Preface

Abstract

In building refurbishment projects, efficient technologies such as heat pumps are increasingly being used as a substitute for conventional technologies such as condensing boilers, with the aim of reducing carbon emissions and determining operational energy and cost savings. Measured building performance, however, often reveals a significant gap between the predicted energy use (design stage) and actual energy use (operation stage). For this reason, a scalable energy signature modeling approach is presented in this paper to verify building energy performance from measured data. Regression models are built with data at multiple temporal resolutions (monthly and daily) and are used to verify the performance improvement due to smart heating controllers (TRV) and Gas Absorption Heat Pumps (GAHP). The capabilities of energy signature analysis are enhanced by including additional variables in the modeling process and by running the models as “digital twins” with a rolling horizon of 15 days of data. Finally, a regression model for GAHP technology is developed to validate the results measured in the monitoring process in a comparative way. The case study chosen is Hale Court sheltered housing, located in the city of Portsmouth (UK). The results obtained are used to illustrate possible extensions of the use of energy signature modeling, highlighting implications for energy management and innovative building technologies development.

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Identifiers

ORCID for Massimiliano Manfren: orcid.org/0000-0003-1438-970X

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