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From in-situ measurement to regression and time series models: an overview of trends and prospects for building performance modelling

From in-situ measurement to regression and time series models: an overview of trends and prospects for building performance modelling
From in-situ measurement to regression and time series models: an overview of trends and prospects for building performance modelling

Data analysis methodologies are crucial to learn insights from data and to create more trust in the assumptions used for energy performance assessment. Indeed, continuous performance monitoring should become a more diffuse practice in order to improve our design and operation strategies for the future. This is an essential step to reduce incrementally the gap between simulated and measured performance. In fact, assumptions in simulation represent a significant source of uncertainty when estimating the energy performance of buildings. This uncertainty affects decision-making processes in multiple ways, from design of new and refurbished buildings to policy making. The research presented aims to highlight potential links between experimental approaches for test-facilities and methods and tools used for continuous performance monitoring, at the state of the art. In particular, we start by exploring the relation between in-situ measurement of thermal transmittance (U) and regression-based monitoring approaches, such as co-heating test and energy signature, for heat load coefficient (HLC) and solar aperture (gA) estimation. After that, we highlight some recent developments in simplified dynamic energy modelling using lumped parameter models. In particular, we want to underline the scalability of these techniques, considering relevant issues in current integrated engineer design perspective. These issues include, among others, the necessity of limiting the number of a sensors to be installed in buildings, the possibility of employing both experimental and real operation data (and compare them with design data as well) and, finally, the possibility to automate performance monitoring at multiple scales, from single components, to individual buildings, to building stock and cities.

building performance monitoring, co-heating test, energy signature, in situ measurements, regression models, time series models
1-8
American Institute of Physics
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Nastasi, Benedetto
0d19eabe-134e-4cbe-9912-ff4c095410cd
Salame, Chafic-Touma
Shaban, Auday Hattem
Papageorgas, Panagiotis
Aillerie, Michel
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Nastasi, Benedetto
0d19eabe-134e-4cbe-9912-ff4c095410cd
Salame, Chafic-Touma
Shaban, Auday Hattem
Papageorgas, Panagiotis
Aillerie, Michel

Manfren, Massimiliano and Nastasi, Benedetto (2019) From in-situ measurement to regression and time series models: an overview of trends and prospects for building performance modelling. Salame, Chafic-Touma, Shaban, Auday Hattem, Papageorgas, Panagiotis and Aillerie, Michel (eds.) In Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES 2019. vol. 2123, American Institute of Physics. pp. 1-8 . (doi:10.1063/1.5117027).

Record type: Conference or Workshop Item (Paper)

Abstract

Data analysis methodologies are crucial to learn insights from data and to create more trust in the assumptions used for energy performance assessment. Indeed, continuous performance monitoring should become a more diffuse practice in order to improve our design and operation strategies for the future. This is an essential step to reduce incrementally the gap between simulated and measured performance. In fact, assumptions in simulation represent a significant source of uncertainty when estimating the energy performance of buildings. This uncertainty affects decision-making processes in multiple ways, from design of new and refurbished buildings to policy making. The research presented aims to highlight potential links between experimental approaches for test-facilities and methods and tools used for continuous performance monitoring, at the state of the art. In particular, we start by exploring the relation between in-situ measurement of thermal transmittance (U) and regression-based monitoring approaches, such as co-heating test and energy signature, for heat load coefficient (HLC) and solar aperture (gA) estimation. After that, we highlight some recent developments in simplified dynamic energy modelling using lumped parameter models. In particular, we want to underline the scalability of these techniques, considering relevant issues in current integrated engineer design perspective. These issues include, among others, the necessity of limiting the number of a sensors to be installed in buildings, the possibility of employing both experimental and real operation data (and compare them with design data as well) and, finally, the possibility to automate performance monitoring at multiple scales, from single components, to individual buildings, to building stock and cities.

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TMREES19_paper_187_Manfren - Accepted Manuscript
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More information

Accepted/In Press date: 7 April 2019
e-pub ahead of print date: 17 July 2019
Venue - Dates: International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability 2019, TMREES 2019, Beirut, Lebanon, 2019-04-10 - 2019-04-12
Keywords: building performance monitoring, co-heating test, energy signature, in situ measurements, regression models, time series models

Identifiers

Local EPrints ID: 433207
URI: https://eprints.soton.ac.uk/id/eprint/433207
PURE UUID: a2741539-7558-4d25-9513-765786022a28
ORCID for Massimiliano Manfren: ORCID iD orcid.org/0000-0003-1438-970X

Catalogue record

Date deposited: 12 Aug 2019 16:30
Last modified: 29 Aug 2019 00:26

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Contributors

Author: Benedetto Nastasi
Editor: Chafic-Touma Salame
Editor: Auday Hattem Shaban
Editor: Panagiotis Papageorgas
Editor: Michel Aillerie

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