Experiences of spatial microsimulation with ‘big’ and ‘little’ data: A comparison of models for (parts of) the United Kingdom and New Zealand

Experiences of spatial microsimulation with ‘big’ and ‘little’ data: A comparison of models for (parts of) the United Kingdom and New Zealand

Anderson, B. , Rushby, T., Bahaj, A. and James, P. (2019) Experiences of spatial microsimulation with ‘big’ and ‘little’ data: A comparison of models for (parts of) the United Kingdom and New Zealand. Paper presented at the 7th World Congress of the International Microsimulation Association, 19-21 June, 2019, Galway, Ireland

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Ben Anderson

June 19, 2019
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  1. Experiences of spatial microsimulation with ‘big’ and ‘little’ data: A

    comparison of models for (parts of) the United Kingdom and New Zealand Ben Anderson, Tom Rushby, 'Bakr Bahaj & Patrick James b.anderson@soton.ac.uk / ben.anderson@otago.ac.nz @dataknut
  2. @dataknut The menu § The problem • Local demand peaks

    § The solution • Local demand models § Initial results • Observation based • Time-Use based § What have we learnt? 2 UK Housing Energy Fact File Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts
  3. @dataknut What’s the problem? 3 Total NZ electricity demand per

    half hour (Winter: June) Source: Electricity Authority GW (sum) UK: A de-carbonisation story…? Source: Staffell (2018) https://doi.org/10.1016/j.enpol.2016.1 2.037
  4. @dataknut UK: Why is ‘peak’ a problem? • ‘Dirty’ energy

    Carbon problems: • Higher priced energy Cost problems: • Inefficient use of resources; • ‘Local’ overload; Infrastructure problems: 4 UK Housing Energy Fact File Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts Filling the trough Peak load
  5. @dataknut Estimating the Technical Potential for Residential Demand Response in

    New Zealand Fig. 3 illustrates electricity generation by time of day on GWh per half-hour trading period. Times of peak electricity generation are characterised by a higher electricity supply and demand at certain times and occur in early morning and evening hours in winter 2017. The maximum power on an average day in winter 2017 was 6.2 GW (equi- valent to 3.1 GWh per half-hour) and 5 GW in summer. Times of electricity peaks change by season. In summer 2017, the evening peak was much flatter and occurred slightly earlier compared to winter of the same year. This change in the electricity supply pat- tern is caused by weather conditions in December that do not necessitate appliances such as electrical heating systems to be activated, coupled with daylight saving and also longer daylight hours for summer, a lower use of lighting technologies in the early even- ing. All figures and calculations in this report consider New Zealand daylight saving. Fig. 3| Daily average half-hour electricity generation profile in summer and winter 2017 Source: Based on (Electricity Authority, 2018c) Increased demand during time intervals of high electricity demand are largely supplied by hydro electricity generation. Hydro electricity generation as depicted in Fig. 4 rep- resents a significant part of New Zealand’s electricity supply and necessitates active Page 17 of 113 NZ: Why is ‘peak’ a problem? • ‘Dirty’ energy (?) Carbon problems: • Higher priced energy Cost problems: • PV & Wind Renewables mis-match • Inefficient use of resources; • ‘Local’ (LV network) overload; Infrastructure problems: 5 Filling the trough Peak load Depends on hydro levels in Feb – April Khan et al (2018) 10.1016/j.jclepro.2018.02.309
  6. @dataknut Estimating the Technical Potential for Residential Demand Response in

    New Zealand Fig. 3 illustrates electricity generation by time of day on GWh per half-hour trading period. Times of peak electricity generation are characterised by a higher electricity supply and demand at certain times and occur in early morning and evening hours in winter 2017. The maximum power on an average day in winter 2017 was 6.2 GW (equi- valent to 3.1 GWh per half-hour) and 5 GW in summer. Times of electricity peaks change by season. In summer 2017, the evening peak was much flatter and occurred slightly earlier compared to winter of the same year. This change in the electricity supply pat- tern is caused by weather conditions in December that do not necessitate appliances such as electrical heating systems to be activated, coupled with daylight saving and also longer daylight hours for summer, a lower use of lighting technologies in the early even- ing. All figures and calculations in this report consider New Zealand daylight saving. Fig. 3| Daily average half-hour electricity generation profile in summer and winter 2017 Source: Based on (Electricity Authority, 2018c) Increased demand during time intervals of high electricity demand are largely supplied by hydro electricity generation. Hydro electricity generation as depicted in Fig. 4 rep- resents a significant part of New Zealand’s electricity supply and necessitates active Page 17 of 113 What makes up peak demand? What might be reduced? Who might respond? And what are the local network consequences? What to do? Storage •Just reducing it per se Demand Reduction •Shifting it somewhere else in time (or space and time) Demand Response 6
  7. @dataknut UK Housing Energy Fact File 65 Graph 7a: HES

    average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts The trickier problem 7 Source: maps.google.co.uk UK Housing Energy Fact File 65 Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts UK Housing Energy Fact File 65 Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts UK Housing Energy Fact File 65 Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts UK Housing Energy Fact File 65 Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts UK Housing Energy Fact File 65 Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts UK Housing Energy Fact File 65 Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts UK Housing Energy Fact File 65 Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts UK Housing Energy Fact File 65 Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts Areas with more electric heating Areas with more students Areas with more EVs? 1. Targeted interventions 2. Network investment decisions £££
  8. @dataknut The menu § The problem • Local demand peaks

    § The solution • Local demand models § Initial results • Observation based • Time-Use based § Where have we learnt? 8 UK Housing Energy Fact File Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts
  9. @dataknut Local demand models: Concept Synthetic Electricity Census Census data

    Household data (demand) 9 • UK examples: • Output Areas • ~ 100 households • Lower Layer Super Output Areas • ~ 900 households Source: http://datashine.org.uk • NZ examples: • Area Units • ~ 600 households • Meshblock areas • ~ 100 households
  10. @dataknut Synthetic Electricity Census Census data Household data (demand) Local

    demand models: Data 10 Source: http://datashine.org.uk Household attributes (area level) Bespoke kWh monitoring? Household attributes Trials: kWh demand response? Time Use Survey Data? (imputed kWh) Smart meter kWh?
  11. @dataknut Conceptually… 11 AU 2 Survey households with ‘constraint’ variables

    + kW AU 1 Iterative Proportional Fitting Deming and Stephan 1940; Fienberg 1970; Wong 1992 Birkin & Clarke, 1989; Ballas et al, 1999 Ballas et al (2005) R package: ipfp Blocker (2016) Weights Census ‘constraint’ tables
  12. @dataknut § UK: Southampton – LSOA level (1000 households) –

    Data: • Time Use -> imputed power • Observed kWh § NZ: Taranaki – Area Unit level (600 households) – Data: • Time Use -> imputed power • Observed kWh Local demand models: Case studies 12
  13. @dataknut UK: SAVE model v1.0 13 •http://doi.org/10.5255/UKDA-SN-4504-1 •Sample of ~

    6,000 households •~ 600 in South East England UK Time-Use Data •~ 1000 households per LSOA •For Southampton UK LSOA level Census tables •IPF re-weighting of survey cases (Ballas et al, 2005) Spatial Microsimulation Method
  14. @dataknut Time Use Surveys: When do people do what? %

    of respondents reporting a selection of energy-demanding activities Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) 14 Winter (November 2000 - February 2001) 0% 10% 20% 30% 40% 50% 60% 0:00 1:30 3:00 4:30 6:00 7:30 9:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 % respondents Audio TV Reading Computer Ironing Laundry Cleaning Dish washing Cooking Wash/dress self Aged 25-64 who are in work 0% 10% 20% 30% 40% 50% 60% 0:00 1:30 3:00 4:30 6:00 7:30 9:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 % respondents Audio TV Reading Computer Ironing Laundry Cleaning Dish washing Cooking Wash/dress self Aged 65+ UK Housing Energy Fact File Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Households with especially high or low consumption do not have particular behaviours that make them easy to identify. Instead they tend to have a cluster of very ordinary behaviours that happen to culminate in high or low gas use. There are, it seems, many different ways to be a high or low gas user. The behaviours in question can be clustered under three broad headings: • physical properties of the home – the particular physical environment in which people live • temperature management – how people manage the temperature in their homes and their awareness of the energy implications of their actions • people in the home – who is in the home, and when, and what they are doing. 60 Physical properties of the home Many UK homes have been modified by extensions, conservatories, conversions and/or open plan spaces. These modifications have the potential to affect the thermal properties of a home. But, typically, these have not been included in existing quantitative modelling of domestic energy consumption. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts
  15. @dataknut Imputing Demand 15 § Imputation at individual level –

    For each primary & secondary activity in each 10 minute time slot § Then aggregated to household level – Assume 100W for lighting if at home – Max: Cooking, Dish Washing, Laundry – Sum: everything else § Problems: – Wash/dress might just be ‘dress’ – Hot water might be gas heated – TVs might be watched ‘together’ – Not all food preparation = cooking and might be gas – People have MANY more lights on! – Several appliances may be ‘on’ but not recorded (Durand- Daubin, 2013) – No heating § => a very simplistic ‘all electricity non-heat’ model! J Widén et al., 2009 doi:10.1016/j.enbuild.2009.02.013 Assumes ‘shared’ use Assumes ‘separate’ use
  16. @dataknut Conceptually… 16 LSOA 2) Survey households with ‘constraint’ variables

    + modelled kWh LSOA 1 Iterative Proportional Fitting Deming and Stephan 1940; Fienberg 1970; Wong 1992 Birkin & Clarke, 1989; Ballas et al, 1999 Ballas et al (2005) Weights Census ‘constraint’ tables • Winter weekdays • SE England • All constraint data • => n = 162! But:
  17. @dataknut Results (Model 1.0) 17 Source: Author’s calculations using UK

    Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted), UKL Census 2001 small area tables and Model 1 power assumptions 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0:00 1:30 3:00 4:30 6:00 7:30 9:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 MW 3+ earners 2 earners 1 earner 0 earners “all electricity, non-heat’ model’ Mean power (all LSOAs) Total power (1 LSOA)
  18. @dataknut UK: SAVE model v2.0 18 • Survey sample of

    ~ 4,000 households SAVE kWh data • ~ 1000 households per LSOA • For Southampton UK LSOA level Census tables • IPF re-weighting of survey cases (Ballas et al, 2005) Spatial Microsimulation Method http://www.energy.soton.ac.uk/save-data-sources/
  19. @dataknut Conceptually… 19 LSOA 2) Survey households with ‘constraint’ variables

    + observed kWh LSOA 1 Iterative Proportional Fitting Deming and Stephan 1940; Fienberg 1970; Wong 1992 Birkin & Clarke, 1989; Ballas et al, 1999 Ballas et al (2005) Weights Census ‘constraint’ tables
  20. @dataknut Example results: Baseline 20 Source: http://datashine.org.uk • Mean kWh

    per halfhour: winter weekdays (January 2017) • 148 LSOAs
  21. @dataknut Comparing models 21 Sim: Observed kWh Sim: Modelled kWh

    (Time Use) • Mean kWh per halfhour: winter weekdays, 148 LSOAs
  22. @dataknut Comparing models 22 Pearson Spearman 1. Morning Peak 0.547

    0.529 2. Day time 0.144 0.180 3. Evening Peak 0.473 0.524 4. All other times 0.889 0.831 “all electricity, non-heat’ model’
  23. @dataknut NZ: GREENGrid area unit model (v0.01a) 23 • Sample

    of ~ 30 monitored households • Hawke’s Bay & Taranaki NZ GREENGrid Data • ~ 600 households per AU • For Hawke’s Bay & Taranaki NZ Area Unit level Census data • IPF re-weighting of survey cases (Ballas et al, 2005) Spatial Microsimulation Method Estimating the Technical Potential for Residential Demand Response in New Zealand Fig. 3 illustrates electricity generation by time of day on GWh per half-hour trading period. Times of peak electricity generation are characterised by a higher electricity supply and demand at certain times and occur in early morning and evening hours in winter 2017. The maximum power on an average day in winter 2017 was 6.2 GW (equi- valent to 3.1 GWh per half-hour) and 5 GW in summer. Times of electricity peaks change by season. In summer 2017, the evening peak was much flatter and occurred slightly earlier compared to winter of the same year. This change in the electricity supply pat- tern is caused by weather conditions in December that do not necessitate appliances such as electrical heating systems to be activated, coupled with daylight saving and also longer daylight hours for summer, a lower use of lighting technologies in the early even- ing. All figures and calculations in this report consider New Zealand daylight saving. Fig. 3| Daily average half-hour electricity generation profile in summer and winter 2017 Source: Based on (Electricity Authority, 2018c) Increased demand during time intervals of high electricity demand are largely supplied by hydro electricity generation. Hydro electricity generation as depicted in Fig. 4 rep- resents a significant part of New Zealand’s electricity supply and necessitates active Page 17 of 113
  24. @dataknut Data: NZ GREENGrid 24 Get the data: https://dx.doi.org/10.5255/UKDA-SN-853334 •

    Circuits measured: • Hot water • Lighting • Heat pumps • Kitchen • Bedrooms • etc • Data: • Household survey • Mean power (W) per minute
  25. @dataknut Data: NZ GREENGrid – Lighting 25 Get the data:

    https://dx.doi.org/10.5255/UKDA-SN-853334 We want to estimate these for each unit area! VERY small n…
  26. @dataknut § Area Unit level • Hawke’s Bay • Taranaki

    § Variables used: • N rooms • N people • Presence children § Potential future variables: • Main heating fuel • Dwelling type • Income band • Age of adults/children Data: NZ Census 26 Matches GREENGrid sample ~ 90,000 households Some are not in GREENGrid data Because they correlate with demand
  27. @dataknut Remember how this works… 27 AU 2 Survey households

    with ‘constraint’ variables + kW AU 1 Iterative Proportional Fitting Deming and Stephan 1940; Fienberg 1970; Wong 1992 Birkin & Clarke, 1989; Ballas et al, 1999 Ballas et al (2005) R package: ipfp Blocker (2016) Weights Census ‘constraint’ tables
  28. @dataknut Remember how this works… 28 AU 2 Survey households

    with ‘constraint’ variables + kW AU 1 Iterative Proportional Fitting Deming and Stephan 1940; Fienberg 1970; Wong 1992 Birkin & Clarke, 1989; Ballas et al, 1999 Ballas et al (2005) R package: ipfp Blocker (2016) Weights Census ‘constraint’ tables GREEN Grid sample
  29. @dataknut The consequence… 29 Source: Author’s calculations using NZ GREENGrid

    data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx] We’re replicating a lot of households Each dot = 1 unit area so weird stuff can happen…
  30. @dataknut But even so… 30 Source: Author’s calculations using NZ

    GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx] Simulated household counts work OK Each dot = 1 unit area
  31. @dataknut But even so… 31 Source: Author’s calculations using NZ

    GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx] Simulated household counts in categories used as constraints work OK Each dot = 1 unit area
  32. @dataknut And quite surprisingly… 32 Source: Author’s calculations using NZ

    GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx] Simulated household counts in categories NOT used as constraints work quite well (sometimes) Each dot = 1 unit area
  33. @dataknut Example: Lighting (spatial, seasonal) 33 Source: Author’s calculations using

    NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://archive.stats.govt.nz/Census/2013-census/data- tables/meshblock-dataset.aspx] Where might LEDs reduce demand? As modelled Each line = 1 area unit Highest lighting Lowest lighting
  34. @dataknut The menu § The problem • Local demand peaks

    § The solution • Local demand models § Initial results • Observation based • Time-Use based § What have we learnt? 34 period. Times of peak electricity generation are characterised by a higher electricity supply and demand at certain times and occur in early morning and evening hours in winter 2017. The maximum power on an average day in winter 2017 was 6.2 GW (equi- valent to 3.1 GWh per half-hour) and 5 GW in summer. Times of electricity peaks change by season. In summer 2017, the evening peak was much flatter and occurred slightly earlier compared to winter of the same year. This change in the electricity supply pat- tern is caused by weather conditions in December that do not necessitate appliances such as electrical heating systems to be activated, coupled with daylight saving and also longer daylight hours for summer, a lower use of lighting technologies in the early even- ing. All figures and calculations in this report consider New Zealand daylight saving. Fig. 3| Daily average half-hour electricity generation profile in summer and winter 2017 Source: Based on (Electricity Authority, 2018c) Increased demand during time intervals of high electricity demand are largely supplied by hydro electricity generation. Hydro electricity generation as depicted in Fig. 4 rep- resents a significant part of New Zealand’s electricity supply and necessitates active Page 17 of 113
  35. @dataknut § We have shown: – The method works… §

    UK: – Time-Use model allows activity scenarios • But is mis-specified? • Some models are useful… – ‘Observed’ model inflates ‘outliers’ – Both offer spurious precision § NZ: – GREENGrid data is insufficient – The results are probably garbage § We need to: – Gather better kW/h data – Represent uncertainty – Validate, validate, validate What have we learnt? 35 N * 100 Representative sample
  36. @dataknut Questions? § ben.anderson@otago.ac.nz § b.anderson@soton.ac.uk § @dataknut § www.energy.soton.ac.uk/tag/spatialec

    – 3 year EU Global Fellowship – 2017-2020 § “This research was supported by a Marie Sklodowska-Curie Individual Global Fellowship within the H2020 European Framework Programme (2014 -2020) under grant agreement no. 700386.” 36 pixabay.com