Implementasi Model Predictive Control (MPC) pada sistem HVAC Skala Kecil untuk Optimasi Kinerja Termal dan Efisiensi Energi
DOI:
https://doi.org/10.32502/jse.v10i2.1691Keywords:
Model Predictive Control, HVAC, Efisiensi Energi, Sistem KendaliAbstract
Sistem Heating, Ventilation, dan Air Conditioning (HVAC) merupakan salah satu penyumbang konsumsi energi terbesar pada bangunan, sehingga diperlukan strategi kendali yang tidak hanya mampu menjaga kenyamanan termal tetapi juga meningkatkan efisiensi energi. Metode kendali konvensional PID memiliki keterbatasan dalam menangani kendala system dan perubahan kondisi lingkungan. Oleh karena itu, penelitian ini mengkaji implementasi Model Predictive Control (MPC) pada subsistem Air Conditioner (AC) pada HVAC skala kecil untuk mengoptimalkan kinerja termal dan konsumsi energi. Sistem HVAC dimodelkan sebagai sistem termal satu zona menggunakan pendekatan lumped parameter dan direpresentasikan dalam bentuk model state-space diskrit. MPC dirancang dengan fungsi objektif yang meminimalkan kesalahan pelacakan suhu terhadap setpoint dan perubahan daya pendinginan dengan memeprtimbangkan kendala operasional system. Pengujian dilakukan melalui simulasi menggunakan MATLAB dengan skenario pelacakan setpoint suhu dan gangguan lingkungan. Kinerja MPC dibandingkan dengan kendali PID konvensional berdasarkan respon suhu dan konsumsi energi. Hasil simulasi menunjukkan bahwa MPC mampu menghasilkan respons suhu yang stabil dengan konsumsi energi yang lebih rendah dibandingkan kendali PID. Secara kuantitatif, MPC membutuhkan energi sekitas 56,6 Wh, lebih rendah dibanding PID sebesar 58,5 Wh. Meskipun demikian, MPC memiliki waktu tunak yang sedikit lebih lama, yaitu sekitar 8 menit, dibandingkan dengan 6 menit pada kendali PID. Selain itu, MPC menghasilkan aksi kendali yang lebih halus, yang terlihat dari perubahan daya pendinginan yang lebih terkendali. Hasil ini menunjukkan bahwa MPC efektif sebagai alternatif kendali untuk meningkatkan kenyamanan termal dan efisiensi energi pada system HVAC skala kecil.
References
A. Afram, F. J.-S. (2017). Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems. Energy and Buildings, 96-113.
A. Parisio, E. R. (2014). A model predictive control approach to microgrid operation optimization. IEEE Transaction on Control Systems Technology, 1813-1827.
Abdul, A., & Farrokh, J.-S. (2014). Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment, 343-355.
Afram, A. J.-s. (2021). Artificial Neural network based model predictive control for HVAC systems. energy and Buildings.
Agency, I. E. (2019). Energy Efficiency in Buildings: Policies and Best Practices,. Paris: IEA Publications.
ASHRAE. (2017). ASHRAE Handbook: Fundamental. Atlanta: USA: ASHRAE.
Clarke, J. A. (2001). eneegy simulation in Building Design, 2nd ed. Oxford: UK: Butterworth-Heinemann.
E. F. Camacho and C. Bordons. (2013). Model Predictive Control, 2nd ed. London: Springer.
F. Oldewurtel, A. P. (2012). Use of Model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 15-27.
Hägglund, K. J. (1995). PID Controllers: Theory, Design, and Tuning, 2nd ed. USA:ISA: Research Triangle Park, NC.
J. B. Rawlings and D. Q. Mayne. (2009). Model Predictive Control: Theory and Design. Madison, WI: USA: Nob Hill Publising.
J. Drgoňa, D. A. (2020). All you need to know about model predictive control for buildings. Annual Reviews in Control, 190-232.
Juan, H., Haoran, L., Natasa, N., & Gongsheng, H. (2022). Model predictive control under weather forecast uncertainty for HVAC systems in University buildings. Energy & Buildings.
K. Ogata. (1995). Discrete-Time Control Systems, 2nd ed. Upper Saddle River, NJ: USA: Prentice Hall.
Killian, M. &. (2017). Implementasi of cooperative fuzzy model predictive control for an energy-efficient office buildings. Energy and Buildings, 158.
Kolokotsa, D. (2016). the role of smart grids in the building sector. Energy and Buildings, 703-708.
Luis, P.-L., Jose , O., & Christine, P. (2008). A review on buildings energy consumtion information. energy and buildings, 394-398.
M. Killian and M. Kozek. (2016). Ten questions concerning model predictive control for energy efficient buildings. Building and Environment, 403-412.
Md, A., Rajam, K., Anil, S. y., Ranjeet, K. A., & V.P., S. (2023). Recent developments trends in HVAC (heating, ventilation, and air-conditionging) systems: A comprehensive review. Materialstoday: Proceedings.
Panagiotis, M., lakovos, M., Federico, M., Hasan Huseyin, C., & Elias, K. (2025). Model Predictive Control for Smart Buildings: Applications and Innovations in Energy Management. Buildings, 1-49.
S. Wang and Z. Ma. (2008). Supervisory and optimal control of building HVAC systems: A review. HVAC&R Research, 3-32.
Sachidananda, S., & Maneesh, K. (2022). MPC based energy system for grid-connected Smart Buildinings with EVs. Emerging Technologies (GLobConET), IEEE IAS Global Conference on.
Saman, T., Panis, H., & Ali, R. (2022). Model Predictive control of Heating ventilation, and Air conditioning (HVAC) systems: A State-of-the-art review. Journal of Building Engineering.
Shiyu , Y., Man, P. W., & Chen, W. (2020). Model Predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. IET Electrical Systems in Transportastion.
Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control, 291-309.
Y. Ma, A. K. (2012). Predictive control for energy efficient buildings with thermal storage. IEEE Transactions on Control Systems Technology, 712-722.
Yaohua, T., Junchao, X., & Shen, Y. (2024). Energy Management Strategy Based on Model Predictive Control-Differential Evolution for hybrid energy storage system in Electric Vehicles. IET electrical Systems in Transportation, 1-13.
Yihang, L., Dang , R., Bin, Y., & Pengju, L. (2024). Energy-efficient control strategy for air conditioning and mechanical ventilation system based on occupant distribution-A case study on stratum ventilation. Journal of Building Engineering.
Yiqun, P., Mingya , Z., Yan, L., Yiqun, Y., Yumin, L., Ruxin, Y., . . . Xiaolei, Y. (2023). builidng energy simulation and its application for building performance optimization: a review of methods, tools, and case studies. advances in Applied Energy, 100135.
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