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A platform for research: civil engineering, architecture and urbanism
Unsupervised learning of load signatures to estimate energy-related building features using surrogate modelling techniques
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.
Unsupervised learning of load signatures to estimate energy-related building features using surrogate modelling techniques
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.
Unsupervised learning of load signatures to estimate energy-related building features using surrogate modelling techniques
Build. Simul.
Ferreira, Shane (author) / Gunay, Burak (author) / Ashouri, Araz (author) / Shillinglaw, Scott (author)
Building Simulation ; 16 ; 1273-1286
2023-07-01
14 pages
Article (Journal)
Electronic Resource
English
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