integration. Journal of Energy Storage, 39, Article 102591. https://doi.org/10.1016/j.est.2021.102591

Ullah, I., Khitab, Z., Khan, M. N., & Hussain, S. (2019). An efficient energy management in office using bio-inspired energy optimization algorithms. Processes, 7(3), Article 142. https://doi.org/10.3390/pr7030142

Zhang, X., & Yang, Y. (2024). Optimization of PID controller parameters using a hybrid PSO algorithm. International Journal of Dynamics and Control. https://doi.org/10.1007/s40435-024-01455-y