Feedback and Feedforward Mechanisms for Generating Occupant Datasets for UK School Stock Simulation Modelling

National construction and energy datasets coupled with batch building performance simulation techniques have made feasible the construction of a stock building simulation model of over 16,000 schools. Although this should provide insights for targeted energy efficiency measures, discrepancies between measured and calculated performance limit predictive powers. A case study of building simulation models of three London schools built using the stock modelling process is presented. Discrepancies in calculated performance have been demonstrated when standardised variables are assumed for schedules, setpoints and equipment over the entire stock. Feedback mechanisms are proposed as a means of recruiting school building users to facilitate future data provision.

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

[2]  Alistair S. Grandison,et al.  Electricity use in the commercial kitchen , 2013 .

[3]  Christoph F. Reinhart,et al.  Urban building energy modeling – A review of a nascent field , 2015 .

[4]  Philip Steadman,et al.  3DStock: A new kind of three-dimensional model of the building stock of England and Wales, for use in energy analysis , 2017 .

[5]  S. M. Hong,et al.  Benchmarking the energy performance of the UK non-domestic stock: a schools case study , 2015 .

[6]  Christine Demanuele,et al.  Bridging the gap between predicted and actual energy performance in schools , 2010 .

[7]  Christoph F. Reinhart,et al.  Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets , 2016 .

[8]  Manuel Gameiro da Silva,et al.  Energy consumption in schools – A review paper , 2014 .

[9]  Simon Tucker,et al.  Placing user needs at the center of building performance simulation (BPS) tool development: using 'designer personas' to assess existing BPS tools , 2016 .

[10]  Amrita Dasgupta,et al.  Operational versus designed performance of low carbon schools in England: Bridging a credibility gap , 2011, HVAC&R Research.

[11]  Dejan Mumovic,et al.  A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .

[12]  Alan Meier,et al.  Rating the energy performance of buildings , 2004 .

[13]  Y. Shimoda,et al.  Residential end-use energy simulation at city scale , 2004 .

[14]  Philip Haves,et al.  An epidemiological approach to simulation-based analysis of large building stocks , 2015 .

[15]  Dejan Mumovic,et al.  Crowd-sourced building intelligence: the potential to go beyond existing benchmarks for effective insight, feedback and targeting , 2015 .

[16]  Dejan Mumovic,et al.  Comprehensiveness and usability of tools for assessment of energy saving measures in schools , 2013 .

[17]  Adrian Leaman,et al.  Assessing building performance in use 3: energy performance of the Probe buildings , 2001 .

[18]  Qinghua Zhu,et al.  Evaluation on crowdsourcing research: Current status and future direction , 2012, Information Systems Frontiers.

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

[20]  Christoph F. Reinhart,et al.  The Use of Multi-detail Building Archetypes in Urban Energy Modelling☆ , 2017 .

[21]  Simone Ferrari,et al.  A supporting method for defining energy strategies in the building sector at urban scale , 2013 .

[22]  Koen Steemers,et al.  Using Display Energy Certificates to quantify public sector office energy consumption , 2015 .

[23]  Akshay Gupta,et al.  Life cycle cost and carbon footprint of energy efficient refurbishments to 20th century UK school buildings , 2014 .

[24]  Dejan Mumovic,et al.  A comparative study of benchmarking approaches for non-domestic buildings : Part 1 – Top-down approach , 2014 .

[25]  Ardeshir Mahdavi,et al.  IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings , 2017 .

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

[27]  Dejan Mumovic,et al.  Determinants of energy use in UK higher education buildings using statistical and artificial neural network methods , 2012 .

[28]  Dejan Mumovic,et al.  Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings , 2014 .

[29]  C. Vlek,et al.  The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. , 2007 .

[30]  M. Ha-Duong,et al.  Climate change 2014 - Mitigation of climate change , 2015 .