2691. Assessing inhabitants’ preference towards Energy Community participation: a preference learning approach
Invited abstract in session MC-46: Energy systems decarbonisation studies, stream Energy Economics & Management.
Monday, 12:30-14:00Room: Newlyn 1.07
Authors (first author is the speaker)
| 1. | Giulio Cavana
|
| DIST, Politecnico di Torino | |
| 2. | Marta Bottero
|
| Department of Urban and Regional Studies and Planning, Politecnico di Torino | |
| 3. | Cristina Becchio
|
| Department of Energy, Politecnico di Torino | |
| 4. | Alexis Tsoukias
|
| CNRS - LAMSADE | |
| 5. | Giovanna Fancello
|
| INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Sorbonne Université |
Abstract
Energy communities are legal entities that aim to increase the production and self-consumption of renewable energy, with the potential to promote the decarbonisation of the building stock and tackle energy poverty. Despite their benefits, there is a lack of bottom-up initiative and various trade-offs in their formation.
This study investigates the preferences of individuals in engaging with Energy communities and the likelihood of alternative configurations to be formed in a case study.
To assess this, a questionnaire was distributed asking respondents about their willingness to participate in alternative Energy Communities and to indicate an order of preference among options. A combination of regression analysis, UTA methods and classification algorithms was used to extrapolate characteristic sets of value functions associated with types of respondents defined by vectors of socio-economic attributes. Finally, a synthetic population generator was used to spatialise the results.
144 types of individuals were identified and their decision models were spatialised in maps of potential participation across the city. Furthermore, each type was assigned a set of value functions and the most preferred alternative in each census tract was evaluated. Such results could help policy makers to identify areas of sub-optimal diffusion of Energy communities and identify corrective actions to the current policy framework to support residents' participation in the energy transition.
Keywords
- Decision Analysis
- Energy Policy and Planning
- Machine Learning
Status: accepted
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