IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v6y2013i9p4769-4785d28689.html
   My bibliography  Save this article

Saving Building Energy through Advanced Control Strategies

Author

Listed:
  • Stephen Treado

    (Department of Architectural Engineering, the Pennsylvania State University, University Park, PA 16802, USA)

  • Yan Chen

    (Department of Architectural Engineering, the Pennsylvania State University, University Park, PA 16802, USA)

Abstract

This article presents an analysis of the relationship between building energy usage and building control system operation and performance. A method is presented for estimating the energy saving potential of improvements in building and control system operation, including the relative impact of recommssioning and hardware and software upgrades, based on a subjective assessment of the level of energy efficient design and the energy usage of the building relative to similar buildings as indicated by the Energy Utilization Index for the building. The method introduces a Building Design Index and a Building Operating Index to evaluate building energy performance versus similar buildings, and uses these indices to estimate potential savings and effectiveness of control system improvements.

Suggested Citation

  • Stephen Treado & Yan Chen, 2013. "Saving Building Energy through Advanced Control Strategies," Energies, MDPI, vol. 6(9), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:9:p:4769-4785:d:28689
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/6/9/4769/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/6/9/4769/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. 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.
    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. Guillermo Escrivá-Escrivá & Carlos Roldán-Blay & Carlos Roldán-Porta & Xavier Serrano-Guerrero, 2019. "Occasional Energy Reviews from an External Expert Help to Reduce Building Energy Consumption at a Reduced Cost," Energies, MDPI, vol. 12(15), pages 1-14, July.
    2. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
    3. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2020. "Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls," Energies, MDPI, vol. 13(12), pages 1-18, June.
    4. Daniel Prusak & Grzegorz Karpiel & Konrad Kułakowski, 2021. "The Architecture of a Real-Time Control System for Heating Energy Management in the Intelligent Building," Energies, MDPI, vol. 14(17), pages 1-13, August.
    5. Rosa Morales González & Shahab Shariat Torbaghan & Madeleine Gibescu & Sjef Cobben, 2016. "Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation," Energies, MDPI, vol. 9(7), pages 1-24, July.
    6. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    7. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    8. 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).
    9. Frederik Ruelens & Sandro Iacovella & Bert J. Claessens & Ronnie Belmans, 2015. "Learning Agent for a Heat-Pump Thermostat with a Set-Back Strategy Using Model-Free Reinforcement Learning," Energies, MDPI, vol. 8(8), pages 1-19, August.

    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. 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.
    2. 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.
    3. Goyal, Siddharth & Barooah, Prabir & Middelkoop, Timothy, 2015. "Experimental study of occupancy-based control of HVAC zones," Applied Energy, Elsevier, vol. 140(C), pages 75-84.
    4. Wenquan Jin & Israr Ullah & Shabir Ahmad & Dohyeun Kim, 2019. "Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
    5. 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.
    6. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    7. Muniak, Damian Piotr, 2014. "A new methodology to determine the pre-setting of the control valve in a heating installation. A general model," Applied Energy, Elsevier, vol. 135(C), pages 35-42.
    8. Lork, Clement & Li, Wen-Tai & Qin, Yan & Zhou, Yuren & Yuen, Chau & Tushar, Wayes & Saha, Tapan K., 2020. "An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management," Applied Energy, Elsevier, vol. 276(C).
    9. 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.
    10. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    11. Molinari, Marco & Anund Vogel, Jonas & Rolando, Davide & Lundqvist, Per, 2023. "Using living labs to tackle innovation bottlenecks: the KTH Live-In Lab case study," Applied Energy, Elsevier, vol. 338(C).
    12. Pisello, Anna Laura & Goretti, Michele & Cotana, Franco, 2012. "A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity," Applied Energy, Elsevier, vol. 97(C), pages 419-429.
    13. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
    14. Ma, Peizheng & Wang, Lin-Shu & Guo, Nianhua, 2015. "Maximum window-to-wall ratio of a thermally autonomous building as a function of envelope U-value and ambient temperature amplitude," Applied Energy, Elsevier, vol. 146(C), pages 84-91.
    15. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    16. Zhuang, Chaoqun & Gao, Yafeng & Zhao, Yingru & Levinson, Ronnen & Heiselberg, Per & Wang, Zhiqiang & Guo, Rui, 2021. "Potential benefits and optimization of cool-coated office buildings: A case study in Chongqing, China," Energy, Elsevier, vol. 226(C).
    17. Zeng, Yaohui & Zhang, Zijun & Kusiak, Andrew, 2015. "Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms," Energy, Elsevier, vol. 86(C), pages 393-402.
    18. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    19. 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.
    20. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).

    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:gam:jeners:v:6:y:2013:i:9:p:4769-4785:d:28689. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.