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A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings

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  • Grillone, Benedetto
  • Mor, Gerard
  • Danov, Stoyan
  • Cipriano, Jordi
  • Sumper, Andreas

Abstract

Methods to obtain accurate estimations of the savings generated by building energy efficiency interventions are a topic of great importance, and considered to be one of the keys to increase capital investments in energy conservation strategies worldwide. In this study, a novel data-driven methodology is proposed for the measurement and verification of energy efficiency savings, with special focus on commercial buildings and facilities. The presented approach involves building use characterization by means of a clustering technique that allows to extract typical consumption profile patterns. These are then used, in combination with an innovative technique to evaluate the building’s weather dependency, to design a model able to provide accurate dynamic estimations of the achieved energy savings. The method was tested on synthetic datasets generated using the building energy simulation software EnergyPlus, as well as on monitoring data from real-world buildings. The results obtained with the proposed methodology were compared with the ones provided by applying the time-of-week-and-temperature (TOWT) model, showing up to 10% CV(RMSE) improvement, depending on the case in analysis. Furthermore, a comparison with the deterministic results provided by EnergyPlus showed that the median estimated savings error was always lower than 3% of the total reporting period consumption, with similar accuracy retained even when reducing the total training data available.

Suggested Citation

  • Grillone, Benedetto & Mor, Gerard & Danov, Stoyan & Cipriano, Jordi & Sumper, Andreas, 2021. "A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings," Applied Energy, Elsevier, vol. 301(C).
    • Handle: RePEc:eee:appene:v:301:y:2021:i:c:s0306261921008862
    DOI: 10.1016/j.apenergy.2021.117502
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    References listed on IDEAS

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    1. Li, Kehua & Ma, Zhenjun & Robinson, Duane & Ma, Jun, 2018. "Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering," Applied Energy, Elsevier, vol. 231(C), pages 331-342.
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    7. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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    Cited by:

    1. Abdurahman Alrobaie & Moncef Krarti, 2022. "A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits," Energies, MDPI, vol. 15(21), pages 1-30, October.
    2. Mirfin, Anthony & Xiao, Xun & Jack, Michael W., 2024. "TOWST: A physics-informed statistical model for building energy consumption with solar gain," Applied Energy, Elsevier, vol. 369(C).
    3. Benedetto Grillone & Gerard Mor & Stoyan Danov & Jordi Cipriano & Florencia Lazzari & Andreas Sumper, 2021. "Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology," Energies, MDPI, vol. 14(17), pages 1-30, September.
    4. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    5. Tzani, Dimitra & Stavrakas, Vassilis & Santini, Marion & Thomas, Samuel & Rosenow, Jan & Flamos, Alexandros, 2022. "Pioneering a performance-based future for energy efficiency: Lessons learnt from a comparative review analysis of pay-for-performance programmes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).

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