The energy performance of dwellings of Dutch non-profit housing associations: Modelling actual energy consumption

•Modelling energy savings of renovations in the Dutch non-profit housing stock.•Assessment of 1.6 million dwellings, 21 building features and 3 empirical models.•Empirical models outperform the theoretical building energy model.•Shortcomings exist when predicting effects of specific energy-saving in...

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Bibliographic Details
Published in:	Energy and buildings Vol. 253; p. 111486
Main Authors:	van der Bent, H.S., van den Brom, P.I., Visscher, H.J., Meijer, A., Mouter, N.
Format:	Journal Article
Language:	English
Published:	Lausanne Elsevier B.V 15-12-2021 Elsevier BV
Subjects:	Actual energy consumption Dwellings Empirical prediction models Energy conservation Energy consumption Energy performance Heat exchangers Heat pumps Housing Housing Associations Learning algorithms Machine learning Non-profit housing Regression analysis Regression models Renovation Non-profit housing Empirical prediction models Energy performance Actual energy consumption
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Summary:	•Modelling energy savings of renovations in the Dutch non-profit housing stock.•Assessment of 1.6 million dwellings, 21 building features and 3 empirical models.•Empirical models outperform the theoretical building energy model.•Shortcomings exist when predicting effects of specific energy-saving interventions. In Europe, the energy performance of dwellings is measured using theoretical building energy models based on the Energy Performance of Buildings Directive (EPBD), which estimates the energy consumption of dwellings. However, literature shows large performance gaps between the theoretically predicted energy consumption and the actual energy consumption of dwellings. The goal of this paper is to investigate the extent to which empirical models provide more accurate estimations of actual energy consumption when compared to a theoretical building energy model, in order to estimate average actual energy savings of renovations. We used the Dutch non-profit housing stock to demonstrate the results. We examined three empirical models to predict the actual energy consumption of dwellings: a linear regression model, a non-linear regression model, and a machine learning model (GBM). This paper shows that these three models alleviate the performance gap by giving a good prediction of actual energy consumption on sectoral cross-sections. However, these models still have shortcomings when predicting the effects of specific renovation interventions, for example newly introduced heat pumps. The non-linear and machine learning model (GBM) outperform the theoretical model in terms of estimating energy savings through renovation interventions.
ISSN:	0378-7788 1872-6178
DOI:	10.1016/j.enbuild.2021.111486