IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p7279-d932967.html
   My bibliography  Save this article

The Impact of Weather-Forecast-Based Regulation on Energy Savings for Heating in Multi-Family Buildings

Author

Listed:
  • Joanna Piotrowska-Woroniak

    (HVAC Department, Bialystok University of Technology, Wiejska 45E, 15-351 Bialystok, Poland)

  • Tomasz Szul

    (Faculty of Production and Power Engineering, University of Agriculture, 30-149 Kraków, Poland)

  • Krzysztof Cieśliński

    (Łomża Housing Cooperative in Łomża, 18-400 Łomża, Poland)

  • Jozef Krilek

    (Faculty of Technology, Technical University in Zvolen, 960 01 Zvolen, Slovakia)

Abstract

In this study, based on 19 years of research, an analysis of thermal energy consumption for heating was carried out on a group of 22 residential multi-family buildings located in a temperate continental climate. The buildings were constructed with two different technologies based on prefabricated elements, and most of them were equipped with central heating cost allocators. A predictive control system for the central heating system was installed in the analyzed buildings, followed by a deep thermo-modernization. An evaluation was made regarding whether the use of a change in the method of central heating control, from the traditional one, which takes into account only the variable external temperature, to weather control, increases the energy efficiency of the thermo-modernized buildings. In addition, the cost-effectiveness of the modernization measures was analyzed by determining economic efficiency indicators; therefore, it was possible to identify the modernization variant that, with limited investment costs, could achieve the best energy efficiency resulting from the European energy policy.

Suggested Citation

  • Joanna Piotrowska-Woroniak & Tomasz Szul & Krzysztof Cieśliński & Jozef Krilek, 2022. "The Impact of Weather-Forecast-Based Regulation on Energy Savings for Heating in Multi-Family Buildings," Energies, MDPI, vol. 15(19), pages 1-30, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7279-:d:932967
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/7279/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/19/7279/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marius Reich & Jonas Gottschald & Philipp Riegebauer & Mario Adam, 2020. "Predictive Control of District Heating System Using Multi-Stage Nonlinear Approximation with Selective Memory," Energies, MDPI, vol. 13(24), pages 1-25, December.
    2. Beata Sadowska & Joanna Piotrowska-Woroniak & Grzegorz Woroniak & Wiesław Sarosiek, 2022. "Energy and Economic Efficiency of the Thermomodernization of an Educational Building and Reduction of Pollutant Emissions—A Case Study," Energies, MDPI, vol. 15(8), pages 1-31, April.
    3. Chanuk Lee & Dong Eun Jung & Donghoon Lee & Kee Han Kim & Sung Lok Do, 2021. "Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads," Energies, MDPI, vol. 14(3), pages 1-19, February.
    4. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
    5. Abhinandana Boodi & Karim Beddiar & Malek Benamour & Yassine Amirat & Mohamed Benbouzid, 2018. "Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations," Energies, MDPI, vol. 11(10), pages 1-26, September.
    6. 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.
    7. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    8. 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).
    9. 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.
    10. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    11. 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.
    12. Jin Wang & Yu Gao & Wei Liu & Arun Kumar Sangaiah & Hye-Jin Kim, 2019. "An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
    13. Mattia Ricci & Paolo Sdringola & Salvatore Tamburrino & Giovanni Puglisi & Elena Di Donato & Maria Alessandra Ancona & Francesco Melino, 2022. "Efficient District Heating in a Decarbonisation Perspective: A Case Study in Italy," Energies, MDPI, vol. 15(3), pages 1-20, January.
    14. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.
    15. Janusz Adamczyk & Robert Dylewski, 2020. "Ecological and Economic Benefits of the “Medium” Level of the Building Thermo-Modernization: A Case Study in Poland," Energies, MDPI, vol. 13(17), pages 1-14, September.
    16. van den Brom, Paula & Hansen, Anders Rhiger & Gram-Hanssen, Kirsten & Meijer, Arjen & Visscher, Henk, 2019. "Variances in residential heating consumption – Importance of building characteristics and occupants analysed by movers and stayers," Applied Energy, Elsevier, vol. 250(C), pages 713-728.
    17. Petri Hietaharju & Mika Ruusunen & Kauko Leiviskä, 2018. "A Dynamic Model for Indoor Temperature Prediction in Buildings," Energies, MDPI, vol. 11(6), pages 1-20, June.
    18. Tomasz Szul, 2022. "Application of a Thermal Performance-Based Model to Prediction Energy Consumption for Heating of Single-Family Residential Buildings," Energies, MDPI, vol. 15(1), pages 1-13, January.
    19. Roozbeh Sadeghian Broujeny & Kurosh Madani & Abdennasser Chebira & Veronique Amarger & Laurent Hurtard, 2021. "A Heating Controller Designing Based on Living Space Heating Dynamic’s Model Approach in a Smart Building," Energies, MDPI, vol. 14(4), pages 1-25, February.
    20. Nilofar Asim & Marzieh Badiei & Masita Mohammad & Halim Razali & Armin Rajabi & Lim Chin Haw & Mariyam Jameelah Ghazali, 2022. "Sustainability of Heating, Ventilation and Air-Conditioning (HVAC) Systems in Buildings—An Overview," IJERPH, MDPI, vol. 19(2), pages 1-16, January.
    21. Akinkunmi Adegbenro & Michael Short & Claudio Angione, 2021. "An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications," Energies, MDPI, vol. 14(8), pages 1-18, April.
    22. Tomasz Szul & Sylwester Tabor & Krzysztof Pancerz, 2021. "Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating," Energies, MDPI, vol. 14(10), pages 1-13, May.
    23. Al-Shammari, Eiman Tamah & Keivani, Afram & Shamshirband, Shahaboddin & Mostafaeipour, Ali & Yee, Por Lip & Petković, Dalibor & Ch, Sudheer, 2016. "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm," Energy, Elsevier, vol. 95(C), pages 266-273.
    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. Piotr Michalak & Krzysztof Szczotka & Jakub Szymiczek, 2023. "Audit-Based Energy Performance Analysis of Multifamily Buildings in South-East Poland," Energies, MDPI, vol. 16(12), pages 1-21, June.
    2. Zbigniew Kowalczyk & Marcin Tomasik, 2023. "Economic and Energy Analysis of the Operation of Windows in Residential Buildings in Poland," Energies, MDPI, vol. 16(19), pages 1-16, September.

    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. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
    2. Joanna Piotrowska-Woroniak & Tomasz Szul, 2022. "Application of a Model Based on Rough Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings," Energies, MDPI, vol. 15(23), pages 1-13, November.
    3. Deng, Zhipeng & Wang, Xuezheng & Jiang, Zixin & Zhou, Nianxin & Ge, Haiwang & Dong, Bing, 2023. "Evaluation of deploying data-driven predictive controls in buildings on a large scale for greenhouse gas emission reduction," Energy, Elsevier, vol. 270(C).
    4. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    5. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    6. Lee, Zachary E. & Zhang, K. Max, 2021. "Generalized reinforcement learning for building control using Behavioral Cloning," Applied Energy, Elsevier, vol. 304(C).
    7. Yang, Shiyu & Wan, Man Pun, 2022. "Machine-learning-based model predictive control with instantaneous linearization – A case study on an air-conditioning and mechanical ventilation system," Applied Energy, Elsevier, vol. 306(PB).
    8. Giovanni Bianco & Stefano Bracco & Federico Delfino & Lorenzo Gambelli & Michela Robba & Mansueto Rossi, 2020. "A Building Energy Management System Based on an Equivalent Electric Circuit Model," Energies, MDPI, vol. 13(7), pages 1-23, April.
    9. Ali Hamza & Muhammad Uneeb & Iftikhar Ahmad & Komal Saleem & Zunaib Ali, 2023. "Variable Structure-Based Control for Dynamic Temperature Setpoint Regulation in Hospital Extreme Healthcare Zones," Energies, MDPI, vol. 16(10), pages 1-27, May.
    10. 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.
    11. Abhinandana Boodi & Karim Beddiar & Yassine Amirat & Mohamed Benbouzid, 2022. "Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives," Energies, MDPI, vol. 15(4), pages 1-27, February.
    12. Krzysztof Szczotka & Anna Barwińska-Małajowicz & Jakub Szymiczek & Radosław Pyrek, 2023. "Thermomodernization as a Mechanism for Improving Energy Efficiency and Reducing Emissions of Pollutants into the Atmosphere in a Public Utility Building," Energies, MDPI, vol. 16(13), pages 1-24, June.
    13. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2021. "Energy Flexibility as Additional Energy Source in Multi-Energy Systems with District Cooling," Energies, MDPI, vol. 14(2), pages 1-30, January.
    14. Bünning, Felix & Huber, Benjamin & Schalbetter, Adrian & Aboudonia, Ahmed & Hudoba de Badyn, Mathias & Heer, Philipp & Smith, Roy S. & Lygeros, John, 2022. "Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC," Applied Energy, Elsevier, vol. 310(C).
    15. Ali Saberi Derakhtenjani & Andreas K. Athienitis, 2021. "Model Predictive Control Strategies to Activate the Energy Flexibility for Zones with Hydronic Radiant Systems," Energies, MDPI, vol. 14(4), pages 1-19, February.
    16. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    17. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    18. Eid Gul & Giorgio Baldinelli & Pietro Bartocci, 2022. "Energy Transition: Renewable Energy-Based Combined Heat and Power Optimization Model for Distributed Communities," Energies, MDPI, vol. 15(18), pages 1-18, September.
    19. Seif Khiati & Rafik Belarbi & Ammar Yahia, 2023. "Sustainable Buildings: A Choice, or a Must for Our Future?," Energies, MDPI, vol. 16(6), pages 1-5, March.
    20. Hyo-Jun Kim & Young-Hum Cho, 2021. "Optimal Control Method of Variable Air Volume Terminal Unit System," Energies, MDPI, vol. 14(22), pages 1-15, November.

    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:15:y:2022:i:19:p:7279-:d:932967. 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.