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Decision Support Tool to Enable Real-Time Data-Driven Building Energy Retrofitting Design

Author

Listed:
  • Kalevi Piira

    (VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland)

  • Julia Kantorovitch

    (VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland)

  • Lotta Kannari

    (VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland)

  • Jouko Piippo

    (VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland)

  • Nam Vu Hoang

    (VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland)

Abstract

The availability of near-real-time data on energy performance is opening new opportunities to optimize buildings’ energy efficiency and flexibility capabilities and to support the decision-making and planning process of building retrofitting infrastructure investment. Existing tools can support retrofitting design and energy performance contracting. However, there are well-recognized shortcomings of these tools related to their usability, complexity, and ability to perform calculations based on the real-time energy performance of buildings. To address this gap, the advanced retrofitting decision support tool is developed and presented in this study. The strengths of our solution rely on easy usability, accuracy, and transparency of results. The automatic collection of real-time building energy consumption data gathered from the building management systems, combined with data analytics techniques, ensures ease of use and quickness of calculation. These results support step-by-step thinking for retrofitting design and hopefully enable a larger utilization rate for deep building retrofits.

Suggested Citation

  • Kalevi Piira & Julia Kantorovitch & Lotta Kannari & Jouko Piippo & Nam Vu Hoang, 2022. "Decision Support Tool to Enable Real-Time Data-Driven Building Energy Retrofitting Design," Energies, MDPI, vol. 15(15), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5408-:d:872580
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    References listed on IDEAS

    as
    1. Hong, Tianzhen & Piette, Mary Ann & Chen, Yixing & Lee, Sang Hoon & Taylor-Lange, Sarah C. & Zhang, Rongpeng & Sun, Kaiyu & Price, Phillip, 2015. "Commercial Building Energy Saver: An energy retrofit analysis toolkit," Applied Energy, Elsevier, vol. 159(C), pages 298-309.
    2. Maia, Iná & Kranzl, Lukas & Müller, Andreas, 2021. "New step-by-step retrofitting model for delivering optimum timing," Applied Energy, Elsevier, vol. 290(C).
    3. Lee, Sang Hoon & Hong, Tianzhen & Piette, Mary Ann & Taylor-Lange, Sarah C., 2015. "Energy retrofit analysis toolkits for commercial buildings: A review," Energy, Elsevier, vol. 89(C), pages 1087-1100.
    4. Lotta Kannari & Jussi Kiljander & Kalevi Piira & Jouko Piippo & Pekka Koponen, 2021. "Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator," Forecasting, MDPI, vol. 3(2), pages 1-13, April.
    5. Alex Gonzalez Caceres & Muriel Diaz, 2018. "Usability of the EPC Tools for the Profitability Calculation of a Retrofitting in a Residential Building," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    6. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    7. Deng, S. & Wang, R.Z. & Dai, Y.J., 2014. "How to evaluate performance of net zero energy building – A literature research," Energy, Elsevier, vol. 71(C), pages 1-16.
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