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|Title: ||Using a bayesian network to evaluate the social, economic and environmental impacts of community deployed renewable energy|
|Authors: ||Leicester, Philip A.|
Goodier, Chris I.
|Keywords: ||Solar PV|
|Issue Date: ||2013|
|Citation: ||LEICESTER, P.A., GOODIER, C.I. and ROWLEY, P., 2013. Using a bayesian network to evaluate the social, economic and environmental impacts of community deployed renewable energy. IN: Scartezzini, J.L. (ed.) Proceedings of CISBAT, Clean Technology for Smart Cities and Buildings, Lausanne, 4-6 September 2013, 10 pp.|
|Abstract: ||Social, economic and environmental (SEE) impacts resulting from the adoption of solar PV
have been modelled at a community scale for the first time using a probabilistic graphical
model in the form of a Bayesian Network (BN). Model parameters required to conceptualise
this multi-disciplinary problem domain are characterised by uncertainty due to stochastic
variability, measurement and modelled data errors, or missing or incomplete information. A
BN conveniently represents the model parameters and the associations between them and
endogenises the uncertainty in probability distribution functions or mass functions.
The theory and method of construction of an object-oriented BN which encapsulates a number
of SEE parameters is described. This is used to model small urban areas as potential adopters
of solar PV technology. The BN has been populated with modelled and empirical quantitative
data from a variety of disciplines to create an inter-disciplinary knowledge representation of
the problem domain.
The model has been used to explore a number of scenarios whereby ‘observations’ are made
on one or more variables of interest thus altering their prior probability distribution. The
updated or posterior distributions of all the other variables are then recalculated using
inference algorithms. Results are presented which show the utility of this approach in
diagnostic and prognostic inference making. For example it is shown that Solar PV can have a
small but significant impact on energy poverty.
It is concluded that the adoption of a BN modelling approach that endogenises uncertainty,
and reduces investment and policy risks associated with energy technology interventions
within communities, can act as a useful due diligence and decision support tool for a number
of private, public and community sector stakeholders active in this sector, in particular key
decision and policy makers.|
|Description: ||This is a conference paper. The conference website is at: http://cisbat.epfl.ch/|
|Version: ||Accepted for publication|
|Appears in Collections:||Conference Papers (Civil and Building Engineering)|
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