IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p390-d1018996.html
   My bibliography  Save this article

Building Energy Simulation and Monitoring: A Review of Graphical Data Representation

Author

Listed:
  • Ofelia Vera-Piazzini

    (Laboratory of Building Physics, Università Iuav di Venezia, Via Torino, 153/A, 30172 Venezia, Italy)

  • Massimiliano Scarpa

    (Department of Architecture and Arts, Università Iuav di Venezia, Dorsoduro 2206, 30123 Venezia, Italy)

  • Fabio Peron

    (Laboratory of Building Physics, Università Iuav di Venezia, Via Torino, 153/A, 30172 Venezia, Italy)

Abstract

Data visualization has become relevant in the framework of the evolution of big data analysis. Being able to understand data collected in a dynamic, interactive, and personalized way allows for better decisions to be made when optimizing and improving performance. Although its importance is known, there is a gap in the research regarding its design, choice criteria, and uses in the field of building energy consumption. Therefore, this review discusses the state-of-the-art of visualization techniques used in the field of energy performance, in particular by considering two types of building analysis: simulation and monitoring. Likewise, data visualizations are categorized according to goals, level of detail and target users. Visualization tools published in the scientific literature, as well as those currently used in the IoT platforms and visualization software, were analyzed. This overview can be used as a starting point when choosing the most efficient data visualization for a specific type of building energy analysis.

Suggested Citation

  • Ofelia Vera-Piazzini & Massimiliano Scarpa & Fabio Peron, 2022. "Building Energy Simulation and Monitoring: A Review of Graphical Data Representation," Energies, MDPI, vol. 16(1), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:390-:d:1018996
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/390/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/390/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Costa, Andrea & Keane, Marcus M. & Torrens, J. Ignacio & Corry, Edward, 2013. "Building operation and energy performance: Monitoring, analysis and optimisation toolkit," Applied Energy, Elsevier, vol. 101(C), pages 310-316.
    2. Koldobika Martin-Escudero & Garazi Atxalandabaso & Aitor Erkoreka & Amaia Uriarte & Matteo Porta, 2021. "Comparison between Energy Simulation and Monitoring Data in an Office Building," Energies, MDPI, vol. 15(1), pages 1-24, December.
    3. Dario Cottafava & Giulia Sonetti & Paolo Gambino & Andrea Tartaglino, 2018. "Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms," Energies, MDPI, vol. 11(5), pages 1-18, May.
    4. Yanxue Li & Weijun Gao & Yingjun Ruan & Yoshiaki Ushifusa, 2018. "Grid Load Shifting and Performance Assessments of Residential Efficient Energy Technologies, a Case Study in Japan," Sustainability, MDPI, vol. 10(7), pages 1-19, June.
    5. Giuseppe Desogus & Emanuela Quaquero & Giulia Rubiu & Gianluca Gatto & Cristian Perra, 2021. "BIM and IoT Sensors Integration: A Framework for Consumption and Indoor Conditions Data Monitoring of Existing Buildings," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    6. Tae-Keun Oh & Donghwan Lee & Minsoo Park & Gichun Cha & Seunghee Park, 2018. "Three-Dimensional Visualization Solution to Building-Energy Diagnosis for Energy Feedback," Energies, MDPI, vol. 11(7), pages 1-18, July.
    7. Francisco, Abigail & Truong, Hanh & Khosrowpour, Ardalan & Taylor, John E. & Mohammadi, Neda, 2018. "Occupant perceptions of building information model-based energy visualizations in eco-feedback systems," Applied Energy, Elsevier, vol. 221(C), pages 220-228.
    Full references (including those not matched with items on IDEAS)

    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. Chalal, M.L. & Medjdoub, B. & Bezai, N. & Bull, R. & Zune, M., 2022. "Visualisation in energy eco-feedback systems: A systematic review of good practice," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    2. Gao, Hao & Koch, Christian & Wu, Yupeng, 2019. "Building information modelling based building energy modelling: A review," Applied Energy, Elsevier, vol. 238(C), pages 320-343.
    3. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    4. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    5. Ágota Bányai & Tamás Bányai, 2022. "Real-Time Maintenance Policy Optimization in Manufacturing Systems: An Energy Efficiency and Emission-Based Approach," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    6. Ciro Aprea & Laura Canale & Marco Dell’Isola & Giorgio Ficco & Andrea Frattolillo & Angelo Maiorino & Fabio Petruzziello, 2023. "On the Use of Ultrasonic Flowmeters for Cooling Energy Metering and Sub-Metering in Direct Expansion Systems," Energies, MDPI, vol. 16(12), pages 1-16, June.
    7. Li, Yanxue & Zhang, Xiaoyi & Gao, Weijun & Xu, Wenya & Wang, Zixuan, 2022. "Operational performance and grid-support assessment of distributed flexibility practices among residential prosumers under high PV penetration," Energy, Elsevier, vol. 238(PB).
    8. Sameh Monna & Adel Juaidi & Ramez Abdallah & Aiman Albatayneh & Patrick Dutournie & Mejdi Jeguirim, 2021. "Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine," Energies, MDPI, vol. 14(13), pages 1-13, June.
    9. Jing Zhao & Yu Shan, 2020. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System," Energies, MDPI, vol. 13(3), pages 1-17, January.
    10. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).
    11. Habib Sadri & Ibrahim Yitmen & Lavinia Chiara Tagliabue & Florian Westphal & Algan Tezel & Afshin Taheri & Goran Sibenik, 2023. "Integration of Blockchain and Digital Twins in the Smart Built Environment Adopting Disruptive Technologies—A Systematic Review," Sustainability, MDPI, vol. 15(4), pages 1-46, February.
    12. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    13. James Ogundiran & Ehsan Asadi & Manuel Gameiro da Silva, 2024. "A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings," Sustainability, MDPI, vol. 16(9), pages 1-30, April.
    14. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
    15. Doussoulin, Jean Pierre & Bittencourt, Mariana, 2022. "How effective is the construction sector in promoting the circular economy in Brazil and France? : A waste input-output analysis," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 47-58.
    16. Li, Haorong & Yu, Yuebin & Niu, Fuxin & Shafik, Michel & Chen, Bing, 2014. "Performance of a coupled cooling system with earth-to-air heat exchanger and solar chimney," Renewable Energy, Elsevier, vol. 62(C), pages 468-477.
    17. Gao, Kangping & Xu, Xinxin & Jiao, Shengjie, 2022. "Prediction and visualization analysis of drilling energy consumption based on mechanism and data hybrid drive," Energy, Elsevier, vol. 261(PA).
    18. Joaquim Amândio Azevedo & Filipe Edgar Santos, 2021. "A More Efficient Technique to Power Home Monitoring Systems Using Controlled Battery Charging," Energies, MDPI, vol. 14(13), pages 1-16, June.
    19. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    20. Siiri Söyrinki & Eva Heiskanen & Kaisa Matschoss, 2018. "Piloting Demand Response in Retailing: Lessons Learned in Real-Life Context," Sustainability, MDPI, vol. 10(10), pages 1-17, October.

    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:16:y:2022:i:1:p:390-:d:1018996. 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.