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Triple bottom line impacts of community energy initiatives Crossing disciplinary boundaries with Bayesian Networks Philip A Leicester BSc DIS School of Civil and Building Engineering Centre for Renewable Energy and Systems Technology Loughborough University. Presentation Objectives.

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Presentation Objectives

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  1. Triple bottom line impacts of community energy initiatives Crossing disciplinary boundaries with Bayesian Networks Philip A Leicester BSc DISSchool of Civil and Building Engineering Centre for Renewable Energy and Systems TechnologyLoughborough University

  2. Presentation Objectives • Introduce the research project. • Describe the utility of Bayesian Networks as a methodology for this problem domain. • Demonstrate the adoption of Solar PV and impact on fuel poverty as a case study. • Highlight the future potential of the methodology • Solicit feedback from domain experts!

  3. Research Aim: To develop and validate a methodology for the evaluation of the triple bottom lineimpacts of community energy initiatives.

  4. Problems are wicked • Parameters are characterised by uncertainty • All models are wrong • Quantsandquals

  5. While fuel prices and low incomes are constituent factors, the real cause of fuel poverty is the energy inefficiency of the home . B. Boardman 2012

  6. ....I find myself referring to reports published by CSE going back several years which presented findings that are extremely pertinent to the debates we are having now. So one, some or all of the following must apply: CSE is failing to communicate the things we’ve discovered; ‘the system’ (DECC, Ofgem, BIS, DoH and their former incarnations) isn’t listening to us; ‘the system’ is unable to assimilate evidence and analysis Ian Preston, “Fuel Poverty – reasons for being Seriously unimpressed.” CSE 14 November 2012.

  7. Credit to L. Jouffe/Bayesia

  8. Judae Pearl 1936 Thomas Bayes 1701-1761

  9. PAY STATEMENT With Grateful thanks to Ben Anderson, CRESEI, Essex University B. Anderson, “ESTIMATING SMALL AREA INCOME DEPRIVATION : AN ITERATIVE PROPORTIONAL FITTING APPROACH.” 2011.

  10. Can solar PV help alleviate fuel poverty? Walker, G., 2008. Decentralised systems and fuel poverty: Are there any links or risks? Energy Policy 36, 4514-4517.

  11. YES 21.6% YES 17.5%

  12. Conclusion • Bayesian Networks can propagate probabilistic causal relations and facilitate prognostic and diagnostic inference. • Can combine expert opinion (subjective probability) with quantitative (frequentist probability) from multiple knowledge domains. • Explicate a complex problem with intuitive causal map and systems thinking • Facilitate decision making under uncertainty • But... • Require rich data and conditional probabilities . . . we need to share data!

  13. Thanks for listening END

  14. Questions / Feedback More information Contact p.a.leicester@lboro.ac.uk

  15. Leicester et al 2011

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