1. Mathematical modelling for the impact of social network on energy savings
Jeff Jiangfeng Zhang
PhD, Senior Lecturer, CEng
Department of Electronic and Electrical Engineering,
University of Strathclyde
Tel:

2. Agenda
Motivation
Modelling
Case study

3. I. Motivation
Important impact of human behaviour to the progress of energy efficiency (EE) technology mass rollout (CFL/LED lighting, heat pump, solar water heater, electric vehicles, smart meters,…)
Forecast the progress of EE programme
Control the progress of EE programme:
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other customer engagement: communications 3

4. II Modelling
Social network (small world)

5. Information transfer along a social network
Social impact means the changes in
physiological states and subjective feelings,
motives and emotions,
cognitions and beliefs,
values and behaviour,
that occur in a human as a result of the actions of others.

6. Energy saving modelling
Expected energy savings = Direct savings + Indirect savings
Direct savings: savings from installed EE technologies
Indirect savings: mathematical expectations of savings through information propagation from people with installed EE technologies
Calculations of information propagation:
Identify initial information source nodes (people): people installed EE technologies
directly connected nodes: information dissipation (represented by a probability) depends on strength of connections between the two nodes/people
indirectly connected nodes: product of the information dissipation probabilities of directly connected paths
needs the help of concept of entropy to quantify information propagation

7. III. Case study: a network with 56 people
node 3 has highest number of connections (17)
free solar water geysers to be dispatched

8. With 3 source nodes: Node 3 does not appear in the best 10 combinations!

9. Further studies References:
Not limited to energy savings calculation: Smart meters
Advertisement/Customer engagement
Global models to be developed: Do not rely on particular network connections
References:
UE Ekpenyong, J Zhang and X Xia, Mathematical modelling for the social impact to energy efficiency savings, Energy and Buildings, vol. 84, 2014, pp
UE Ekpenyong, J Zhang and X Xia, How information propagation in social networks can improve energy savings based on time of use tariff, Sustainable CitiesandSociety, vol. 19, 2015, pp. 26–33