An investigation of the feedback and feedforward mechanisms required for Crowdsourcing occupant datasets for a UK school stock model Duncan Grassie oC? ppm? kWh?

Today’s presentation Modelling school buildings Why? – Within the context of energy demand reduction targets How? - Stock modelling and preceding methods Stock modelling case study Creation and potential auto-generation of datasets Issues with stock modelling process Sensitivities run Crowdsourcing How does it work? – Data sharing for shared knowledge Feedback and feedforward mechanisms Future Work – a framework for creating and testing a Crowdsourcing platform

Modelling school buildings Why? – Within the context of energy demand reduction targets How? - Stock modelling and preceding methods Stock modelling case study Creation and potential auto-generation of datasets Issues with stock modelling process Sensitivities run Crowdsourcing How does it work? – Data sharing for shared knowledge Feedback and feedforward mechanisms Future Work – a framework for creating and testing a Crowdsourcing platform

School buildings in context of energy demand Non-domestic buildings Any building that’s not residential! ~1.8 million in UK Carbon emissions for heating, lighting, power account for 18% of total UK Schools Comprise around 47,500 premises in UK 6% of electrical, 13% of non-electrical non-domestic consumption Discrepancy between: a) Operation measurements and design predictions b) Similar buildings with different energy consumptions

Modelling techniques Top-down Bottom-up Statistical analyses of headline figures Using national level datasets ie Statistical methods, artificial neural networks Model informs on a national level Does not provide causal detail Bottom-up Aggregating end-use components Using individual post occupancy studies ie Building physics Performance attributed to end-uses Intensive datasets/gathering

Stock models – National scale building simulation SimStock – auto-generation of energy simulation models National level Lidar and taxation databases Requires input on fabric, energy services offered Ref: Coffey, B. et al. (2015) ‘An epidemiological approach to simulation-based analysis of large building stocks

Modelling school buildings Why? – Within the context of energy demand reduction targets How? - Stock modelling and preceding methods Stock modelling case study Creation and potential auto-generation of datasets Issues with stock modelling process Sensitivities run Crowdsourcing How does it work? – Data sharing for shared knowledge Feedback and feedforward mechanisms Future Work – a framework for creating and testing a Crowdsourcing platform

Case study school campuses Typically a mixture of Victorian, 1960s and post-2000 builds

Data Sources for occupant data Setpoints and equipment specs Schedules

Use of Archetypes for fabric data 1950s map 1960s map Ref: Digimaps – online historical OS maps

Problems with modelling school buildings Modern archetypes How many floors?

Results and sensitivities

Sensitivity to heating controls

Modelling school buildings Why? – Within the context of energy demand reduction targets How? - Stock modelling and preceding methods Stock modelling case study Creation and potential auto-generation of datasets Issues with stock modelling process Sensitivities run Crowdsourcing How does it work? – Data sharing for shared knowledge Feedback and feedforward mechanisms Future Work – a framework for creating and testing a Crowdsourcing platform

Crowdsourcing – data gathering on grand scale The Problem: Occupant schedules, equipment, setpoints required for effective feed forward Head teachers, janitors know this data but what motivation for passing it on? Crowdsourcing Recruiting volunteers on a mass scale by sharing aggregated knowledge / reward Monetary example – Crowdfunding – Brewdog Knowledge example – Extra terrestrials! In schools? Compared to peers/standards Ref: Carbon Trust Cost of fixing?

Feedback and feedforward mechanisms 1 2 3 Future research project: Design feedback mechanism to occupants based on workshops with school governors/head teachers and buildings engineers on motivations/knowledge required Design structure of aggregated data to be fed forward based on metrics / sub-sectors of stock specified by Measure if individual school user data gathered from a crowdsourcing platform improves the accuracy of fedforward individual detail on energy performance

Future Work

Summary The important role of school buildings in UK energy reduction As a proportion of UK non-domestic buildings Access to national datasets (DECs) and energy efficiency measures Stock modelling can provide: Causal data (bottom-up) on entire sub-sector performance (top-down) Case study demonstrates: Stock models requires individualised data on school schedules and setpoints to feedforward underperforming schools to policy makers Datasets of occupant data over entire population Require new methods of motivating / educating volunteers (feedback)

Acknowledgements This work was supported by the EPSRC Centre for Doctoral Training in Energy Demand (LoLo), under Grants EP/L01517X/1 and EP/H009612/1. Thanks to Dr. Sung-min Hong and Steve Evans for providing the geometry for the three case study schools Any questions?