A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Improving the predictive power of simplified residential space heating demand models : a field data and model driven study
Large discrepancies have been found in different countries when comparing real energy use in houses to the theoretical energy use calculated using energy performance of buildings (EPB) calculation methods. This prediction gap has become a major concern in the residential building sector, where the information provided on energy performance certificates is often the only energy related basis to support investment decisions in construction and renovation. Additionally, the simplified energy performance calculation methods are also often used for building stock analyses to support policy making by analysing potential savings on a regional or national level. Following these concerns, numerous studies have focussed on behavioural and physical causes of these prediction errors, e.g. on rebound effect and physical temperature take-back. While literature agrees on many findings (e.g. the importance of user behaviour), there is still a debate on what part of the error is due to user behaviour and what part to physical modelling errors. This can partly be explained by the fact that the size of the reported prediction gaps varies depending on the local building tradition, building performance levels and performance assessment framework. This dissertation pursues the investigation on the prediction gap between simplified calculation methods and real energy use in houses, focussing on the space heating demand in single-family houses in Belgium. Building on analyses on field data from inhabited houses, a new simplified calculation approach is developed and used for sensitivity analyses. The first part of the study is data-driven, analysing data from surveys of inhabitants, field-measurements, energy bills and official energy performance calculations. Two datasets are analysed. The first dataset, analysed in Chapter 2, contains over 500 randomly selected high-performance houses. The second dataset, analysed in Chapter 3, was collected in two uniform neighbourhoods, one with old uninsulated houses, the other with new and well ...
Improving the predictive power of simplified residential space heating demand models : a field data and model driven study
Large discrepancies have been found in different countries when comparing real energy use in houses to the theoretical energy use calculated using energy performance of buildings (EPB) calculation methods. This prediction gap has become a major concern in the residential building sector, where the information provided on energy performance certificates is often the only energy related basis to support investment decisions in construction and renovation. Additionally, the simplified energy performance calculation methods are also often used for building stock analyses to support policy making by analysing potential savings on a regional or national level. Following these concerns, numerous studies have focussed on behavioural and physical causes of these prediction errors, e.g. on rebound effect and physical temperature take-back. While literature agrees on many findings (e.g. the importance of user behaviour), there is still a debate on what part of the error is due to user behaviour and what part to physical modelling errors. This can partly be explained by the fact that the size of the reported prediction gaps varies depending on the local building tradition, building performance levels and performance assessment framework. This dissertation pursues the investigation on the prediction gap between simplified calculation methods and real energy use in houses, focussing on the space heating demand in single-family houses in Belgium. Building on analyses on field data from inhabited houses, a new simplified calculation approach is developed and used for sensitivity analyses. The first part of the study is data-driven, analysing data from surveys of inhabitants, field-measurements, energy bills and official energy performance calculations. Two datasets are analysed. The first dataset, analysed in Chapter 2, contains over 500 randomly selected high-performance houses. The second dataset, analysed in Chapter 3, was collected in two uniform neighbourhoods, one with old uninsulated houses, the other with new and well ...
Improving the predictive power of simplified residential space heating demand models : a field data and model driven study
Delghust, Marc (author) / Janssens, Arnold / De Weerdt, Yves
2015-01-01
Theses
Electronic Resource
English
prediction gap , heating profiles , quasi-steady-state models , space heating , user behaviour , temperature take-back , Technology and Engineering , residential energy use , energy performance , parametric building typologies , rebound effect , energy performance regulation , Energy Performance of Buildings Directive (EPBD)
DDC:
690
| TIBKAT | 2024
Space heating demand response potential of retrofitted residential apartment blocks
| Online Contents | 2017
Space heating demand response potential of retrofitted residential apartment blocks
| Online Contents | 2017
Hidden Power Drains: Residential Heating and Cooling Fan Power Demand
| British Library Conference Proceedings | 2000