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Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings

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  • Homod, Raad Z.

Abstract

The most distinctive properties of the HVAC systems are their large-scale nonlinear systems that contain large thermal inertia, time variability, nonlinear constraints, uncertain disturbance factors, multivariate systems and coupled properties for both temperature and humidity. This paper considers a novel control algorithm that could handle such intricate characteristics by using hybridization layers between the physical parameters' memory and the neural networks' weight, which is well-structured by the Takagi-Sugeno-Kang Fuzzy inference strategy. The application of nonlinear regression to the offline hybrid layers construction and online fine-tuning methods are conducted by using the Gauss-Newton Method in order to achieve fast tuning operation. The feedforward strategy is adopted, so as to boost the stability of the overall system in addition to increasing the control precision and its response speed. Moreover, the effects of disturbances and uncertainty are eradicated by online tuning. The tracking control goal takes full advantage of mature strategies regarding the predicted mean vote (PMV) to address high thermal inertia, to save energy and to tackle coupling problem. The proposed control performance results are analysed and compared to hybrid PID cascade control, where both strategies are tested individually and simultaneously through the use on the HVAC system. The obtained results showed that Feedforward Hybrid Layers Control (FHLC) led to effective advantages regarding optimal performance, adaptation, precision and robustness. Furthermore, adopted the adaptive structural control algorithm for FHLC to improve indoor thermal comfort, whereas the significant energy reduction is achieved. The prospective scope for future work is to expand the control structure for full building control by adding more controlled elements, such as lighting, ventilation, security, fire protection and other building appliances.

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  • Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    • Handle: RePEc:eee:renene:v:126:y:2018:i:c:p:49-64
    DOI: 10.1016/j.renene.2018.03.022
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    References listed on IDEAS

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    Cited by:

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    3. Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
    4. Alexandre Correia & Luís Miguel Ferreira & Paulo Coimbra & Pedro Moura & Aníbal T. de Almeida, 2022. "Smart Thermostats for a Campus Microgrid: Demand Control and Improving Air Quality," Energies, MDPI, vol. 15(4), pages 1-21, February.
    5. Elena Korneeva & Nina Olinder & Wadim Strielkowski, 2021. "Consumer Attitudes to the Smart Home Technologies and the Internet of Things (IoT)," Energies, MDPI, vol. 14(23), pages 1-15, November.
    6. Shirazi, Peimaneh & Behzadi, Amirmohammad & Ahmadi, Pouria & Rosen, Marc A. & Sadrizadeh, Sasan, 2024. "Comparison of control strategies for efficient thermal energy storage to decarbonize residential buildings in cold climates: A focus on solar and biomass sources," Renewable Energy, Elsevier, vol. 220(C).
    7. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    8. Nam-Chul Seong & Jee-Heon Kim & Wonchang Choi, 2019. "Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms," Sustainability, MDPI, vol. 11(18), pages 1-12, September.
    9. Joanna Piotrowska-Woroniak & Tomasz Szul & Krzysztof Cieśliński & Jozef Krilek, 2022. "The Impact of Weather-Forecast-Based Regulation on Energy Savings for Heating in Multi-Family Buildings," Energies, MDPI, vol. 15(19), pages 1-30, October.
    10. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
    11. Li, Zening & Su, Su & Jin, Xiaolong & Chen, Houhe, 2021. "Distributed energy management for active distribution network considering aggregated office buildings," Renewable Energy, Elsevier, vol. 180(C), pages 1073-1087.
    12. Homod, Raad Z. & Togun, Hussein & Ateeq, Adnan A. & Al-Mousawi, Fadhel Noraldeen & Yaseen, Zaher Mundher & Al-Kouz, Wael & Hussein, Ahmed Kadhim & Alawi, Omer A. & Goodarzi, Marjan & Ahmadi, Goodarz, 2022. "An innovative clustering technique to generate hybrid modeling of cooling coils for energy analysis: A case study for control performance in HVAC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    13. Roberto Casado-Vara & Angel Martín del Rey & Ricardo S. Alonso & Saber Trabelsi & Juan M. Corchado, 2020. "A New Stability Criterion for IoT Systems in Smart Buildings: Temperature Case Study," Mathematics, MDPI, vol. 8(9), pages 1-13, August.
    14. V. S. K. V. Harish & Arun Kumar & Tabish Alam & Paolo Blecich, 2021. "Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability," Sustainability, MDPI, vol. 13(21), pages 1-26, October.
    15. Amjad Almusaed & Asaad Almssad & Raad Z. Homod & Ibrahim Yitmen, 2020. "Environmental Profile on Building Material Passports for Hot Climates," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    16. Zhu, Li & Chen, Sarula & Yang, Yang & Tian, Wei & Sun, Yong & Lyu, Mian, 2019. "Global sensitivity analysis on borehole thermal energy storage performances under intermittent operation mode in the first charging phase," Renewable Energy, Elsevier, vol. 143(C), pages 183-198.
    17. Aguilar, J. & Garces-Jimenez, A. & R-Moreno, M.D. & García, Rodrigo, 2021. "A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    18. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).

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