Bale, Catherine

Foxon, Timothy

Gale, William

McCullen, N J

Rucklidge, Alastair

OPUS

        Citation for published version:McCullen, NJ, Bale, C, Foxon, T, Rucklidge, A & Gale, W 2013, 'Modelling the uptake of domestic energytechnologies via local networks and integrating real-world data' FutureBuild 2013, Bath, UK United Kingdom,4/09/13 - 6/12/13, .Publication date:2013Document VersionPublisher's PDF, also known as Version of recordLink to publicationPublisher RightsCC BY-NC-NDUniversity of BathGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Download date: 13. May. 2019Modelling the uptake of domestic energy technologies vialocal networks and integrating real-world dataNick McCullen‡, Catherine Bale?1, Timothy Foxon?2, Alastair Rucklidge?3 and William Gale?1‡EDEn research unit, University of Bath, UK. —— ?(1Energy, 3Earth & Environment, 2Maths), University of Leeds , UK.email: ‡n.mccullen@physics.org —— url: http://people.bath.ac.uk/nm268/Introduction•Companies and policy-makers are in a position to influence residents and businesses to adopt domestic energymeasures and reduce energy demand;•Tools are needed to support decision-makers in achieving their energy and climate change targets [1];•Quantification and integration of real-world data into mathematical and simulation models is needed for themto be reliable and usable as tools by strategic planners.Objectives1. To develop tools for modelling diffusion of energy technologies via net-works of households, in order to aid decision-making in local authorities;2. To use real-world empirical data to guide the models towards more ac-curately representing heterogeneous populations and studying the effectthis has on the model results.Modelling Uptake of InnovationHouseholder decisions to adopt a particular innovation arebased on a combination of factors:Total Utility to household[3]:ui = αipi + βisi + γim,pi, si,m: personal, peer-group and societal influence,αi, βi, γi: relative weightings given to each factor.1. Households are represented as nodes on a network.2. People communicate via peer-to-peer interactions.3. Interactions represented by links between nodes.Figure 1: Network Model4. Each node i has adoption state variable xi = 0, 1.5. Dynamical equations determine individual uptake.Adoption Rule:x′i =1 if xi = 1,1 if xi = 0 and ui > θi,0 otherwise.• θi: threshold (barriers, costs etc.),Modelling Social NetworksFigure 2: Links established between nodes eitherindividually or via groups[4] — social, workplaces,etc. Here there are N = 11 nodes, with node iconnected to G = 2 from a total of W groupsoverall. There are L = 3 links established pergroup and individually.Integrating Real-World Data•A survey of Leeds residents was undertaken in May–June 2011in order to populate the model with empirical data.•The survey gathered information about household type andtenure, socio-economic data, geographic location, and ques-tions on who people spoke to (and therefore were connectedwith) specifically about energy-related issues.• 1068 valid responses were received.•The table below shows how empirical data from the survey hasbeen used in the model.Model Feature Parameter Data SourceNetwork structure N , G, M | W , L Survey | AssumptionIndividual connections I | L Survey | AssumptionGroup connections G | L Survey | AssumptionArchetypes Ai = (αi, βi, γi), P (Ai) SimulationThreshold θ | Pθ Survey | Assumption• Individual preferences and social network influences are impor-tant factors in the adoption of energy innovations; local author-ities have the means to potentially harness these influences totheir advantage in encouraging increased adoption.• Since expected uptake of an innovation emerges as a result ofadoption behaviour of individuals connected on a social net-work, in order for us to investigate potentially successful inter-ventions, a complex-systems perspective is needed.Systematic Investigation ofParametersIndividual simulations with the same parameters can depend sen-sitively on model details and initial conditions:0%10%20%30%40%50%60%70%80% 0  5  10  15  20  25  30  35Uptaketime (months)48.8%48%Figure 3: Examples of 100 individual runs with sameparameters but different details and seed.Need to look at ensemble averages over many realisations.Method:(1) pick a set of parameters,(2) perform 20 runs for 36 time-steps,(3) plot average uptake for that set of parameters,(4) can study sensitivity to various parameters.ResultsA selection of results is shown here.(a)Average Uptake                      α = 0                      β = 0                      γ = 0(1,0,0)(0,1,0)(0,0,1)parameters: (α, β, γ) 0  0.2  0.4  0.6  0.8  1(b)Average Uptake                      α = 0                      β = 0                      γ = 0(1,0,0)(0,1,0)(0,0,1)parameters: (α, β, γ) 0  0.2  0.4  0.6  0.8  1Figure 4: Different values of two thresholds, eachassigned to half the nodes. (a) θ1 = 0.45, θ2 = 0.25(b) θ1 = 0.9, θ2 = 0.1. The shift in the behaviourdemonstrates that the choice of thresholds iscrucial to the outcome of the simulations.(a)Average Uptake                  P(A1 ) = 0                  P(A2) = 0                  P(A3) = 0(1,0,0)(0,1,0)(0,0,1)parameters: (P(A1), P(A2), P(A3));where Aj = (αj, βj, γj);A1 = (0.25, 0.7, 0.05),A2 = (0.1,  0.8, 0.1),A3 = (0.05, 0.6, 0.35), 0  0.2  0.4  0.6  0.8  1(b)Average Uptake                  P(A1 ) = 0                  P(A2) = 0                  P(A3) = 0(1,0,0)(0,1,0)(0,0,1)parameters: (P(A1), P(A2), P(A3));where Aj = (αj, βj, γj);A1 = (0.5,  0.45, 0.05),A2 = (0.25, 0.65, 0.1),A3 = (0.1,  0.7,  0.2), 0  0.2  0.4  0.6  0.8  1(c)Average Uptake                  P(A1 ) = 0                  P(A2) = 0                  P(A3) = 0(1,0,0)(0,1,0)(0,0,1)parameters: (P(A1), P(A2), P(A3));where Aj = (αj, βj, γj);A1 = (0.5,  0.45, 0.05),A2 = (0.25, 0.65, 0.1),A3 = (0.1,  0.7,  0.2), 0  0.2  0.4  0.6  0.8  1Figure 5: The population is divided into threearchetypes Aj = (αj, βj, γj). Each point on theplot is for a different set of relative proportions ofthe population (P (A1), P (A2), P (A3)). (a) Singlethreshold θ = 0.25. (b) Thresholds are distributedwith θ = (1, 0.75, 0.45, 0.25) with proportions (0.5,0.05, 0.17, 28). (c) The θ = 1 threshold islowered to θ = 0.45. The difference between theresults is due to the different distribution ofarchetypes and thresholds.Conclusions•We have developed a model for exploring the parameter space to investigate what factors are important in thediffusion of innovations on a real-world social network.•We extended our basic dynamical network model to integrate empirical data (gathered via a city-wide survey)into the models in order to more closely represents a real social system.•We have highlighted the need for new data to understand (in a quantitative way) householder barriers anddrivers to adoption of energy-efficient innovations.•The benefits to adopting network interventions are becoming clearer and this is certainly an area where furtherresearch is warranted.References[1] Bale, C. S. E., Foxon, T. J., Hannon, M. J., and Gale, W. F. Strategic energy planning within local authoritiesin the UK: A study of the city of Leeds. Energy Policy, 48:242–251.[2] Delre, S., Jager, W., Bijmolt, T., and Janssen, M. (2010). Will it spread or not? the effects of social influencesand network topology on innovation diffusion. Journal of Product Innovation Management, 27(2):267–282.[3] McCullen, N. J., Rucklidge, A. M., Bale, C. S. E., Foxon, T. J., and Gale, W. F. (2012).Multi-parameter models of innovation diffusion on complex networks.SIAM Journal on Applied Dynamical Systems, 12(1):515–532.[4] Newman, M. E. J. (2003). Properties of highly clustered networks. Physical Review E, 68(2), 026121.[5] Valente, T. (1996). Social network thresholds in the diffusion of innovations. Social networks, 18(1):69–89.[6] Valente, T. (2012). Network interventions. Science, 337(6090):49–53.EPSRC-funded project “Energy Decision Making for Cities - Can Complexity Science Rise to the Challenge?” (Reference EP/G059780/1)

Modelling the uptake of domestic energy technologies via local networks and integrating real-world data

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Modelling the uptake of domestic energy technologies via local networks and integrating real-world data

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