IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v185y2017ip1p29-43.html
   My bibliography  Save this article

Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons

Author

Listed:
  • Alibabaei, Nima
  • Fung, Alan S.
  • Raahemifar, Kaamran
  • Moghimi, Arash

Abstract

Based on their structure, residential houses/buildings (RHs) can offer excellent opportunities for managing their internal energy demand and subsequently lowering their energy cost. Demand management and energy cost saving can be achieved by taking advantage of RHs/buildings capabilities in storing thermal energy. Thermal energy can be stored utilizing intelligent Strategy Planning Models (SPMs) which are applied in the heating, ventilating and air conditioning (HVAC) system as one of the largest energy consumer in RHs buildings. This study discusses the development of three different strategy planning models including Smart Dual Fuel Switching System (SDFSS), Load Shifting (LSH), and LSHSDFSS, a combination of load shifting and fuel switching SPMs. In order to facilitate the implementation of the developed SPMs on the HVAC system of the house used in this case study, an advanced controller was designed by connecting both TRNSYS-Matlab programs. The HVAC system energy demand as well as the corresponding saving on the HVAC system energy cost are analyzed in-depth numerically using each of the strategy planning models during both the heating and cooling seasons. Simulation results showed that in the heating season, the operating/energy cost of HVAC system decreased significantly (23.8%) by implementing SDFSS-SPM. LSHSDFSS-SPM reduced the HVAC system operating cost by 15.8%. In the cooling season, LSH-SPM reduced the HVAC system operating cost by 6.63%.

Suggested Citation

  • Alibabaei, Nima & Fung, Alan S. & Raahemifar, Kaamran & Moghimi, Arash, 2017. "Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons," Applied Energy, Elsevier, vol. 185(P1), pages 29-43.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p1:p:29-43
    DOI: 10.1016/j.apenergy.2016.10.062
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916315082
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.10.062?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. Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
    2. Radhakrishnan, Nikitha & Su, Yang & Su, Rong & Poolla, Kameshwar, 2016. "Token based scheduling for energy management in building HVAC systems," Applied Energy, Elsevier, vol. 173(C), pages 67-79.
    3. Di Giorgio, Alessandro & Liberati, Francesco, 2014. "Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models," Applied Energy, Elsevier, vol. 128(C), pages 119-132.
    4. Patteeuw, Dieter & Henze, Gregor P. & Helsen, Lieve, 2016. "Comparison of load shifting incentives for low-energy buildings with heat pumps to attain grid flexibility benefits," Applied Energy, Elsevier, vol. 167(C), pages 80-92.
    5. Ghahramani, Ali & Zhang, Kenan & Dutta, Kanu & Yang, Zheng & Becerik-Gerber, Burcin, 2016. "Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings," Applied Energy, Elsevier, vol. 165(C), pages 930-942.
    6. Arteconi, Alessia & Ciarrocchi, Eleonora & Pan, Quanwen & Carducci, Francesco & Comodi, Gabriele & Polonara, Fabio & Wang, Ruzhu, 2017. "Thermal energy storage coupled with PV panels for demand side management of industrial building cooling loads," Applied Energy, Elsevier, vol. 185(P2), pages 1984-1993.
    7. Kramer, R.P. & Maas, M.P.E. & Martens, M.H.J. & van Schijndel, A.W.M. & Schellen, H.L., 2015. "Energy conservation in museums using different setpoint strategies: A case study for a state-of-the-art museum using building simulations," Applied Energy, Elsevier, vol. 158(C), pages 446-458.
    8. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    9. Wang, Qinpeng & Augenbroe, Godfried & Kim, Ji-Hyun & Gu, Li, 2016. "Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings," Applied Energy, Elsevier, vol. 174(C), pages 166-180.
    10. Pagani, Giuliano Andrea & Aiello, Marco, 2013. "The Power Grid as a complex network: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(11), pages 2688-2700.
    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. Villa-Arrieta, Manuel & Sumper, Andreas, 2018. "A model for an economic evaluation of energy systems using TRNSYS," Applied Energy, Elsevier, vol. 215(C), pages 765-777.
    2. Romero Rodríguez, Laura & Sánchez Ramos, José & Álvarez Domínguez, Servando & Eicker, Ursula, 2018. "Contributions of heat pumps to demand response: A case study of a plus-energy dwelling," Applied Energy, Elsevier, vol. 214(C), pages 191-204.
    3. Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    4. Germán Campos Gordillo & Germán Ramos Ruiz & Yves Stauffer & Stephan Dasen & Carlos Fernández Bandera, 2020. "EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB ® and C#," Sustainability, MDPI, vol. 12(2), pages 1-14, January.
    5. Deena Ahmed Al Qurneh & Lama AbuMoeilak & Salwa Beheiry & Maruf Mortula, 2024. "Coupling and Quantifying Sustainability and Resilience in Intelligent Buildings," Sustainability, MDPI, vol. 16(8), pages 1-23, April.
    6. Wangqi Xiong & Jiandong Wang, 2020. "Minimizing Power Consumption of an Experimental HVAC System Based on Parallel Grid Searching," Energies, MDPI, vol. 13(8), pages 1-18, April.
    7. Daehyun Kim & Hyunmuk Lim & Jongmin Moon & Jinsoo Park & Gwanghoon Rhee, 2021. "Heating Performances of a Large-Scale Factory Evaluated through Thermal Comfort and Building Energy Consumption," Energies, MDPI, vol. 14(18), pages 1-16, September.
    8. Bloemendal, Martin & Jaxa-Rozen, Marc & Olsthoorn, Theo, 2018. "Methods for planning of ATES systems," Applied Energy, Elsevier, vol. 216(C), pages 534-557.
    9. Li, Gang & Du, Yuqing, 2018. "Performance investigation and economic benefits of new control strategies for heat pump-gas fired water heater hybrid system," Applied Energy, Elsevier, vol. 232(C), pages 101-118.
    10. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    11. Samy Faddel & Guanyu Tian & Qun Zhou, 2021. "Decentralized Management of Commercial HVAC Systems," Energies, MDPI, vol. 14(11), pages 1-18, May.
    12. Dimitra G. Kyriakou & Fotios D. Kanellos, 2022. "Optimal Operation of Microgrids Comprising Large Building Prosumers and Plug-in Electric Vehicles Integrated into Active Distribution Networks," Energies, MDPI, vol. 15(17), pages 1-26, August.

    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. Fernandez, Edstan & Hossain, M.J. & Nizami, M.S.H., 2018. "Game-theoretic approach to demand-side energy management for a smart neighbourhood in Sydney incorporating renewable resources," Applied Energy, Elsevier, vol. 232(C), pages 245-257.
    2. Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).
    3. Ghahramani, Ali & Pantelic, Jovan & Lindberg, Casey & Mehl, Matthias & Srinivasan, Karthik & Gilligan, Brian & Arens, Edward, 2018. "Learning occupants’ workplace interactions from wearable and stationary ambient sensing systems," Applied Energy, Elsevier, vol. 230(C), pages 42-51.
    4. Romero Rodríguez, Laura & Sánchez Ramos, José & Álvarez Domínguez, Servando & Eicker, Ursula, 2018. "Contributions of heat pumps to demand response: A case study of a plus-energy dwelling," Applied Energy, Elsevier, vol. 214(C), pages 191-204.
    5. Lee, Junghun & Yoo, Seunghwan & Kim, Jonghun & Song, Doosam & Jeong, Hakgeun, 2018. "Improvements to the customer baseline load (CBL) using standard energy consumption considering energy efficiency and demand response," Energy, Elsevier, vol. 144(C), pages 1052-1063.
    6. Alimohammadisagvand, Behrang & Jokisalo, Juha & Kilpeläinen, Simo & Ali, Mubbashir & Sirén, Kai, 2016. "Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control," Applied Energy, Elsevier, vol. 174(C), pages 275-287.
    7. Du, Y.F. & Jiang, L. & Li, Y.Z. & Counsell, J. & Smith, J.S., 2016. "Multi-objective demand side scheduling considering the operational safety of appliances," Applied Energy, Elsevier, vol. 179(C), pages 864-874.
    8. Anilkumar, T.T. & Simon, Sishaj P. & Padhy, Narayana Prasad, 2017. "Residential electricity cost minimization model through open well-pico turbine pumped storage system," Applied Energy, Elsevier, vol. 195(C), pages 23-35.
    9. Lu Pang & Cheng Hu & Juan Yu & Haijun Jiang, 2022. "Fixed-Time Synchronization for Fuzzy-Based Impulsive Complex Networks," Mathematics, MDPI, vol. 10(9), pages 1-16, May.
    10. Hartmann, Bálint & Divényi, Dániel & Vokony, István, 2018. "Evaluation of business possibilities of energy storage at commercial and industrial consumers – A case study," Applied Energy, Elsevier, vol. 222(C), pages 59-66.
    11. Gianluca Fulli & Marcelo Masera & Catalin Felix Covrig & Francesco Profumo & Ettore Bompard & Tao Huang, 2017. "The EU Electricity Security Decision-Analytic Framework: Status and Perspective Developments," Energies, MDPI, vol. 10(4), pages 1-20, March.
    12. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
    13. Ma, Li & Wang, Lingfeng & Liu, Zhaoxi, 2021. "Multi-level trading community formation and hybrid trading network construction in local energy market," Applied Energy, Elsevier, vol. 285(C).
    14. Jennifer Date & José A. Candanedo & Andreas K. Athienitis, 2021. "A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building through Predictive Control of Passive and Active Storage," Energies, MDPI, vol. 14(5), pages 1-28, March.
    15. Wu, Di & Radhakrishnan, Nikitha & Huang, Sen, 2019. "A hierarchical charging control of plug-in electric vehicles with simple flexibility model," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. Claudio M. Rocco & Kash Barker & Jose Moronta, 2022. "Determining the best algorithm to detect community structures in networks: application to power systems," Environment Systems and Decisions, Springer, vol. 42(2), pages 251-264, June.
    17. Gangwal, Utkarsh & Singh, Mayank & Pandey, Pradumn Kumar & Kamboj, Deepak & Chatterjee, Samrat & Bhatia, Udit, 2022. "Identifying early-warning indicators of onset of sudden collapse in networked infrastructure systems against sequential disruptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    18. Schaffer, Markus & Vera-Valdés, J. Eduardo & Marszal-Pomianowska, Anna, 2024. "Exploring smart heat meter data: A co-clustering driven approach to analyse the energy use of single-family houses," Applied Energy, Elsevier, vol. 371(C).
    19. Felten, Björn & Weber, Christoph, 2018. "The value(s) of flexible heat pumps – Assessment of technical and economic conditions," Applied Energy, Elsevier, vol. 228(C), pages 1292-1319.
    20. Drysdale, Brian & Wu, Jianzhong & Jenkins, Nick, 2015. "Flexible demand in the GB domestic electricity sector in 2030," Applied Energy, Elsevier, vol. 139(C), pages 281-290.

    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:185:y:2017:i:p1:p:29-43. 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.