IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v160y2022ics1364032122002696.html
   My bibliography  Save this article

A review of optimization based tools for design and control of building energy systems

Author

Listed:
  • Barber, Kyle A.
  • Krarti, Moncef

Abstract

This paper reviews applications of multi-objective optimization approaches for design, control, and the combination of both design and control of a single element or a set of integrated building systems using the current state of the art in building energy modeling and simulation tools. The review provides background on the simulation tools and applicable data-analysis methods currently available to the building industry for both design-oriented and control-oriented optimization approaches of various energy systems. In particular, the analysis presented in this paper reviews the capabilities of these toolsets, as well as their suitability for use by industry professionals, and academic researchers. Reviewed studies show significant annual energy savings and peak load shifting potential for integrated system optimization within buildings. However, the available toolsets and frameworks are not suitable to readily perform multi-objective combined optimizations that integrate a wide range of building energy systems at industry scale. To maximize the efficiency of these systems and mitigate greenhouse gas emissions by buildings in accordance with goals set forth by nations across the world, a comprehensive and easy-to-use combined design and control-oriented optimization tool that interfaces easily with geometric architectural design tools is needed for the building simulation industry. Moreover, optimization-based building energy modeling and simulation tools that consider utility driven demand response and peak load management, would be beneficial for designing and operating a built environment that is resilient and sustainable. The case studies reviewed in this paper confirm the limited studies of energy simulation optimizations that have simultaneously analyzed design and control elements of building energy systems prior to construction. This review concludes that there is a need for the development of a more advanced user-friendly tools that integrate both design and control-oriented optimizations of building energy systems to truly transition the building energy industry forward beyond the common code compliance designs.

Suggested Citation

  • Barber, Kyle A. & Krarti, Moncef, 2022. "A review of optimization based tools for design and control of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  • Handle: RePEc:eee:rensus:v:160:y:2022:i:c:s1364032122002696
    DOI: 10.1016/j.rser.2022.112359
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032122002696
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2022.112359?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. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2010. "Particle swarm optimization for redundant building cooling heating and power system," Applied Energy, Elsevier, vol. 87(12), pages 3668-3679, December.
    2. Khuc, Quy Van & Pham, Phu & Tran, Duc-Trung, 2021. "Questionnaire design," OSF Preprints q3um6, Center for Open Science.
    3. Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
    4. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2021. "Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems," Applied Energy, Elsevier, vol. 297(C).
    5. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa, 2010. "Optimization of capacity and operation for CCHP system by genetic algorithm," Applied Energy, Elsevier, vol. 87(4), pages 1325-1335, April.
    6. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    7. Beckman, William A. & Broman, Lars & Fiksel, Alex & Klein, Sanford A. & Lindberg, Eva & Schuler, Mattias & Thornton, Jeff, 1994. "TRNSYS The most complete solar energy system modeling and simulation software," Renewable Energy, Elsevier, vol. 5(1), pages 486-488.
    8. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    9. Ngoc Le Chau & Ngoc Thoai Tran & Thanh-Phong Dao, 2021. "An Optimal Design Method for Compliant Mechanisms," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-18, March.
    10. ., 2021. "Empirical analysis, research design and methodology," Chapters, in: A Guide to Islamic Asset Management, chapter 4, pages 77-165, Edward Elgar Publishing.
    11. Johari, F. & Peronato, G. & Sadeghian, P. & Zhao, X. & Widén, J., 2020. "Urban building energy modeling: State of the art and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    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. Jelić, Marko & Batić, Marko & Krstić, Aleksandra & Bottarelli, Michele & Mainardi, Elena, 2023. "Comparative analysis of metaheuristic optimization approaches for multisource heat pump operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    2. Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "Quantum computing for future real-time building HVAC controls," Applied Energy, Elsevier, vol. 334(C).
    3. Kotarela, Faidra & Kyritsis, Anastasios & Agathokleous, Rafaela & Papanikolaou, Nick, 2023. "On the exploitation of dynamic simulations for the design of buildings energy systems," Energy, Elsevier, vol. 271(C).
    4. Lantonio, Nicole A. & Krarti, Moncef, 2022. "Simultaneous design and control optimization of smart glazed windows," Applied Energy, Elsevier, vol. 328(C).
    5. Lucarelli, Giuseppe & Genovese, Matteo & Florio, Gaetano & Fragiacomo, Petronilla, 2023. "3E (energy, economic, environmental) multi-objective optimization of CCHP industrial plant: Investigation of the optimal technology and the optimal operating strategy," Energy, Elsevier, vol. 278(PA).

    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. Piacentino, Antonio & Barbaro, Chiara & Cardona, Fabio & Gallea, Roberto & Cardona, Ennio, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part I: Description of the method," Applied Energy, Elsevier, vol. 111(C), pages 1204-1221.
    2. Jie, Pengfei & Yan, Fuchun & Li, Jing & Zhang, Yumei & Wen, Zhimei, 2019. "Optimizing the insulation thickness of walls of existing buildings with CHP-based district heating systems," Energy, Elsevier, vol. 189(C).
    3. Guozheng Li & Rui Wang & Tao Zhang & Mengjun Ming, 2018. "Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g," Energies, MDPI, vol. 11(4), pages 1-26, March.
    4. Wang, Jiangjiang & Sui, Jun & Jin, Hongguang, 2015. "An improved operation strategy of combined cooling heating and power system following electrical load," Energy, Elsevier, vol. 85(C), pages 654-666.
    5. Han, Jie & Ouyang, Leixin & Xu, Yuzhen & Zeng, Rong & Kang, Shushuo & Zhang, Guoqiang, 2016. "Current status of distributed energy system in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 288-297.
    6. Deng, Yan & Liu, Yicai & Zeng, Rong & Wang, Qianxu & Li, Zheng & Zhang, Yu & Liang, Heng, 2021. "A novel operation strategy based on black hole algorithm to optimize combined cooling, heating, and power-ground source heat pump system," Energy, Elsevier, vol. 229(C).
    7. Liu, Mingxi & Shi, Yang & Fang, Fang, 2012. "A new operation strategy for CCHP systems with hybrid chillers," Applied Energy, Elsevier, vol. 95(C), pages 164-173.
    8. Jradi, M. & Riffat, S., 2014. "Tri-generation systems: Energy policies, prime movers, cooling technologies, configurations and operation strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 396-415.
    9. Namuli, R. & Pillay, P. & Jaumard, B. & Laflamme, C.B., 2013. "Threshold herd size for commercial viability of biomass waste to energy conversion systems on rural farms," Applied Energy, Elsevier, vol. 108(C), pages 308-322.
    10. Wei, Dajun & Chen, Alian & Sun, Bo & Zhang, Chenghui, 2016. "Multi-objective optimal operation and energy coupling analysis of combined cooling and heating system," Energy, Elsevier, vol. 98(C), pages 296-307.
    11. Luqing Zhang & Aikang Chen & Han Gu & Xitian Wang & Da Xie & Chenghong Gu, 2019. "Planning of the Multi-Energy Circular System Coupled with Waste Processing Base: A Case from China," Energies, MDPI, vol. 12(20), pages 1-17, October.
    12. Tian, Zhe & Niu, Jide & Lu, Yakai & He, Shunming & Tian, Xue, 2016. "The improvement of a simulation model for a distributed CCHP system and its influence on optimal operation cost and strategy," Applied Energy, Elsevier, vol. 165(C), pages 430-444.
    13. Ahn, Hyeunguk & Rim, Donghyun & Freihaut, James D., 2018. "Performance assessment of hybrid chiller systems for combined cooling, heating and power production," Applied Energy, Elsevier, vol. 225(C), pages 501-512.
    14. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
    15. Deng, Yan & Zeng, Rong & Liu, Yicai, 2022. "A novel off-design model to optimize combined cooling, heating and power system with hybrid chillers for different operation strategies," Energy, Elsevier, vol. 239(PB).
    16. 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.
    17. Wang, Jiang-Jiang & Xu, Zi-Long & Jin, Hong-Guang & Shi, Guo-hua & Fu, Chao & Yang, Kun, 2014. "Design optimization and analysis of a biomass gasification based BCHP system: A case study in Harbin, China," Renewable Energy, Elsevier, vol. 71(C), pages 572-583.
    18. Wang, Jiangjiang & Liu, Yi & Ren, Fukang & Lu, Shuaikang, 2020. "Multi-objective optimization and selection of hybrid combined cooling, heating and power systems considering operational flexibility," Energy, Elsevier, vol. 197(C).
    19. 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.
    20. Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2017. "Development of an optimization based design framework for microgrid energy systems," Energy, Elsevier, vol. 140(P1), pages 340-351.

    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:rensus:v:160:y:2022:i:c:s1364032122002696. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.