From annual benchmarks to dynamic behaviour: a scalable multi-level analysis framework for energy performance in buildings
From annual benchmarks to dynamic behaviour: a scalable multi-level analysis framework for energy performance in buildings
Attaining net-zero emissions targets for a building stock requires rigorous evaluation of building performance to establish tailored decarbonisation strategies. This paper introduces a multi-level, top-down modelling framework for evaluating individual building energy performance through three distinct temporal scales: yearly benchmarking, monthly interval regressions, and sub-hourly interval analysis for occupancy-related energy consumption. The framework is demonstrated using the Highfield Campus at the University of Southampton (UK), analysing data from 35 buildings monitored from 2017 to 2023, categorised across 9 end-use benchmarks. Yearly analysis utilising CIBSE benchmarks evaluated performance variability Pre-, During-, and Post-COVID pandemic, indicating the impact of occupancy pattern changes after weather normalisation. Monthly interpretable change-point regression model analysis was employed to identify heating balance points and temperature dependence. Observed changes in balance point temperature were attributed to reduced internal gains from lower occupancy. Finally, sub-hourly electricity data provided deeper insights into behavioural and operational variability, capturing the impact of hybrid working on building usage. To illustrate the framework, two buildings are discussed. Yearly data suggested no pandemic-related impact, yet granular analysis revealed otherwise. Monthly results Post-COVID showed heating-related reductions, with the thermal balance point decreasing by 1.8 °C and 2.5 °C respectively. Sub-hourly electricity usage Post-COVID during occupied hours was 107 points lower and 53 points lower respectively, with greater variability (around 5 points). Key findings underscore that granular analysis provides valuable insights missed by yearly benchmarks, enabling targeted efficiency measures. Further research should focus on integrating these into BMS to support decarbonisation strategies.
Data-driven energy modelling, Decarbonisation, Energy Analytics, Energy Efficiency, Interpretable machine-learning, Top-down energy modelling
Gonzalez Carreon, Karla M.
83974d01-7128-4e9a-9895-edff89cd9a31
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Ridett, Ellis
ed32cf3e-5ce3-4596-ba28-0e7bc3794b18
James, Patrick A.B.
da0be14a-aa63-46a7-8646-a37f9a02a71b
15 February 2026
Gonzalez Carreon, Karla M.
83974d01-7128-4e9a-9895-edff89cd9a31
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Ridett, Ellis
ed32cf3e-5ce3-4596-ba28-0e7bc3794b18
James, Patrick A.B.
da0be14a-aa63-46a7-8646-a37f9a02a71b
Gonzalez Carreon, Karla M., Manfren, Massimiliano, Ridett, Ellis and James, Patrick A.B.
(2026)
From annual benchmarks to dynamic behaviour: a scalable multi-level analysis framework for energy performance in buildings.
Energy and Buildings, 353, [116894].
(doi:10.1016/j.enbuild.2025.116894).
Abstract
Attaining net-zero emissions targets for a building stock requires rigorous evaluation of building performance to establish tailored decarbonisation strategies. This paper introduces a multi-level, top-down modelling framework for evaluating individual building energy performance through three distinct temporal scales: yearly benchmarking, monthly interval regressions, and sub-hourly interval analysis for occupancy-related energy consumption. The framework is demonstrated using the Highfield Campus at the University of Southampton (UK), analysing data from 35 buildings monitored from 2017 to 2023, categorised across 9 end-use benchmarks. Yearly analysis utilising CIBSE benchmarks evaluated performance variability Pre-, During-, and Post-COVID pandemic, indicating the impact of occupancy pattern changes after weather normalisation. Monthly interpretable change-point regression model analysis was employed to identify heating balance points and temperature dependence. Observed changes in balance point temperature were attributed to reduced internal gains from lower occupancy. Finally, sub-hourly electricity data provided deeper insights into behavioural and operational variability, capturing the impact of hybrid working on building usage. To illustrate the framework, two buildings are discussed. Yearly data suggested no pandemic-related impact, yet granular analysis revealed otherwise. Monthly results Post-COVID showed heating-related reductions, with the thermal balance point decreasing by 1.8 °C and 2.5 °C respectively. Sub-hourly electricity usage Post-COVID during occupied hours was 107 points lower and 53 points lower respectively, with greater variability (around 5 points). Key findings underscore that granular analysis provides valuable insights missed by yearly benchmarks, enabling targeted efficiency measures. Further research should focus on integrating these into BMS to support decarbonisation strategies.
Text
Ener_Build_Gonzalez_2026_author_submission
- Accepted Manuscript
Restricted to Repository staff only until 21 December 2027.
Request a copy
More information
Accepted/In Press date: 19 December 2025
e-pub ahead of print date: 20 December 2025
Published date: 15 February 2026
Keywords:
Data-driven energy modelling, Decarbonisation, Energy Analytics, Energy Efficiency, Interpretable machine-learning, Top-down energy modelling
Identifiers
Local EPrints ID: 509459
URI: http://eprints.soton.ac.uk/id/eprint/509459
ISSN: 0378-7788
PURE UUID: 2e233d65-090a-4230-875a-611b7ba3ec29
Catalogue record
Date deposited: 23 Feb 2026 17:54
Last modified: 24 Feb 2026 03:07
Export record
Altmetrics
Contributors
Author:
Karla M. Gonzalez Carreon
Author:
Ellis Ridett
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics