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Impact of Heating Control Strategy and Occupant Behavior on the Energy Consumption in a Building with Natural Ventilation in Poland

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  • Aniela Kaminska

    (Faculty of Electrical Engineering, Poznan University of Technology, ul. Piotrowo 3a, 60-965 Poznań, Poland)

Abstract

This study aims to provide an experimental assessment of energy consumption in an existing public building in Poland, in order to analyze the impact of occupant behavior on that consumption. The building is naturally ventilated and the occupants have the freedom to change the temperature set point and open or close the windows. The energy consumption is calculated and the calculation results are compared with the experimental data. An analysis of occupants’ behavior has revealed that they choose temperature set points in a wide range recognized as thermal comfort, and window opening is accidental and difficult to predict. The implemented heating control algorithms take into account the strong influence of individual occupant preferences on the feeling of comfort. The energy consumption assessment has revealed that the lowering of temperature set point by 1 °C results in an energy saving of about 5%. Comparisons of energy consumption with heating control and without any controls showed that the potential for energy reduction due to heating control reached approximately 10%. The use of windows control, which allows to turn off the heating after opening the window and its impact on energy savings have been discussed as well.

Suggested Citation

  • Aniela Kaminska, 2019. "Impact of Heating Control Strategy and Occupant Behavior on the Energy Consumption in a Building with Natural Ventilation in Poland," Energies, MDPI, vol. 12(22), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4304-:d:285961
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    References listed on IDEAS

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    1. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    2. Roberto Robledo-Fava & Mónica C. Hernández-Luna & Pedro Fernández-de-Córdoba & Humberto Michinel & Sonia Zaragoza & A Castillo-Guzman & Romeo Selvas-Aguilar, 2019. "Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings," Energies, MDPI, vol. 12(8), pages 1-23, April.
    3. Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), pages 1-18, February.
    4. Jin Dong & Christopher Winstead & James Nutaro & Teja Kuruganti, 2018. "Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings," Energies, MDPI, vol. 11(9), pages 1-20, September.
    5. Aniela Kaminska & Andrzej Ożadowicz, 2018. "Lighting Control Including Daylight and Energy Efficiency Improvements Analysis," Energies, MDPI, vol. 11(8), pages 1-18, August.
    6. Mohammad K. Najjar & Vivian W. Y. Tam & Leandro Torres Di Gregorio & Ana Catarina Jorge Evangelista & Ahmed W. A. Hammad & Assed Haddad, 2019. "Integrating Parametric Analysis with Building Information Modeling to Improve Energy Performance of Construction Projects," Energies, MDPI, vol. 12(8), pages 1-22, April.
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    Cited by:

    1. Qadeer Ali & Muhammad Jamaluddin Thaheem & Fahim Ullah & Samad M. E. Sepasgozar, 2020. "The Performance Gap in Energy-Efficient Office Buildings: How the Occupants Can Help?," Energies, MDPI, vol. 13(6), pages 1-27, March.
    2. Przemysław Markiewicz-Zahorski & Joanna Rucińska & Małgorzata Fedorczak-Cisak & Michał Zielina, 2021. "Building Energy Performance Analysis after Changing Its Form of Use from an Office to a Residential Building," Energies, MDPI, vol. 14(3), pages 1-24, January.
    3. Piotr Michalak, 2021. "Experimental and Theoretical Study on the Internal Convective and Radiative Heat Transfer Coefficients for a Vertical Wall in a Residential Building," Energies, MDPI, vol. 14(18), pages 1-22, September.
    4. Evi Lambie & Dirk Saelens, 2020. "Identification of the Building Envelope Performance of a Residential Building: A Case Study," Energies, MDPI, vol. 13(10), pages 1-28, May.

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