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Unsupervised learning of load signatures to estimate energy-related building features using surrogate modelling techniques

  • Research Article
  • Advances in Modeling and Simulation Tools
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Abstract

Characterization of an existing building’s energy-related features is critical to inform maintenance and retrofit decisions. However, existing field-scale characterization methods tend to be labour intensive, invasive, and require high fidelity longitudinal data gathered through tightly regulated experiments. This highlights the need for a low cost, scalable, and efficient screening method. This paper puts forward a surrogate model-based approach to rapidly estimate energy-related building features. To this end, EnergyPlus models for 12 midrise office archetypes, all with a rectangular footprint, are developed. Ten thousand variants of each archetype are generated by altering envelope, causal heat gain, and heating, ventilation, and air conditioning operation features. A unique load signature is derived for each variant’s heating and cooling energy use. The parameters of the load signatures are clustered, then each cluster is associated with a set of plausible energy-related features. The accuracy of the results was evaluated using five test buildings not seen by the algorithm. The method could effectively identify building features with reasonable accuracy and no significant degradation in performance across all 12 archetypes.

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Abbreviations

A core :

area of core zones (m2)

A prmtr :

area of perimeter zones (m2)

f afterhours :

fraction on afterhours (%)

f vav,min-sp :

minimum outdoor air fraction (%)

m set up/down :

night cycle

Q casual :

light and plug load (W/m2)

Q clg :

cooling load intensity (W/m2)

Q htg :

heating load intensity (W/m2)

q infil :

infiltration rate (L/(s·m2))

q ven :

ventilation rate (L/(s·m2))

R opaque :

opaque thermal transmittance (m2·°C/W)

SHGC:

solar heat gain coefficient

S htg,summer :

summer heating availability

T oa :

outdoor air temperature (°C)

T sa,clg :

supply air temperature while cooling (°C)

T sa,htg :

supply air temperature while heating (°C)

T sa,reset :

supply air reset program (°C)

T unocc,clg :

unoccupied thermostat setpoint while cooling (°C)

T unocc,htg :

unoccupied thermostat setpoint while heating (°C)

U win :

window U-value (W/(m2·°C))

β 1 :

base level heating or cooling energy (W/m2)

β 2 :

rate of change of heating or cooling energy (W/(m2·°C))

β 3 :

heating or cooling change point temperature (°C)

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Acknowledgements

This research is supported by a research contract with the National Research Council Canada (Contract number 996635).

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Authors and Affiliations

  1. Carleton University, Ottawa, K1S 5B6, Canada

    Shane Ferreira, Burak Gunay & Araz Ashouri

  2. National Research Council Canada, Ottawa, K1A 0R6, Canada

    Scott Shillinglaw

Authors
  1. Shane Ferreira
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  2. Burak Gunay
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  3. Araz Ashouri
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  4. Scott Shillinglaw
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Contributions

Araz Ashouri, Burak Gunay, and Scott Shillinglaw all contributed to the study conception and design. Material preparation, data collection and analysis were performed by Shane Ferreira. The first draft of the manuscript was written by Shane Ferreira and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shane Ferreira.

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This study does not contain any studies with human or animal subjects performed by any of the authors.

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Declaration of competing interest

The authors have no competing interests to declare that are relevant to the content of this article.

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Unsupervised learning of load signatures to estimate energy-related building features using surrogate modelling techniques

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Ferreira, S., Gunay, B., Ashouri, A. et al. Unsupervised learning of load signatures to estimate energy-related building features using surrogate modelling techniques. Build. Simul. 16, 1273–1286 (2023). https://doi.org/10.1007/s12273-023-1005-5

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  • DOI: https://doi.org/10.1007/s12273-023-1005-5

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