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A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study

Author

Listed:
  • Serafín Alonso

    (Grupo de Investigación en Supervisión, Control y Automatización de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial e Informática, Universidad de León, Campus de Vegazana s/n, 24007 León, Spain)

  • Antonio Morán

    (Grupo de Investigación en Supervisión, Control y Automatización de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial e Informática, Universidad de León, Campus de Vegazana s/n, 24007 León, Spain)

  • Miguel Ángel Prada

    (Grupo de Investigación en Supervisión, Control y Automatización de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial e Informática, Universidad de León, Campus de Vegazana s/n, 24007 León, Spain)

  • Perfecto Reguera

    (Grupo de Investigación en Supervisión, Control y Automatización de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial e Informática, Universidad de León, Campus de Vegazana s/n, 24007 León, Spain)

  • Juan José Fuertes

    (Grupo de Investigación en Supervisión, Control y Automatización de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial e Informática, Universidad de León, Campus de Vegazana s/n, 24007 León, Spain)

  • Manuel Domínguez

    (Grupo de Investigación en Supervisión, Control y Automatización de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial e Informática, Universidad de León, Campus de Vegazana s/n, 24007 León, Spain)

Abstract

Large buildings cause more than 20% of the global energy consumption in advanced countries. In buildings such as hospitals, cooling loads represent an important percentage of the overall energy demand (up to 44%) due to the intensive use of heating, ventilation and air conditioning (HVAC) systems among other key factors, so their study should be considered. In this paper, we propose a data-driven analysis for improving the efficiency in multiple-chiller plants. Coefficient of performance (COP) is used as energy efficiency indicator. Data analysis, based on aggregation operations, filtering and data projection, allows us to obtain knowledge from chillers and the whole plant, in order to define and tune management rules. The plant manager software (PMS) that implements those rules establishes when a chiller should be staged up/down and which chiller should be started/stopped according different efficiency criteria. This approach has been applied on the chiller plant at the Hospital of León.

Suggested Citation

  • Serafín Alonso & Antonio Morán & Miguel Ángel Prada & Perfecto Reguera & Juan José Fuertes & Manuel Domínguez, 2019. "A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study," Energies, MDPI, vol. 12(5), pages 1-28, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:827-:d:210391
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    References listed on IDEAS

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    Cited by:

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    3. Dong Kon Hwang & Jinkyun Cho & Junghwan Moon, 2019. "Feasibility Study on Energy Audit and Data Driven Analysis Procedure for Building Energy Efficiency: Bench-Marking in Korean Hospital Buildings," Energies, MDPI, vol. 12(15), pages 1-18, August.
    4. Guoying Lin & Yuyao Yang & Feng Pan & Sijian Zhang & Fen Wang & Shuai Fan, 2019. "An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality," Future Internet, MDPI, vol. 11(4), pages 1-16, April.

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