IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v34y2009i11p1835-1845.html
   My bibliography  Save this article

Optimal decision making in ventilation control

Author

Listed:
  • Kusiak, Andrew
  • Li, Mingyang

Abstract

In this paper, a two-mode ventilation control of a single facility is formulated as a scheduling model over multiple time horizons. Using the CO2 concentration as the major indoor air quality index and expected room occupancy schedule, optimal solutions leading to reduced CO2 concentration and energy costs are obtained by solving the multi-objective optimization model formulated in the paper. A modified evolutionary strategy algorithm is used to solve the model at different time horizons. The optimized ventilation schedules result in energy savings and maintain an acceptable level of indoor CO2 concentration.

Suggested Citation

  • Kusiak, Andrew & Li, Mingyang, 2009. "Optimal decision making in ventilation control," Energy, Elsevier, vol. 34(11), pages 1835-1845.
  • Handle: RePEc:eee:energy:v:34:y:2009:i:11:p:1835-1845
    DOI: 10.1016/j.energy.2009.07.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544209003181
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2009.07.039?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. 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.
    2. Chang, Yung-Chung & Chen, Wu-Hsing, 2009. "Optimal chilled water temperature calculation of multiple chiller systems using Hopfield neural network for saving energy," Energy, Elsevier, vol. 34(4), pages 448-456.
    3. 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.
    4. 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.
    5. Chang, Yung-Chung, 2006. "An innovative approach for demand side management—optimal chiller loading by simulated annealing," Energy, Elsevier, vol. 31(12), pages 1883-1896.
    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. 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.
    2. Kusiak, Andrew & Li, Mingyang, 2010. "Reheat optimization of the variable-air-volume box," Energy, Elsevier, vol. 35(5), pages 1997-2005.
    3. Panagiotis Korkidis & Anastasios Dounis & Panagiotis Kofinas, 2021. "Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings," Energies, MDPI, vol. 14(16), pages 1-33, August.
    4. Chaim Fershtman & Uzi Segal, 2018. "Preferences and Social Influence," American Economic Journal: Microeconomics, American Economic Association, vol. 10(3), pages 124-142, August.
    5. 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.
    6. Chantrelle, Fanny Pernodet & Lahmidi, Hicham & Keilholz, Werner & Mankibi, Mohamed El & Michel, Pierre, 2011. "Development of a multicriteria tool for optimizing the renovation of buildings," Applied Energy, Elsevier, vol. 88(4), pages 1386-1394, April.
    7. Schibuola, Luigi & Scarpa, Massimiliano & Tambani, Chiara, 2018. "CO2 based ventilation control in energy retrofit: An experimental assessment," Energy, Elsevier, vol. 143(C), pages 606-614.
    8. 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.
    9. R. Shiller I. & Р. Шиллер Дж., 2018. "Нарративная Экономика И Нейроэкономика // Narrative Economics And Neuroeconomics," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(1), pages 64-91.
    10. 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).
    11. Schmid, Eva & Knopf, Brigitte, 2012. "Ambitious mitigation scenarios for Germany: A participatory approach," Energy Policy, Elsevier, vol. 51(C), pages 662-672.
    12. 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.
    13. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "Modeling, air balancing and optimal pressure set-point selection for the ventilation system with minimized energy consumption," Applied Energy, Elsevier, vol. 236(C), pages 574-589.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Kuo, Cheng-Chien, 2009. "Reactive energy scheduling using bi-objective programming with modified particle swarm optimization," Energy, Elsevier, vol. 34(6), pages 804-815.
    6. 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.
    7. Kusiak, Andrew & Li, Mingyang, 2010. "Reheat optimization of the variable-air-volume box," Energy, Elsevier, vol. 35(5), pages 1997-2005.
    8. Liu, Xuefeng & Xu, Jinman & Bi, Mengbo & Ma, Wenjing & Chen, Wencong & Zheng, Minglong, 2024. "Multivariate coupled full-case physical model of large chilled water systems and its application," Energy, Elsevier, vol. 298(C).
    9. Wang, Yijun & Jin, Xinqiao & Shi, Wantao & Wang, Jiangqing, 2019. "Online chiller loading strategy based on the near-optimal performance map for energy conservation," Applied Energy, Elsevier, vol. 238(C), pages 1444-1451.
    10. Coelho, Leandro dos Santos & Klein, Carlos Eduardo & Sabat, Samrat L. & Mariani, Viviana Cocco, 2014. "Optimal chiller loading for energy conservation using a new differential cuckoo search approach," Energy, Elsevier, vol. 75(C), pages 237-243.
    11. 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.
    12. 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.
    13. Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017. "Energy efficiency-based course timetabling for university buildings," Energy, Elsevier, vol. 139(C), pages 394-405.
    14. Jing Zhao & Yu Shan, 2020. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System," Energies, MDPI, vol. 13(3), pages 1-17, January.
    15. Min-Yong Qi & Jun-Qing Li & Yu-Yan Han & Jin-Xin Dong, 2020. "Optimal Chiller Loading for Energy Conservation Using an Improved Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 13(15), pages 1-18, July.
    16. Chenari, Behrang & Dias Carrilho, João & Gameiro da Silva, Manuel, 2016. "Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1426-1447.
    17. 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.
    18. Junqi Wang & Rundong Liu & Linfeng Zhang & Hussain Syed ASAD & Erlin Meng, 2019. "Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches," Energies, MDPI, vol. 12(20), pages 1-20, October.
    19. Shi, Xing, 2011. "Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm," Energy, Elsevier, vol. 36(3), pages 1659-1667.
    20. Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.

    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:34:y:2009:i:11:p:1835-1845. 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.