Merging Observed and Self-Reported Behaviour in Agent-Based Simulation: A Case Study on Photovoltaic Adoption
Designing and evaluating energy policies is a difficult challenge because the energy sector is a complex system that cannot be adequately understood without using models merging economic, social and individual perspectives. Appropriate models allow policy makers to assess the impact of policy measur...
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doaj-e48f3c989c5547e8be885574b3fbf76a2020-11-25T01:36:36ZengMDPI AGApplied Sciences2076-34172019-05-01910209810.3390/app9102098app9102098Merging Observed and Self-Reported Behaviour in Agent-Based Simulation: A Case Study on Photovoltaic AdoptionAndrea Borghesi0Michela Milano1DISI, University of Bologna, 40136 Bologna, ItalyDISI, University of Bologna, 40136 Bologna, ItalyDesigning and evaluating energy policies is a difficult challenge because the energy sector is a complex system that cannot be adequately understood without using models merging economic, social and individual perspectives. Appropriate models allow policy makers to assess the impact of policy measures, satisfy strategic objectives and develop sustainable policies. Often the implementation of a policy cannot be directly enforced by governments, but falls back to many stakeholders, such as private citizens and enterprises. We propose to integrate two basic cornerstones to devise realistic models: the self-reported behaviour, derived from surveys, and the observed behaviour, from historical data. The self-reported behaviour enables the identification of drivers and barriers pushing or limiting people in their decision making process, while the observed behaviour is used to tune these drivers/barriers in a model. We test our methodology on a case-study: the adoption of photovoltaic panels among private citizens in the Emilia−Romagna region, Italy. We propose an agent-based model devised using self-reported data and then empirically tuned using historical data. The results reveal that our model can predict with great accuracy the photovoltaic (PV) adoption rate and thus support the energy policy-making process.https://www.mdpi.com/2076-3417/9/10/2098simulation modelmulti-agent systemsphotovoltaic energyparameter fine-tuningself-reported behaviourpredictive model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Andrea Borghesi Michela Milano |
spellingShingle |
Andrea Borghesi Michela Milano Merging Observed and Self-Reported Behaviour in Agent-Based Simulation: A Case Study on Photovoltaic Adoption Applied Sciences simulation model multi-agent systems photovoltaic energy parameter fine-tuning self-reported behaviour predictive model |
author_facet |
Andrea Borghesi Michela Milano |
author_sort |
Andrea Borghesi |
title |
Merging Observed and Self-Reported Behaviour in Agent-Based Simulation: A Case Study on Photovoltaic Adoption |
title_short |
Merging Observed and Self-Reported Behaviour in Agent-Based Simulation: A Case Study on Photovoltaic Adoption |
title_full |
Merging Observed and Self-Reported Behaviour in Agent-Based Simulation: A Case Study on Photovoltaic Adoption |
title_fullStr |
Merging Observed and Self-Reported Behaviour in Agent-Based Simulation: A Case Study on Photovoltaic Adoption |
title_full_unstemmed |
Merging Observed and Self-Reported Behaviour in Agent-Based Simulation: A Case Study on Photovoltaic Adoption |
title_sort |
merging observed and self-reported behaviour in agent-based simulation: a case study on photovoltaic adoption |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-05-01 |
description |
Designing and evaluating energy policies is a difficult challenge because the energy sector is a complex system that cannot be adequately understood without using models merging economic, social and individual perspectives. Appropriate models allow policy makers to assess the impact of policy measures, satisfy strategic objectives and develop sustainable policies. Often the implementation of a policy cannot be directly enforced by governments, but falls back to many stakeholders, such as private citizens and enterprises. We propose to integrate two basic cornerstones to devise realistic models: the self-reported behaviour, derived from surveys, and the observed behaviour, from historical data. The self-reported behaviour enables the identification of drivers and barriers pushing or limiting people in their decision making process, while the observed behaviour is used to tune these drivers/barriers in a model. We test our methodology on a case-study: the adoption of photovoltaic panels among private citizens in the Emilia−Romagna region, Italy. We propose an agent-based model devised using self-reported data and then empirically tuned using historical data. The results reveal that our model can predict with great accuracy the photovoltaic (PV) adoption rate and thus support the energy policy-making process. |
topic |
simulation model multi-agent systems photovoltaic energy parameter fine-tuning self-reported behaviour predictive model |
url |
https://www.mdpi.com/2076-3417/9/10/2098 |
work_keys_str_mv |
AT andreaborghesi mergingobservedandselfreportedbehaviourinagentbasedsimulationacasestudyonphotovoltaicadoption AT michelamilano mergingobservedandselfreportedbehaviourinagentbasedsimulationacasestudyonphotovoltaicadoption |
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