IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v36y2011i5p2440-2449.html
   My bibliography  Save this article

Multi-objective optimization of HVAC system with an evolutionary computation algorithm

Author

Listed:
  • Kusiak, Andrew
  • Tang, Fan
  • Xu, Guanglin

Abstract

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables — supply air temperature and supply air duct static pressure set points — are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system.

Suggested Citation

  • Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:5:p:2440-2449
    DOI: 10.1016/j.energy.2011.01.030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544211000314
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2011.01.030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ke, Yu-Pei & Mumma, Stanley A., 1997. "Optimized supply-air temperature (SAT) in variable-air-volume (VAV) systems," Energy, Elsevier, vol. 22(6), pages 601-614.
    2. Zheng, G.R. & Zaheer-Uddin, M., 1996. "Optimization of thermal processes in a variable air volume HVAC system," Energy, Elsevier, vol. 21(5), pages 407-420.
    3. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    4. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    5. Yu, F.W. & Chan, K.T., 2005. "Experimental determination of the energy efficiency of an air-cooled chiller under part load conditions," Energy, Elsevier, vol. 30(10), pages 1747-1758.
    6. Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.
    7. Mathews, E. H. & Arndt, D. C. & Piani, C. B. & van Heerden, E., 2000. "Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort," Applied Energy, Elsevier, vol. 66(2), pages 135-159, June.
    8. Zaheer-Uddin, M., 1994. "Intelligent control strategies for HVAC processes in buildings," Energy, Elsevier, vol. 19(1), pages 67-79.
    9. Tashtoush, Bourhan & Molhim, M. & Al-Rousan, M., 2005. "Dynamic model of an HVAC system for control analysis," Energy, Elsevier, vol. 30(10), pages 1729-1745.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kostevšek, Anja & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Papa, Gregor & Petek, Janez, 2016. "The concept of an ecosystem model to support the transformation to sustainable energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1460-1469.
    2. Leehter Yao & Jin-Hao Huang, 2019. "Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center," Energies, MDPI, vol. 12(8), pages 1-16, April.
    3. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
    4. Yu-Fang Wang, 2020. "Adaptive job shop scheduling strategy based on weighted Q-learning algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 417-432, February.
    5. Wang, Xinli & Cai, Wenjian & Lu, Jiangang & Sun, Youxian & Zhao, Lei, 2015. "Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm," Energy, Elsevier, vol. 82(C), pages 939-948.
    6. Mahbub, Md Shahriar & Cozzini, Marco & Østergaard, Poul Alberg & Alberti, Fabrizio, 2016. "Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design," Applied Energy, Elsevier, vol. 164(C), pages 140-151.
    7. Wang, Xinli & Cai, Wenjian & Yin, Xiaohong, 2017. "A global optimized operation strategy for energy savings in liquid desiccant air conditioning using self-adaptive differential evolutionary algorithm," Applied Energy, Elsevier, vol. 187(C), pages 410-423.
    8. Zhen Yang & Jinhong Du & Yiting Lin & Zhen Du & Li Xia & Qianchuan Zhao & Xiaohong Guan, 2022. "Increasing the energy efficiency of a data center based on machine learning," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 323-335, February.
    9. Iivo Metsä-Eerola & Jukka Pulkkinen & Olli Niemitalo & Olli Koskela, 2022. "On Hourly Forecasting Heating Energy Consumption of HVAC with Recurrent Neural Networks," Energies, MDPI, vol. 15(14), pages 1-20, July.
    10. Nikos Kampelis & Elisavet Tsekeri & Dionysia Kolokotsa & Kostas Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2018. "Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions," Energies, MDPI, vol. 11(11), pages 1-22, November.
    11. Le Cam, M. & Zmeureanu, R. & Daoud, A., 2017. "Cascade-based short-term forecasting method of the electric demand of HVAC system," Energy, Elsevier, vol. 119(C), pages 1098-1107.
    12. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Liu, Hongwu & Wang, Cheng, 2020. "An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control," Energy, Elsevier, vol. 199(C).
    13. Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
    14. Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "Quantum computing for future real-time building HVAC controls," Applied Energy, Elsevier, vol. 334(C).
    15. Zhao, Lei & Cai, Wenjian & Ding, Xudong & Chang, Weichung, 2013. "Model-based optimization for vapor compression refrigeration cycle," Energy, Elsevier, vol. 55(C), pages 392-402.
    16. Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
    17. Zhang, Zijun & Zeng, Yaohui & Kusiak, Andrew, 2012. "Minimizing pump energy in a wastewater processing plant," Energy, Elsevier, vol. 47(1), pages 505-514.
    18. Alperen Yayla & Kübra Sultan Świerczewska & Mahmut Kaya & Bahadır Karaca & Yusuf Arayici & Yunus Emre Ayözen & Onur Behzat Tokdemir, 2022. "Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings," Sustainability, MDPI, vol. 14(23), pages 1-29, December.
    19. Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    20. Guiqiang Wang & Haiman Wang & Zhiqiang Kang & Guohui Feng, 2020. "Data-Driven Optimization for Capacity Control of Multiple Ground Source Heat Pump System in Heating Mode," Energies, MDPI, vol. 13(14), pages 1-15, July.
    21. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    22. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2014. "A novel dynamic modeling approach for predicting building energy performance," Applied Energy, Elsevier, vol. 114(C), pages 91-103.
    23. Turanjanin, Valentina & Vučićević, Biljana & Jovanović, Marina & Mirkov, Nikola & Lazović, Ivan, 2014. "Indoor CO2 measurements in Serbian schools and ventilation rate calculation," Energy, Elsevier, vol. 77(C), pages 290-296.
    24. Alessia Arteconi & Fabio Polonara, 2018. "Assessing the Demand Side Management Potential and the Energy Flexibility of Heat Pumps in Buildings," Energies, MDPI, vol. 11(7), pages 1-19, July.
    25. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kusiak, Andrew & Li, Mingyang, 2010. "Reheat optimization of the variable-air-volume box," Energy, Elsevier, vol. 35(5), pages 1997-2005.
    2. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    3. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    4. Okochi, Godwine Swere & Yao, Ye, 2016. "A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 784-817.
    5. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    6. Kusiak, Andrew & Li, Mingyang, 2009. "Optimal decision making in ventilation control," Energy, Elsevier, vol. 34(11), pages 1835-1845.
    7. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
    8. Chiu, Chien-Chin & Tsai, Nan-Chyuan & Lin, Chun-Chi, 2014. "Near-optimal order-reduced control for A/C (air-conditioning) system of EVs (electric vehicles)," Energy, Elsevier, vol. 66(C), pages 342-353.
    9. Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    10. Han, H.J. & Jeon, Y.I. & Lim, S.H. & Kim, W.W. & Chen, K., 2010. "New developments in illumination, heating and cooling technologies for energy-efficient buildings," Energy, Elsevier, vol. 35(6), pages 2647-2653.
    11. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
    12. Homod, Raad Z., 2014. "Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-humid climatic region of Iraq," Energy, Elsevier, vol. 74(C), pages 762-774.
    13. Baldi, Simone & Zhang, Fan & Le Quang, Thuan & Endel, Petr & Holub, Ondrej, 2019. "Passive versus active learning in operation and adaptive maintenance of Heating, Ventilation, and Air Conditioning," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    14. Baldi, Simone & Yuan, Shuai & Endel, Petr & Holub, Ondrej, 2016. "Dual estimation: Constructing building energy models from data sampled at low rate," Applied Energy, Elsevier, vol. 169(C), pages 81-92.
    15. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Liu, Hongwu & Wang, Cheng, 2020. "An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control," Energy, Elsevier, vol. 199(C).
    16. Khan, Muhammad Waqas & Choudhry, Mohammad Ahmad & Zeeshan, Muhammad & Ali, Ahsan, 2015. "Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit," Energy, Elsevier, vol. 81(C), pages 477-488.
    17. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    18. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
    19. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    20. Muhammad Fayaz & DoHyeun Kim, 2018. "Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic," Energies, MDPI, vol. 11(1), pages 1-22, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:36:y:2011:i:5:p:2440-2449. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.