Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0
Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0
Accelerating the decarbonisation of the built environment necessitates increasing electrification of end-uses, which in turn poses the issue of rethinking the role of energy efficiency in conjunction with flexibility in grid interaction. This requires a better understanding of the electricity load profiles at hourly or sub-hourly intervals using techniques that are simple, reliable, and interpretable. To this extent, this study proposes a reformulation of the Time Of Week and Temperature modelling approach. This approach is able to separate the energy consumption dependence on building operational characteristics (Time Of Week) and on weather (outdoor air temperature), through a highly automated modelling workflow, necessitating minimal effort for model tuning. These features, along with its intrinsic interpretability due to its formulation using multivariate regression and the availability of open-source software, makes it an ideal starting point for applied research. The case study selected for the research is a fully electrified public building in Southern Italy. The building has been monitored for 5 years, before, during and after the COVID-19 lockdown. The novel model formulation is calibrated using hourly interval data with a Coefficient of Variation of Root Mean Square Error in the range of 20.0–28.5% throughout the various monitoring periods. The counterfactual analysis of electricity consumption indicates a 10.7–26.7% decrease in electricity consumption due to operational adjustments following COVID-19 lockdown, highlighting the impact of behavioural change. Finally, the possibility of additional workflow automation and enhanced interpretability is discussed.
Data-driven methods, Energy analytics, Energy management, Interpretability, M&V 2.0, Measurement and verification, Regression-based approaches, TOWT
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Nastasi, Benedetto
0d19eabe-134e-4cbe-9912-ff4c095410cd
15 November 2023
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Nastasi, Benedetto
0d19eabe-134e-4cbe-9912-ff4c095410cd
Manfren, Massimiliano and Nastasi, Benedetto
(2023)
Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0.
Energy, 283, [128490].
(doi:10.1016/j.energy.2023.128490).
Abstract
Accelerating the decarbonisation of the built environment necessitates increasing electrification of end-uses, which in turn poses the issue of rethinking the role of energy efficiency in conjunction with flexibility in grid interaction. This requires a better understanding of the electricity load profiles at hourly or sub-hourly intervals using techniques that are simple, reliable, and interpretable. To this extent, this study proposes a reformulation of the Time Of Week and Temperature modelling approach. This approach is able to separate the energy consumption dependence on building operational characteristics (Time Of Week) and on weather (outdoor air temperature), through a highly automated modelling workflow, necessitating minimal effort for model tuning. These features, along with its intrinsic interpretability due to its formulation using multivariate regression and the availability of open-source software, makes it an ideal starting point for applied research. The case study selected for the research is a fully electrified public building in Southern Italy. The building has been monitored for 5 years, before, during and after the COVID-19 lockdown. The novel model formulation is calibrated using hourly interval data with a Coefficient of Variation of Root Mean Square Error in the range of 20.0–28.5% throughout the various monitoring periods. The counterfactual analysis of electricity consumption indicates a 10.7–26.7% decrease in electricity consumption due to operational adjustments following COVID-19 lockdown, highlighting the impact of behavioural change. Finally, the possibility of additional workflow automation and enhanced interpretability is discussed.
Text
1-s2.0-S0360544223018844-main
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More information
Accepted/In Press date: 19 July 2023
e-pub ahead of print date: 25 July 2023
Published date: 15 November 2023
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Publisher Copyright:
© 2023 Elsevier Ltd
Keywords:
Data-driven methods, Energy analytics, Energy management, Interpretability, M&V 2.0, Measurement and verification, Regression-based approaches, TOWT
Identifiers
Local EPrints ID: 481154
URI: http://eprints.soton.ac.uk/id/eprint/481154
ISSN: 0360-5442
PURE UUID: 1c2cc748-d158-48df-a015-a002dc329b46
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Date deposited: 16 Aug 2023 16:49
Last modified: 18 Mar 2024 03:40
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Author:
Benedetto Nastasi
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