IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v91y2012i1p222-234.html
   My bibliography  Save this article

Buildings dynamic simulation: Water loop heat pump systems analysis for European climates

Author

Listed:
  • Buonomano, Annamaria
  • Calise, Francesco
  • Palombo, Adolfo

Abstract

In this paper, a purposely designed code for the performance analysis of the Water Loop Heat Pump (WLHP) systems is presented. Hourly, daily and seasonal energy system consumptions, operating economic costs and environmental impact assessments are dealt with. For the scope of comparison, the performances of two reference HVAC system are investigated too. For the computation of the building heating and cooling requirements, a suitable dynamic performance simulation model is being developed. All the relevant algorithms are implemented in MATLAB®. A case study of an office building undergoing simulation in different European climatic areas is being presented. Here, different building thermal features are considered. In order to maximize the system performance an additional optimization procedure to the operating devices temperatures is carried out. Results show that primary energy savings and avoided CO2 emissions of the WLHP system vary in relation to the compared reference systems and can be obtained only in several European weather zones. The feasibility of the WLHP system strongly depends on electricity and natural gas national costs.

Suggested Citation

  • Buonomano, Annamaria & Calise, Francesco & Palombo, Adolfo, 2012. "Buildings dynamic simulation: Water loop heat pump systems analysis for European climates," Applied Energy, Elsevier, vol. 91(1), pages 222-234.
    • Handle: RePEc:eee:appene:v:91:y:2012:i:1:p:222-234
    DOI: 10.1016/j.apenergy.2011.09.031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261911006349
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2011.09.031?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    3. Chen, Chao & Sun, Feng-ling & Feng, Lei & Liu, Ming, 2005. "Underground water-source loop heat-pump air-conditioning system applied in a residential building in Beijing," Applied Energy, Elsevier, vol. 82(4), pages 331-344, December.
    4. Adelard, L. & Pignolet-Tardan, F. & Mara, T. & Lauret, P. & Garde, F. & Boyer, H., 1998. "Sky temperature modelisation and applications in building simulation," Renewable Energy, Elsevier, vol. 15(1), pages 418-430.
    5. Freire, Roberto Zanetti & Mazuroski, Walter & Abadie, Marc Olivier & Mendes, Nathan, 2011. "Capacitive effect on the heat transfer through building glazing systems," Applied Energy, Elsevier, vol. 88(12), pages 4310-4319.
    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. Gjoka, Kristian & Rismanchi, Behzad & Crawford, Robert H., 2023. "Fifth-generation district heating and cooling systems: A review of recent advancements and implementation barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    2. Marini, Dashamir, 2013. "Optimization of HVAC systems for distributed generation as a function of different types of heat sources and climatic conditions," Applied Energy, Elsevier, vol. 102(C), pages 813-826.
    3. Buonomano, A. & Calise, F. & Palombo, A., 2013. "Solar heating and cooling systems by CPVT and ET solar collectors: A novel transient simulation model," Applied Energy, Elsevier, vol. 103(C), pages 588-606.
    4. Ascione, Fabrizio & Bianco, Nicola & de’ Rossi, Filippo & Turni, Gianluca & Vanoli, Giuseppe Peter, 2013. "Green roofs in European climates. Are effective solutions for the energy savings in air-conditioning?," Applied Energy, Elsevier, vol. 104(C), pages 845-859.
    5. Buffa, Simone & Cozzini, Marco & D’Antoni, Matteo & Baratieri, Marco & Fedrizzi, Roberto, 2019. "5th generation district heating and cooling systems: A review of existing cases in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 504-522.
    6. Buonomano, Annamaria & Calise, Francesco & Palombo, Adolfo & Vicidomini, Maria, 2016. "BIPVT systems for residential applications: An energy and economic analysis for European climates," Applied Energy, Elsevier, vol. 184(C), pages 1411-1431.
    7. Bojić, Milorad & Cvetković, Dragan & Bojić, Ljubiša, 2015. "Decreasing energy use and influence to environment by radiant panel heating using different energy sources," Applied Energy, Elsevier, vol. 138(C), pages 404-413.
    8. Barone, G. & Buonomano, A. & Calise, F. & Forzano, C. & Palombo, A., 2019. "Building to vehicle to building concept toward a novel zero energy paradigm: Modelling and case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 625-648.
    9. Wan, Kevin K.W. & Li, Danny H.W. & Pan, Wenyan & Lam, Joseph C., 2012. "Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications," Applied Energy, Elsevier, vol. 97(C), pages 274-282.
    10. Behzadi, Amirmohammad & Holmberg, Sture & Duwig, Christophe & Haghighat, Fariborz & Ooka, Ryozo & Sadrizadeh, Sasan, 2022. "Smart design and control of thermal energy storage in low-temperature heating and high-temperature cooling systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    11. Annamaria Buonomano, 2016. "Code-to-Code Validation and Application of a Dynamic Simulation Tool for the Building Energy Performance Analysis," Energies, MDPI, vol. 9(4), pages 1-29, April.
    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. Buonomano, Annamaria & Montanaro, Umberto & Palombo, Adolfo & Santini, Stefania, 2016. "Dynamic building energy performance analysis: A new adaptive control strategy for stringent thermohygrometric indoor air requirements," Applied Energy, Elsevier, vol. 163(C), pages 361-386.
    14. Marinakis, Vangelis & Doukas, Haris & Karakosta, Charikleia & Psarras, John, 2013. "An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector," Applied Energy, Elsevier, vol. 101(C), pages 6-14.
    15. Buonomano, Annamaria & Palombo, Adolfo, 2014. "Building energy performance analysis by an in-house developed dynamic simulation code: An investigation for different case studies," Applied Energy, Elsevier, vol. 113(C), pages 788-807.
    16. Francisco Javier Fernández & María Belén Folgueras & Inés Suárez, 2017. "Study and Optimization of Design Parameters in Water Loop Heat Pump Systems for Office Buildings in the Iberian Peninsula," Energies, MDPI, vol. 10(12), pages 1-12, November.
    17. Wang, Dan-Yi & Wang, Xueqing & Ding, Ru-Xi, 2022. "Welfare maximization with the least subsidy: Pricing model for surface water loop heat pump PPP projects considering occupancy rate growth and coefficient of performance," Renewable Energy, Elsevier, vol. 194(C), pages 1131-1141.

    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. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
    2. Yao Lu & Faisal Khaled Aldawood & Wanyu Hu & Yuxin Ma & Mohamed Kchaou & Chengjun Zhang & Xinpeng Yang & Ruitong Yang & Zitong Qi & Dong Li, 2023. "Optimization Strategy for Selecting the Combination Structure of Multilayer Phase Change Material (PCM) Glazing Windows under Different Climate Zones," Sustainability, MDPI, vol. 15(23), pages 1-24, November.
    3. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    4. Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
    5. 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.
    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. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    8. 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.
    9. 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).
    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. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. Nguyen, Hiep V. & Law, Ying Lam E. & Alavy, Masih & Walsh, Philip R. & Leong, Wey H. & Dworkin, Seth B., 2014. "An analysis of the factors affecting hybrid ground-source heat pump installation potential in North America," Applied Energy, Elsevier, vol. 125(C), pages 28-38.
    17. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    18. 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.
    19. 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.
    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:eee:appene:v:91:y:2012:i:1:p:222-234. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.