Understanding the performance gap in energy retrofitting: Measured input data for adjusting building simulation models

Abstract This paper focuses on exploring methods for reducing the gap between the expected and actual building energy performance by using simulation tools. The study has two purposes. The first is to quantify the relative effect of the different building parameters measured on the energy heating and cooling consumption compared with standard parameters through the adjustment of simulation models. The second is to develop an approach, based on three methods, for monitoring residential buildings, while also testing and calibrating methodologies for the simulation software. The approach developed is applied and tested in two real case studies (two apartments in two identically constructed buildings, one refurbished and the other not) in the city of Madrid, Spain. The analysis of the case studies shows that there is a four-fold difference in potential savings in energy for heating between models adjusted with standard and actual parameters. Moreover, the results reveal the significant impact of the use of actual weather data and users’ behaviour in the adjustment of simulation models and demonstrate the utility of the application of these methods.

[1]  Martin Fischer,et al.  Parametric analysis of design stage building energy performance simulation models , 2018, Energy and Buildings.

[2]  Zaid Chalabi,et al.  The relative importance of input weather data for indoor overheating risk assessment in dwellings , 2014 .

[3]  Michael E. Webber,et al.  Using BEopt (EnergyPlus) with energy audits and surveys to predict actual residential energy usage , 2015 .

[4]  T. Konstantinou,et al.  Considering user profiles and occupants’ behaviour on a zero energy renovation strategy for multi-family housing in the Netherlands , 2018 .

[5]  Bojan Milovanović,et al.  Comparison of dynamic simulations and the ISO 52016 standard for the assessment of building energy performance , 2019, Applied Energy.

[6]  Pieter de Wilde,et al.  Building Performance Analysis , 2018 .

[7]  Tatjana Vilutiene,et al.  Modelling the Effect of the Domestic Occupancy Profiles on Predicted Energy Demand of the Energy Efficient House , 2013 .

[8]  C. Tweed,et al.  Interdisciplinary perspectives on building thermal performance , 2018 .

[9]  Maria Kolokotroni,et al.  Using localised weather files to assess overheating in naturally ventilated offices within London's urban heat island , 2012 .

[10]  Hiroshi Yoshino,et al.  IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods , 2017 .

[11]  Rajat Gupta,et al.  Understanding occupants: feedback techniques for large-scale low-carbon domestic refurbishments , 2010 .

[12]  Olivia Guerra-Santin,et al.  Relationship Between Building Technologies, Energy Performance and Occupancy in Domestic Buildings , 2017 .

[13]  Dino Bouchlaghem,et al.  Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap , 2012 .

[14]  Rory V. Jones,et al.  The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings , 2015 .

[15]  Jones,et al.  The gap between simulated and measured energy performance: A case study across six identical new-build flats in the UK , 2015 .

[16]  Juan J. Sendra,et al.  Towards a calibration of building energy models: A case study from the Spanish housing stock in the Mediterranean climate , 2015 .

[17]  Olivia Guerra Santin,et al.  Behavioural Patterns and User Profiles related to energy consumption for heating , 2011 .

[18]  Natale Arcuri,et al.  Behavioral variables and occupancy patterns in the design and modeling of Nearly Zero Energy Buildings , 2017 .

[19]  Dirk Saelens,et al.  Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour , 2016 .

[20]  Francesca Stazi,et al.  Experimental comparison between 3 different traditional wall constructions and dynamic simulations to identify optimal thermal insulation strategies , 2013 .

[21]  Jan Carmeliet,et al.  Brick Cavity Walls: A Performance Analysis Based on Measurements and Simulations , 2007 .

[22]  J. New,et al.  Evaluation of weather datasets for building energy simulation , 2012 .

[23]  Paul Raftery,et al.  A review of methods to match building energy simulation models to measured data , 2014 .

[24]  Zhiqiang Zhai,et al.  Advances in building simulation and computational techniques: A review between 1987 and 2014 , 2016 .

[25]  Hans Auer,et al.  The impact of consumer behavior on residential energy demand for space heating , 1998 .

[26]  Olivia Guerra-Santin,et al.  Comparing the impact of presence patterns on energy demand in residential buildings using measured data and simulation models , 2019, Building Simulation.

[27]  Mohammed Al-Khawaja,et al.  Determination and selecting the optimum thickness of insulation for buildings in hot countries by accounting for solar radiation , 2004 .

[28]  Olivia Guerra-Santin,et al.  Learning from design reviews in low energy buildings , 2014 .

[29]  Soteris A. Kalogirou,et al.  Comparison between measured and calculated energy performance for dwellings in a summer dominant environment , 2011 .

[30]  Tianzhen Hong,et al.  A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures , 2017 .

[31]  Juan J. Sendra,et al.  On the assessment of the energy performance and environmental behaviour of social housing stock for the adjustment between simulated and measured data: The case of mild winters in the Mediterranean climate of southern Europe , 2017 .

[32]  Pieter de Wilde,et al.  The gap between predicted and measured energy performance of buildings: A framework for investigation , 2014 .

[33]  O. Guerra-Santin,et al.  Occupants' behaviour: determinants and effects on residential heating consumption , 2010 .

[34]  Juan J. Sendra,et al.  An approach to modelling envelope airtightness in multi-family social housing in Mediterranean Europe based on the situation in Spain , 2016 .

[35]  Tony Roskilly,et al.  This Work Is Licensed under a Creative Commons Attribution 4.0 International License Royapoor M, Roskilly T. Building Model Calibration Using Energy and Environmental Data. Energy and Buildings Building Model Calibration Using Energy and Environmental Data Keywords: Model Calibration Measured Energy , 2022 .

[36]  Afif Hasan,et al.  Optimizing insulation thickness for buildings using life cycle cost , 1999 .

[37]  J. B. Siviour,et al.  Experimental U-values of some house walls , 1994 .

[38]  Ray Galvin,et al.  Introducing the prebound effect: the gap between performance and actual energy consumption , 2012 .

[39]  Liwei Tian,et al.  A study on optimum insulation thicknesses of external walls in hot summer and cold winter zone of China , 2009 .

[40]  Hugo Hens,et al.  Comparison of measurements and simulations of a passive house , 2005 .

[41]  Joseph Andrew Clarke,et al.  Energy Simulation in Building Design , 1985 .

[42]  Tianzhen Hong,et al.  Occupancy schedules learning process through a data mining framework , 2015 .

[43]  Laurent Georges,et al.  Influence of occupant’s behavior on heating needs and energy system performance: A case of well-insulated detached houses in cold climates , 2015 .

[44]  Samuel Domínguez-Amarillo,et al.  Social housing airtightness in Southern Europe , 2019, Energy and Buildings.

[45]  P. E. Condon,et al.  In-situ measurements of residential energy performance using electric co-heating , 1980 .

[46]  F. J. Neila González,et al.  Definiendo patrones de ocupación mediante la monitorización de edificios existentes , 2017 .

[47]  Tianzhen Hong,et al.  A data-mining approach to discover patterns of window opening and closing behavior in offices , 2014 .

[48]  Alex Summerfield,et al.  The reality of English living rooms - A comparison of internal temperatures against common model assumptions , 2013 .

[49]  Henk Visscher,et al.  The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock , 2009 .

[50]  Paul Raftery,et al.  Calibrating whole building energy models: Detailed case study using hourly measured data , 2011 .

[51]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[52]  Lisa Guan,et al.  Preparation of future weather data to study the impact of climate change on buildings , 2009 .

[53]  Ak Persily,et al.  Measuring Airflow Rates with Pulse Tracer Techniques , 1990 .

[54]  Bernard Marie Lachal,et al.  Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data , 2004 .

[55]  Niccolò Aste,et al.  The influence of the external walls thermal inertia on the energy performance of well insulated buildings , 2009 .

[56]  P. James,et al.  Developing English domestic occupancy profiles , 2019 .

[57]  Adrian Leaman,et al.  Evaluating housing performance in relation to human behaviour: new challenges , 2010 .

[58]  Horace Herring,et al.  Energy efficiency and sustainable consumption : the rebound effect , 2009 .

[59]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[60]  Juan J. Sendra,et al.  Protocols for measuring the airtightness of multi-dwelling units in Southern Europe , 2011 .