M. Moglia, C. Nygaard, Stephen Glackin, S. Cook, S. Tapsuwan
{"title":"Hybrid Approach for Modelling the Uptake of Residential Solar PV Systems, with Case Study Application in Melbourne, Australia","authors":"M. Moglia, C. Nygaard, Stephen Glackin, S. Cook, S. Tapsuwan","doi":"10.18564/jasss.4921","DOIUrl":null,"url":null,"abstract":": Understanding the processes of residential solar PV uptake is critical to developing planning and policy energy transition pathways. This paper outlines a novel hybrid Agent-Based-Modelling/statistical adoption prediction framework that addresses several drawbacks in current modelling approaches. Specifically, we extend the capabilities of similar previous models and incorporate empirical data, behavioural theory, social networks and explicitly considers the spatial context. We provide empirical data affecting households’ propensity to adopt, including perceptions of solar PV systems, the role of tenure and urban location. We demonstrate the approach in the context of Melbourne metropolitan region, Australia; and draw on housing approval data to demonstrate the role of housing construction in accelerating adoption. Finally, we explore the approach’s validity against real-world data with promising results that also indicate key areas for further research and improvement.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"331 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Artif. Soc. Soc. Simul.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18564/jasss.4921","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
: Understanding the processes of residential solar PV uptake is critical to developing planning and policy energy transition pathways. This paper outlines a novel hybrid Agent-Based-Modelling/statistical adoption prediction framework that addresses several drawbacks in current modelling approaches. Specifically, we extend the capabilities of similar previous models and incorporate empirical data, behavioural theory, social networks and explicitly considers the spatial context. We provide empirical data affecting households’ propensity to adopt, including perceptions of solar PV systems, the role of tenure and urban location. We demonstrate the approach in the context of Melbourne metropolitan region, Australia; and draw on housing approval data to demonstrate the role of housing construction in accelerating adoption. Finally, we explore the approach’s validity against real-world data with promising results that also indicate key areas for further research and improvement.