Ben Anderson – University of Southampton (@dataknut)
Jacopo Torriti – University of Reading
Richard Hanna – University of Reading
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014.
A presentation on the value of solar calculation being investigated by the Minnesota Department of Commerce as a part of the state's new solar energy law.
PRACTICE HUNTING: Time Use Surveys for a quantification of practices distribu...Ben Anderson
Mathieu Durand-Daubin (EDF R&D-ECLEER)
Ben Anderson (Southampton University)
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014
CMP E1FW-32
CMP Cable Glands For Hazardous Areas
Zone 1, Zone 2, Zone 21 & Zone 22 - ATEX & IECEx
CMP E1FW-32 Cable Glands - Flameproof ATEX Cable Gland - 23.7-33.9mm
CMP Type E1FW Tri-Star Triple Certified Flameproof (Type ‘d’), Increased Safety (Type ‘e’) and Restricted Breathing (Type ‘nR’) cable gland for use in Zone 1, Zone 2, Zone 21 and Zone 22 Hazardous Areas with Single Wire Armour (SWA) cable.
CMP E1FW Technical Data
Temperature Rating : -60°C to +130°C
Cable Type : Single Wire Armour (SWA) & Aluminium Wire Armour (AWA)
Cable Sealing Area : Cable Inner Bedding & Outer Cable Sheath
CMP E1FW Hazardous Area Approvals
ATEX: SIRA06ATEX1097X, SIRA07ATEX4326X
ATEX Ex II 2/3 GD, Ex d IIC, Ex e II, Ex nR II, Ex tD A21 IP66
IECEx: IECEx SIR 06.0043X
A presentation on the value of solar calculation being investigated by the Minnesota Department of Commerce as a part of the state's new solar energy law.
PRACTICE HUNTING: Time Use Surveys for a quantification of practices distribu...Ben Anderson
Mathieu Durand-Daubin (EDF R&D-ECLEER)
Ben Anderson (Southampton University)
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014
CMP E1FW-32
CMP Cable Glands For Hazardous Areas
Zone 1, Zone 2, Zone 21 & Zone 22 - ATEX & IECEx
CMP E1FW-32 Cable Glands - Flameproof ATEX Cable Gland - 23.7-33.9mm
CMP Type E1FW Tri-Star Triple Certified Flameproof (Type ‘d’), Increased Safety (Type ‘e’) and Restricted Breathing (Type ‘nR’) cable gland for use in Zone 1, Zone 2, Zone 21 and Zone 22 Hazardous Areas with Single Wire Armour (SWA) cable.
CMP E1FW Technical Data
Temperature Rating : -60°C to +130°C
Cable Type : Single Wire Armour (SWA) & Aluminium Wire Armour (AWA)
Cable Sealing Area : Cable Inner Bedding & Outer Cable Sheath
CMP E1FW Hazardous Area Approvals
ATEX: SIRA06ATEX1097X, SIRA07ATEX4326X
ATEX Ex II 2/3 GD, Ex d IIC, Ex e II, Ex nR II, Ex tD A21 IP66
IECEx: IECEx SIR 06.0043X
Practices by proxy: Climate, Consumption and WaterBen Anderson
Anderson, B., Browne, A., and Medd, W., (2012) Practices by proxy: climate, consumption and water. Paper presented at Living Costs and Food Survey user meeting, Tuesday 20 March 2012 at the Royal Statistical Society, London
Hunting for (energy) demanding practices using big & medium sized dataBen Anderson
Presentation given at 'Reshaping the Domestic Nexus: Analytical Insights and Methodologies', Manchester 23/11/2015 (see https://nexusathome.wordpress.com/2015/12/02/workshop-2-reshaping-the-domestic-nexus-manchester/)
La Asociación Española de Análisis (AEV) anuncia la incorporación de Lee Mays como secretario de la patronal que representa a las 23 sociedades que llevan a acabo en torno al 90% de las tasaciones del sector. Su obetivo como representante es consolidar el valor añadido de los miembros de la Asociación en el marco de actuación español, europeo e internacional.
The Time and Timing of UK Domestic Energy DEMANDBen Anderson
Anderson, B. (2014) The Time and Timing of UK Domestic Energy DEMAND. Keynote paper presented at the 2014 Otago Energy Research Centre Symposium, University of Otago, Dunedin, New Zealand, 28/11/2014.
Ipsos MORI research on public attitudes to the UK’s energy challengesIpsos UK
The British public are now far more concerned by energy security than climate change compared with people around the world. There is recognition we need a diverse mix of energy sources to meet needs, including support for nuclear. However, consumers themselves are still wedded to gas, and have limited awareness of alternative options. Ben Page gave this presentation to Madano Partnership's breakfast briefing on the UK’s evolving energy policy: Opportunties and Challenges on 25 April 2013.
Alternative Energy Facts - Between a ROC and a Green PlaceBrian Catt
An engineer, physicist and businessman's independently verifiable fact based take on the big green energy fraud, as run by government for the profit of banks and generators to make every on of its supposed benefits expensively worse.
Small Area Estimation as a tool for thinking about temporal and spatial varia...Ben Anderson
Anderson, B (2014) Small Area Estimation as a tool for thinking about temporal and spatial variation in energy demand. Paper presented at AURIN/NATSEM Microsimulation Workshop, University of Melbourne, Thursday 4th December 2014
Deep Retrofit: Energy Cultures and the Importance of Energy Practices Within ...SustainableEnergyAut
Dr Eimear Heaslip, NUIG: Deep Retrofit: Energy Cultures and the Importance of Energy Practices Within Households, SEAI Deep Retrofit conference, June 21st 2017
Slide presentation from ISO New England CEO Gordon van Welie on the role of natural gas and pipelines for that gas and their importance to the electricity market in New England.
Muireann Lynch delivered this presentation at a joint ESRI-UCD conference tilted 'Energy research to enable climate change mitigation' on 17 September.
Photos from the conference are available to view on the ESRI website here: https://www.esri.ie/events/esri-ucd-conference-energy-research-to-enable-climate-change-mitigation
Practices by proxy: Climate, Consumption and WaterBen Anderson
Anderson, B., Browne, A., and Medd, W., (2012) Practices by proxy: climate, consumption and water. Paper presented at Living Costs and Food Survey user meeting, Tuesday 20 March 2012 at the Royal Statistical Society, London
Hunting for (energy) demanding practices using big & medium sized dataBen Anderson
Presentation given at 'Reshaping the Domestic Nexus: Analytical Insights and Methodologies', Manchester 23/11/2015 (see https://nexusathome.wordpress.com/2015/12/02/workshop-2-reshaping-the-domestic-nexus-manchester/)
La Asociación Española de Análisis (AEV) anuncia la incorporación de Lee Mays como secretario de la patronal que representa a las 23 sociedades que llevan a acabo en torno al 90% de las tasaciones del sector. Su obetivo como representante es consolidar el valor añadido de los miembros de la Asociación en el marco de actuación español, europeo e internacional.
The Time and Timing of UK Domestic Energy DEMANDBen Anderson
Anderson, B. (2014) The Time and Timing of UK Domestic Energy DEMAND. Keynote paper presented at the 2014 Otago Energy Research Centre Symposium, University of Otago, Dunedin, New Zealand, 28/11/2014.
Ipsos MORI research on public attitudes to the UK’s energy challengesIpsos UK
The British public are now far more concerned by energy security than climate change compared with people around the world. There is recognition we need a diverse mix of energy sources to meet needs, including support for nuclear. However, consumers themselves are still wedded to gas, and have limited awareness of alternative options. Ben Page gave this presentation to Madano Partnership's breakfast briefing on the UK’s evolving energy policy: Opportunties and Challenges on 25 April 2013.
Alternative Energy Facts - Between a ROC and a Green PlaceBrian Catt
An engineer, physicist and businessman's independently verifiable fact based take on the big green energy fraud, as run by government for the profit of banks and generators to make every on of its supposed benefits expensively worse.
Small Area Estimation as a tool for thinking about temporal and spatial varia...Ben Anderson
Anderson, B (2014) Small Area Estimation as a tool for thinking about temporal and spatial variation in energy demand. Paper presented at AURIN/NATSEM Microsimulation Workshop, University of Melbourne, Thursday 4th December 2014
Deep Retrofit: Energy Cultures and the Importance of Energy Practices Within ...SustainableEnergyAut
Dr Eimear Heaslip, NUIG: Deep Retrofit: Energy Cultures and the Importance of Energy Practices Within Households, SEAI Deep Retrofit conference, June 21st 2017
Slide presentation from ISO New England CEO Gordon van Welie on the role of natural gas and pipelines for that gas and their importance to the electricity market in New England.
Muireann Lynch delivered this presentation at a joint ESRI-UCD conference tilted 'Energy research to enable climate change mitigation' on 17 September.
Photos from the conference are available to view on the ESRI website here: https://www.esri.ie/events/esri-ucd-conference-energy-research-to-enable-climate-change-mitigation
The Distribution of Domestic Energy-Tech in Great Britain: 2008 – 2011Ben Anderson
Paper presented at 'What makes us act green?', Research & Policy Seminar, 17th December 2013, BIS Conference Centre, London. Uses @usociety survey data to analyse household uptake of solar PV and solar thermal in the UK 2008-2011
By Robert Sansom, Imperial College
Presented at 'UK Energy System in Transition: Technology, Infrastructure and Investment'; an event organised by the UK Energy Research Centre, ClimateXChange and the Edinburgh Centre for Carbon Innovation, on Tuesday 1 April 2014, 14.00-17.00, in Edinburgh, United Kingdom.
Electricity consumption and household characteristics: Implications for censu...Ben Anderson
Presentation given at MRS Workshop "Can Big Data replace the Census? What does Big Data give us now?" , March 7, 2016, MRS, London (https://www.mrs.org.uk/event/conferences/can_big_data_replace_the_census/course/4088/id/10035)
Census2022: Extracting value from domestic consumption data in a postcensus eraBen Anderson
Andy Newing a.newing@soton.ac.uk
Ben Anderson b.anderson@soton.ac.uk (@dataknut)
10 minute 'lightning' paper presented at BEHAVE 2014, Said Business School, Oxford, 4th September 2014.
Tracking Social Practices with Big(ish) dataBen Anderson
Paper presented at 'Methodology' session of PRACTICES, THE BUILT ENVIRONMENT AND SUSTAINABILITY EARLY CAREER RESEARCHER NETWORK Workshop,
26-27 June 2014, Cambridge
Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy product...Ben Anderson
"Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy production technologies?"
Paper presented at "What Makes Us Act Green?", June 25 2014, London
Small Area Estimation as a tool for thinking about spatial variation in energ...Ben Anderson
Paper presented at "Spatial Variation in Energy Use, Attitudes and Behaviours: Implications for Smart Grids and Energy Demand", Policy Studies Institute, Friday, 7 February 2014, London, United Kingdom
Patterns of Water: Thinking about diversity, demand and consumptionBen Anderson
Presentation on @ESRC funded water practices/demand research to @scotgov @GreenerScotland with @dralibrowne 4th December 2013, Scottish Government, Victoria Quay, Leith
An overview of the new ESRC Transformative research project given by Andy Newing to MRS Census and Geodemographic Group (CGG) hosted by GFK NOP, 19th November 2013 at Ludgate House, London
Practices by Proxy: Climate, Consumption and Water (and troubles with data)Ben Anderson
Please cite as:
Anderson, B (2012) Practices By Proxy: Climate, Consumption and Water, Paper presented at “Can Climate Change Policies Be Fair?”, Royal Statistical Society, July 5th 2012, London
Net gains – the returns to education of home internet useBen Anderson
Paper presented at the iCS-OII 2011 symposium, "A Decade in Internet Time" - an academic conference organised to critically assess the last decade of social research on the Internet and identify directions for research over the next.
Wednesday 21st September and Friday 23rd September, 2011
http://microsites.oii.ox.ac.uk/ics2011/content/programme
Estimating the small area effects of austerity measures in the UKBen Anderson
Dr Ben Anderson (benander@essex.ac.uk)
Dr Paola De Agostini (pdeago@essex.ac.uk) Tony Lawson (tlawso@essex.ac.uk)
Governments across Europe are starting to implement a range of cost-cutting and income- generating programmes in order to re-balance their fiscal budgets following substantial investments in stabilising domestic financial institutions in 2008 and 2009. One method of doing this has been to increase tax rates such as the increase in VAT in the UK from 17.5% to 20% from January 1st 2011. In this paper we explore the different spatial impact of this VAT rise on household expenditure on public and private transport and communication technology from 2006 to 2016. We do this by combining three elements: an agent-based dynamic population microsimulation model that produces projected snapshots of the UK population in 2006, 2011 and 2016; an expenditure system model based on the familiar Quadratic Almost Ideal Demand System approach; and synthetic small area census tables produced by projecting historical UK census data. Taken together these elements provide a toolkit for assessing the potential spatial impact of rising taxes or prices (or both) and we use them to compare small area projections of household expenditure under two scenarios. The first is a 'no intervention' scenario where prices and income align to UK government inflation forecasts and the second is a one-off non-reversed 2.5% increase in VAT on goods and services rated at 17.5% on 1st January 2011. We present results for different areas (rural vs urban/deprived vs affluent) and for different income groups within them and discuss the potential implications for the telecommunications industry and for the usage of public and private transport.
Paper presented at the 3rd General Conference of the International Microsimulation Association, 8-10 June 2011, Stockholm (http://www.scb.se/IMA2011)
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
The Rhythms and Components of ‘Peak Energy’ Demand
1. The Rhythms and Components
of ‘Peak Energy’ Demand
Ben Anderson – University of Southampton (@dataknut)
Jacopo Torriti – University of Reading
Richard Hanna – University of Reading
www.demand.ac.uk
BEHAVE Conference 2014
3rd September 2014
2. What’s the problem?
• Domestic demand for electricity is
particularly ‘peaky’…
• Infrastructure problems
• Network ‘import’ overload on
weekday evenings;
• Network ‘export’ overload at mid-day
on weekdays due to under-used
PV generation;
• Inefficient use of resources (night-time
trough)
• Carbon problems:
• Peak load can demand ‘dirty’
generation
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 nhouseholds’ e ergy
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
Gas use varies enormously from
household to household, and the
variation has more to do with
behaviour than how dwellings are
built.
800
700
600
500
400
300
200
100
0
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
• Cost problems:
• Peak generation is higher
priced energy
3. What to do?
Two inter-linked approaches to dealing
with ‘Peak’:
• Demand Reduction
• Just reducing it per se
• Demand Response
• Shifting it somewhere else in
time (or space and time)
This raises the crucial questions:
• What do people do during peaks?
• How has this evolved?
• What can shift and where can it
shift to?
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 nhouseholds’ e ergy
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
Gas use varies enormously from
household to household, and the
variation has more to do with
behaviour than how dwellings are
built.
800
700
600
500
400
300
200
100
0
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
4. But there’s another problem… UK Housing Energy Fact File
• This is an appliance
level view
• It tells us very little
about what people
do in peaks (and
troughs)
• And nothing about
change over time
• But time-use diary
data might…
Graph 7a: HES average 24-hour electricity use profile for owner-occupied
homes, England 2010-11
Gas consumption
Gas use varies enormously from
800
700
600
500
400
300
200
100
0
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
5. So what constitutes peak?
ONS 2005 Time Use Survey Data (UK, weekdays) % of persons reporting
6. So what constitutes peak?
ONS 2005 Time Use Survey Data (UK, weekdays) % of persons reporting
7. The ‘average day’ is not that helpful
Monday Friday
ONS 2005 Time Use Survey Data (UK) % people reporting category – half hour summaries
8. Whose ‘peak’: gendered practices
Men Women
ONS 2005 Time Use Survey Data (UK, all days) % people reporting category – half hour summaries
10. Whose ‘peak’: age/cohort variation
16-64 : weekdays 65+ : weekdays
ONS 2005 Time Use Survey Data (UK, week days) % people reporting category – half hour summaries
11. Synchronisation and peaks…
• Occurs when practices are to some extent happening together over the
same time periods, across multiple spaces.
• Synchronisation matters because it generates peaks in energy demand
and implies potential to manage social practices.
Synchronisation high Synchronisation low
Many people doing the same
energy-intensive activity at
the same time e.g. cooking
Many people doing different
energy-intensive activities at
the same time
Many people doing the same
lower energy activity at the
same time e.g. sleeping
Many people doing different
lower energy activities at the
same time
Energy demand
higher
Energy demand
lower
12. Synchronisation index: relative synchronisation
of men and women
• ‘Trajectory’ 2011 time use
data (n = 500)
• Based on Shannon entropy
index
• Indicator of people ‘doing
the same thing’
• Chapela (2013)
dx.doi.org/10.13085/eIJTU
R.10.1.9-37
13. Summary
• Energy ‘demands’ are emergent from co-evolving
infrastructures and what people do (social practices)
• There are a range of factors that affect how these demands emerge
and how they are synchronised to produce ‘peaks’
• We need more than ‘average days’ and ‘appliance profiles’ to
understand these quantitatively
• Non-energy energy policy
• E.g. labour market participation influences the time & timing of
demand
• Next steps:
• Which kinds of people are engaged in similar social practices?
• Which sequences of practices are implicated in peak demand?
14. Thank you
Ben Anderson b.anderson@soton.ac.uk
Jacopo Torriti j.torriti@reading.ac.uk
Richard Hanna r.f.hanna@reading.ac.uk
www.demand.ac.uk