2016 4th International Symposium on Computational and Business Intelligence (ISCBI) 2016
DOI: 10.1109/iscbi.2016.7743256
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A predictive model to forecast customer adoption of rooftop solar

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Cited by 3 publications
(2 citation statements)
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“…Individuals' race was relevant when studies included more than one ethnicity. The predictor was mostly used for the US [23,55,58,88,104,108,110,[113][114][115][116][117][118] and sparsely for the UK [119], Belgium [111] and Malaysia [60]. Note that the applicability and practicality of the listed social factors (Table 4) fundamentally rests on the specifications of the area under study.…”
Section: Social Predictorsmentioning
confidence: 99%
“…Individuals' race was relevant when studies included more than one ethnicity. The predictor was mostly used for the US [23,55,58,88,104,108,110,[113][114][115][116][117][118] and sparsely for the UK [119], Belgium [111] and Malaysia [60]. Note that the applicability and practicality of the listed social factors (Table 4) fundamentally rests on the specifications of the area under study.…”
Section: Social Predictorsmentioning
confidence: 99%
“…1. fixed growth factors for existing solar, storage, and controllable (or uncontrollable) loads; input as a schedule of %/year vs. time 2. pre-identified feasible sites for new capacitor banks, chosen from a list of fixed sizes 3. residential rooftop solar adoption models for existing houses (Zhang et al 2015;Sultan et al 2016), or a simpler one based on total energy use and floor area of the house 4. changing size of an existing substation or service transformer.…”
Section: Operational and Growth Modelsmentioning
confidence: 99%