The reliability of inverse modelling for the wide scale characterization of the thermal properties of buildings

The reduction of energy use in buildings is a major component of greenhouse gas mitigation policy and requires knowledge of the fabric and the occupant behaviour. Hence there has been a longstanding desire to use automatic means to identify these. Smart metres and the internet-of-things have the potential to do this. This paper describes a study where the ability of inverse modelling to identify building parameters is evaluated for 6 monitored real and 1000 simulated buildings. It was found that low-order models provide good estimates of heat transfer coefficients and internal temperatures if heating, electricity use and CO2 concentration are measured during the winter period. This implies that the method could be used with a small number of cheap sensors and enable the accurate assessment of buildings’ thermal properties, and therefore the impact of any suggested retrofit. This has the potential to be transformative for the energy efficiency industry.

[1]  Cristian Ghiaus.  Equivalence between the load curve and the free-running temperature in energy estimating methods , 2006 .

[2]  D. Kolokotsa,et al.  Predictive control techniques for energy and indoor environmental quality management in buildings , 2009 .

[3]  Robert Lowe,et al.  Evaluating the impact of an enhanced energy performance standard on load-bearing masonry domestic construction: Understanding the gap between designed and real performance: lessons from Stamford Brook. , 2011 .

[4]  Communities.  English House condition Survey , 2013 .

[5]  Henrik Madsen,et al.  Identifying suitable models for the heat dynamics of buildings , 2011 .

[6]  Derek Alien.  Powering the nation , 2007 .

[7]  J. Kämpf,et al.  A simplified thermal model to support analysis of urban resource flows , 2007 .

[8]  Jin Yang,et al.  On-line building energy prediction using adaptive artificial neural networks , 2005 .

[9]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[10]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[11]  Alex Summerfield,et al.  Energy efficiency in the British housing stock: Energy demand and the Homes Energy Efficiency Database , 2013 .

[12]  A. Rabl,et al.  Energy signature models for commercial buildings: test with measured data and interpretation , 1992 .

[13]  Ian Richardson,et al.  A high-resolution domestic building occupancy model for energy demand simulations , 2008 .

[14]  L. H. Hansen,et al.  Modelling the heat dynamics of a building using stochastic differential equations , 2000 .

[15]  Refrigerating.  ASHRAE handbook of fundamentals , 1967 .

[16]  T. Wilbanks,et al.  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[17]  Shengwei Wang,et al.  Simplified building model for transient thermal performance estimation using GA-based parameter identification , 2006 .

[18]  Jay Burch,et al.  Development of Standardized Domestic Hot Water Event Schedules for Residential Buildings , 2008 .

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

[20]  P. N. Paraskevopoulos,et al.  Modern Control Engineering , 2001 .

[21]  H. Madsen,et al.  Modelling the heat consumption in district heating systems using a grey-box approach , 2006 .

[22]  L. Wong,et al.  Shower water heat recovery in high-rise residential buildings of Hong Kong , 2010 .

[23]  A. Tindale.  Third-order lumped-parameter simulation method , 1993 .

[24]  Thierry Talbert,et al.  A procedure for modeling buildings and their thermal zones using co-simulation and system identification , 2014 .

[25]  Edward Henry Mathews,et al.  A first-order thermal model for building design , 1994 .

[26]  Julian Padget,et al.  'just enough' sensing to ENLITEN: a preliminary demonstration of sensing strategy for the 'energy literacy through an intelligent home energy advisor' (ENLITEN) project , 2013, e-Energy '13.

[27]  David Coley,et al.  Second order system identification in the thermal response of real buildings. Paper II: Recursive formulation for on-line building energy management and control , 1992 .

[28]  Henrik Madsen,et al.  Models for describing the thermal characteristics of building components , 2008 .

[29]  Alfonso P. Ramallo-González,et al.  Remote Facade Surveying of Windows Characteristics , 2015 .

[30]  Lino Guzzella,et al.  EKF based self-adaptive thermal model for a passive house , 2014 .

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

[32]  Ronny Östin,et al.  Further validation of a method aimed to estimate building performance parameters , 2005 .

[33]  J. M. Penman,et al.  Second order system identification in the thermal response of a working school , 1990 .

[34]  Darren Robinson,et al.  The impact of occupants' behaviour on building energy demand , 2011 .

[35]  Kody M. Powell,et al.  Reduced-order residential home modeling for model predictive control , 2014 .