IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p9257-d995443.html
   My bibliography  Save this article

A Novel Temperature-Independent Model for Estimating the Cooling Energy in Residential Homes for Pre-Cooling and Solar Pre-Cooling

Author

Listed:
  • Simon Heslop

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Baran Yildiz

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Mike Roberts

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Dong Chen

    (The Commonwealth Scientific and Industrial Research Organisation, Melbourne, VIC 3190, Australia)

  • Tim Lau

    (UniSA STEM, University of South Australia, Adelaide, SA 5095, Australia)

  • Shayan Naderi

    (School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia)

  • Anna Bruce

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Iain MacGill

    (School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia)

  • Renate Egan

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

Abstract

Australia’s electricity networks are experiencing low demand during the day due to excessive residential solar export and high demand during the evening on days of extreme temperature due to high air conditioning use. Pre-cooling and solar pre-cooling are demand-side management strategies with the potential to address both these issues. However, there remains a lack of comprehensive studies into the potential of pre-cooling and solar pre-cooling due to a lack of data. In Australia, however, extensive datasets of household energy measurements, including consumption and generation from rooftop solar, obtained through retailer-owned smart meters and household-owned third-party monitoring devices, are now becoming available. However, models presented in the literature which could be used to simulate the cooling energy in residential homes are temperature-based, requiring indoor temperature as an input. Temperature-based models are, therefore, precluded from being able to use these newly available and extensive energy-based datasets, and there is a need for the development of new energy-based simulation tools. To address this gap, a novel data-driven model to estimate the cooling energy in residential homes is proposed. The model is temperature-independent, requiring only energy-based datasets for input. The proposed model was derived by an analysis comparing the internal free-running and air conditioned temperature data and the air conditioning data for template residential homes generated by AccuRate, a building energy simulation tool. The model is comprised of four linear equations, where their respective slope intercepts represent a thermal efficiency metric of a thermal zone in the template residential home. The model can be used to estimate the difference between the internal free-running, and air conditioned temperature, which is equivalent to the cooling energy in the thermal zone. Error testing of the model compared the difference between the estimated and AccuRate air conditioned temperature and gave average CV-RMSE and MAE values of 22% and 0.3 °C, respectively. The significance of the model is that the slope intercepts for a template home can be applied to an actual residential home with equivalent thermal efficiency, and a pre-cooling or solar pre-cooling analysis is undertaken using the model in combination with the home’s energy-based dataset. The model is, therefore, able to utilise the newly available extensive energy-based datasets for comprehensive studies on pre-cooling and solar pre-cooling of residential homes.

Suggested Citation

  • Simon Heslop & Baran Yildiz & Mike Roberts & Dong Chen & Tim Lau & Shayan Naderi & Anna Bruce & Iain MacGill & Renate Egan, 2022. "A Novel Temperature-Independent Model for Estimating the Cooling Energy in Residential Homes for Pre-Cooling and Solar Pre-Cooling," Energies, MDPI, vol. 15(23), pages 1-18, December.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9257-:d:995443
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/9257/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/23/9257/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nelson, James & Johnson, Nathan G. & Chinimilli, Prudhvi Tej & Zhang, Wenlong, 2019. "Residential cooling using separated and coupled precooling and thermal energy storage strategies," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Romaní, Joaquim & Belusko, Martin & Alemu, Alemu & Cabeza, Luisa F. & de Gracia, Alvaro & Bruno, Frank, 2018. "Optimization of deterministic controls for a cooling radiant wall coupled to a PV array," Applied Energy, Elsevier, vol. 229(C), pages 1103-1110.
    3. O'Shaughnessy, Eric & Cutler, Dylan & Ardani, Kristen & Margolis, Robert, 2018. "Solar plus: Optimization of distributed solar PV through battery storage and dispatchable load in residential buildings," Applied Energy, Elsevier, vol. 213(C), pages 11-21.
    4. Roberts, Mike B. & Bruce, Anna & MacGill, Iain, 2019. "Impact of shared battery energy storage systems on photovoltaic self-consumption and electricity bills in apartment buildings," Applied Energy, Elsevier, vol. 245(C), pages 78-95.
    5. Romaní, Joaquim & Belusko, Martin & Alemu, Alemu & Cabeza, Luisa F. & de Gracia, Alvaro & Bruno, Frank, 2018. "Control concepts of a radiant wall working as thermal energy storage for peak load shifting of a heat pump coupled to a PV array," Renewable Energy, Elsevier, vol. 118(C), pages 489-501.
    6. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    7. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    8. Turner, W.J.N. & Walker, I.S. & Roux, J., 2015. "Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass," Energy, Elsevier, vol. 82(C), pages 1057-1067.
    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. Naderi, Shayan & Heslop, Simon & Chen, Dong & Watts, Scott & MacGill, Iain & Pignatta, Gloria & Sproul, Alistair, 2023. "Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock," Renewable Energy, Elsevier, vol. 216(C).

    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. Marwan, Marwan, 2020. "The impact of probability of electricity price spike and outside temperature to define total expected cost for air conditioning," Energy, Elsevier, vol. 195(C).
    2. Dehwah, Ammar H.A. & Krarti, Moncef, 2022. "Optimal controls of precooling strategies using switchable insulation systems for commercial buildings," Applied Energy, Elsevier, vol. 320(C).
    3. Yang, Yang & Chen, Sarula, 2022. "Thermal insulation solutions for opaque envelope of low-energy buildings: A systematic review of methods and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Hu, Maomao & Xiao, Fu & Jørgensen, John Bagterp & Wang, Shengwei, 2019. "Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 92-106.
    5. Sun, Yue & Luo, Zhiwen & Li, Yu & Zhao, Tianyi, 2024. "Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm," Energy, Elsevier, vol. 296(C).
    6. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    7. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Naderi, Shayan & Heslop, Simon & Chen, Dong & Watts, Scott & MacGill, Iain & Pignatta, Gloria & Sproul, Alistair, 2023. "Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock," Renewable Energy, Elsevier, vol. 216(C).
    9. Wang, Lingshi & Liu, Xiaobing & Yang, Zhiyao & Gluesenkamp, Kyle R., 2020. "Experimental study on a novel three-phase absorption thermal battery with high energy density applied to buildings," Energy, Elsevier, vol. 208(C).
    10. Wei, Zhichen & Calautit, John, 2023. "Predictive control of low-temperature heating system with passive thermal mass energy storage and photovoltaic system: Impact of occupancy patterns and climate change," Energy, Elsevier, vol. 269(C).
    11. Hu, Maomao & Xiao, Fu, 2020. "Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior," Energy, Elsevier, vol. 194(C).
    12. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    13. Tang, Rui & Li, Hangxin & Wang, Shengwei, 2019. "A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage," Applied Energy, Elsevier, vol. 242(C), pages 809-820.
    14. Md Jahidur Rahman & Tahar Tafticht & Mamadou Lamine Doumbia & Iqbal Messaïf, 2023. "Optimal Inverter Control Strategies for a PV Power Generation with Battery Storage System in Microgrid," Energies, MDPI, vol. 16(10), pages 1-36, May.
    15. Bernadette Fina & Hans Auer, 2020. "Economic Viability of Renewable Energy Communities under the Framework of the Renewable Energy Directive Transposed to Austrian Law," Energies, MDPI, vol. 13(21), pages 1-31, November.
    16. Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017. "Energy efficiency-based course timetabling for university buildings," Energy, Elsevier, vol. 139(C), pages 394-405.
    17. Zhu, Jianquan & Xia, Yunrui & Mo, Xiemin & Guo, Ye & Chen, Jiajun, 2021. "A bilevel bidding and clearing model incorporated with a pricing strategy for the trading of energy storage use rights," Energy, Elsevier, vol. 235(C).
    18. Luigi Maffei & Antonio Ciervo & Achille Perrotta & Massimiliano Masullo & Antonio Rosato, 2023. "Innovative Energy-Efficient Prefabricated Movable Buildings for Smart/Co-Working: Performance Assessment upon Varying Building Configurations," Sustainability, MDPI, vol. 15(12), pages 1-37, June.
    19. M. M. Hasan & Shakhawat Hossain & M. Mofijur & Zobaidul Kabir & Irfan Anjum Badruddin & T. M. Yunus Khan & Esam Jassim, 2023. "Harnessing Solar Power: A Review of Photovoltaic Innovations, Solar Thermal Systems, and the Dawn of Energy Storage Solutions," Energies, MDPI, vol. 16(18), pages 1-30, September.
    20. Ahsan, Syed M. & Khan, Hassan A. & Hassan, Naveed-ul & Arif, Syed M. & Lie, Tek-Tjing, 2020. "Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts," Applied Energy, Elsevier, vol. 273(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:gam:jeners:v:15:y:2022:i:23:p:9257-:d:995443. 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.