ABSTRACT
The COVID-19 pandemic has influenced many aspects of human life, including working environments. Some research finds that there is a tendency to the increase of energy and CO2 emissions of large office buildings in developed countries, such as US and Europe’s top five economics, post-pandemic. Therefore, advanced heating, ventilation and air-conditioning (HVAC) technology that can reduce energy consumption in the building sector will yield a significant impact on the total national energy consumption. Many buildings equipped with conventional control in their HVAC control systems, such as PI or PID controls. Such controllers have drawbacks like unable to handle cross-coupling nature and constraints in a HVAC system. Conversely, model predictive control (MPC)—which belongs to advanced control—has the advantages when dealing with the system with constraints and uncertainties as it can take into account them in its optimization control problem formulation. This paper derived mathematically an industrial HVAC system based on Hammerstein-bilinear model—a model consists of a static nonlinearity followed by a dynamic bilinear subsystem. The obtained linear output-error (OE) models are subsequently used as plant models in the MPC design. The MPC controller performance is quite superior and proven to be able to meet the desired control objective (keeping the zone temperature in range of . In addition, the MPC controller gives more economic energy consumption (about save) than the PI one both for temperature and humidity control loop.
- Abdul Afram and Farrokh Janabi-Sharifi. 2014. Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72 (2014), 343–355. https://doi.org/10.1016/j.buildenv.2013.11.016Google ScholarCross Ref
- Fatih Birol. 2016. Tracking clean energy progress 2016. Technical Report. International Energy Agency.Google Scholar
- David Blum, Zhe Wang, Chris Weyandt, Donghun Kim, Michael Wetter, Tianzhen Hong, and Mary Ann Piette. 2022. Field demonstration and implementation analysis of model predictive control in an office HVAC system. Applied Energy 318(2022), 119104. https://doi.org/10.1016/j.apenergy.2022.119104Google ScholarCross Ref
- Jing Chen and Xiuping Wang. 2015. Identification of Hammerstein systems with continuous nonlinearity. Inform. Process. Lett. 115, 11 (2015), 822–827. https://doi.org/10.1016/j.ipl.2015.06.004Google ScholarDigital Library
- Nuno D. Cortiços and Carlos C. Duarte. 2021. COVID-19: The impact in US high-rise office buildings energy efficiency. Energy and Buildings 249(2021), 111180. https://doi.org/10.1016/j.enbuild.2021.111180Google ScholarCross Ref
- Nuno D. Cortiços and Carlos C. Duarte. 2022. Energy efficiency in large office buildings post-COVID-19 in Europe’s top five economies. Energy for Sustainable Development 68 (2022), 410–424. https://doi.org/10.1016/j.esd.2022.04.006Google ScholarCross Ref
- Feng Ding, Xiaoping Peter Liu, and Guangjun Liu. 2011. Identification methods for Hammerstein nonlinear systems. Digital Signal Processing 21, 2 (2011), 215–238. https://doi.org/10.1016/j.dsp.2010.06.006Google ScholarDigital Library
- Iput Kasiyanto. 2021. Disain Pengendali Prediktif pada Sistem HVAC Berbasis Model Hammerstein Bilinear. Master’s thesis. Universitas Indonesia, Depok, Indonesia. https://doi.org/10.13140/RG.2.2.21656.88325Google Scholar
- Iput Kasiyanto and Aries Subiantoro. 2020. Model Predictive Control of Precision Air Conditioning System with Secondary Condenser. In Proceedings of the 1st International Conference on Environmental Science and Sustainable Development, ICESSD 2019. EAI. https://doi.org/10.4108/eai.22-10-2019.2291465Google ScholarCross Ref
- Anthony Kelman, Yudong Ma, and Francesco Borrelli. 2013. Analysis of local optima in predictive control for energy efficient buildings. Journal of Building Performance Simulation 6, 3 (2013), 236–255. https://doi.org/10.1109/TSMC.1980.4308378Google ScholarCross Ref
- Tomasz M. Larkowski, Ivan Zajic, Keith J. Burnham, and Leszek Koszalka. 2014. Properties of input-output Hammerstein-bilinear structure with application to an industrial air handling unit. Journal of Physics: Conference Series 570, 6 (dec 2014), 062003. https://doi.org/10.1088/1742-6596/570/6/062003Google ScholarCross Ref
- Jan M. Maciejowski. 2001. Predictive Control with Constraints(1st. ed.). Pearson Education.Google Scholar
- Srijita Nundy, Aritra Ghosh, Abdelhakim Mesloub, Ghazy A. Albaqawy, and Mohammed M. Alnaim. 2021. Impact of COVID-19 pandemic on socio-economic, energy-environment and transport sector globally and sustainable development goal (SDG). Journal of Cleaner Production 312 (2021), 127705. https://doi.org/10.1016/j.jclepro.2021.127705Google ScholarCross Ref
- Ai Hui Tan. 2006. Wiener-Hammerstein modeling of nonlinear effects in bilinear systems. IEEE Trans. Automat. Control 51, 4 (2006), 648–652. https://doi.org/10.1109/TAC.2006.872759Google ScholarCross Ref
- Ivan Zajíc. 2013. A Hammerstein-bilinear Approach with Application to HVAC System. Ph. D. Dissertation. Coventry University, Coventry.Google Scholar
- Ivan Zajic, Tomasz M. Larkowski, Keith J. Burnham, and Dean Hill. 2012. Temperature Model of an Industrial Air Handling Unit and Manufacturing Zone. IFAC Proceedings Volumes 45, 16 (2012), 656–661. https://doi.org/10.3182/20120711-3-BE-2027.00349 16th IFAC Symposium on System Identification.Google ScholarCross Ref
- Ivan Zajic, Tomasz M. Larkowski, Malgorzata Sumislawska, Keith J. Burnham, and Dean Hill. 2011. Modelling of an Air Handling Unit: A Hammerstein-bilinear Model Identification Approach. In 2011 21st International Conference on Systems Engineering. 59–63. https://doi.org/10.1109/ICSEng.2011.19Google ScholarDigital Library
Index Terms
- Advanced Control for Hammerstein-bilinear HVAC System
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