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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
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    References listed on IDEAS

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    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.
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    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.

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