Estimating Rooftop Suitability for PV: A Review of Methods, Patents, and Validation Techniques

Melius, Jennifer; Margolis, Robert; Ong, Sean

doi:10.2172/1117057

Cited by 86 publications

(78 citation statements)

References 18 publications

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“…Various approaches are used for estimating rooftop PV potential, as summarized in Melius et al (2013) and Freitas et al (2015). These methods have been used to estimate the suitability of rooftops and PV technical potential for numerous cities and countries, such as San Diego (Anders and Bialek 2006), Philadelphia (Boz et al 2015), Seoul (Hong et al 2017), Chile (Campos et al 2016), Ontario (Wiginton et al 2010), Lisbon (Brito et al 2012), Brazil (Miranda et al 2015), Minneapolis (Risse et al 2014), Austin (Wiese et al 2010), and Canada (Pelland and Poissant 2006).…”

Section: Introductionmentioning

confidence: 99%

Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling

Gagnon

Margolis

Melius

et al. 2018

Environ. Res. Lett.

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Section: Introductionmentioning

confidence: 99%

Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling

Gagnon

Margolis

Melius

et al. 2018

Environ. Res. Lett.

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“…To produce this estimate, the fraction of rooftop area suitable for PV must be estimated. Melius et al (2013) identify three main approaches to estimating rooftop suitability: constant-value methods, manual selection, and methods based on geographic information systems (GIS). Constant-value methods assume a certain percentage of building rooftop area is suitable for hosting PV and then applies these percentages to the total building stock to estimate the area available for PV; most previous estimates of national PV technical potential have relied on such methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…GIS-based methods provide more precision than constant-value methods while handling much larger data sets than manual selection. Melius et al (2013) give many examples of GIS-based applications (e.g. Hofierka and Kanuk 2009, Compagnon 2004, Santos et al 2011, and they develop and validate a suitability-estimation method based on best practices from the literature.…”

Section: Introductionmentioning

confidence: 99%

Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities

Margolis¹,

Gagnon²,

Melius³

et al. 2017

Environ. Res. Lett.

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We estimate the technical potential of rooftop solar photovoltaics (PV) for select US cities by combining light detection and ranging (lidar) data, a validated analytical method for determining rooftop PV suitability employing geographic information systems, and modeling of PV electricity generation. We find that rooftop PV's ability to meet estimated city electricity consumption varies widely-from meeting 16% of annual consumption (in Washington, DC) to meeting 88% (in Mission Viejo, CA). Important drivers include average rooftop suitability, household footprint/ per-capita roof space, the quality of the solar resource, and the city's estimated electricity consumption. In addition to city-wide results, we also estimate the ability of aggregations of households to offset their electricity consumption with PV. In a companion article, we will use statistical modeling to extend our results and estimate national rooftop PV technical potential. In addition, our publically available data and methods may help policy makers, utilities, researchers, and others perform customized analyses to meet their specific needs.

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“…1,2,14,21,23 In Refs. 1 and 2, the eventual adoption rate was simply assumed to be 30% for residential customers.…”

Section: Forecasting Eventual Adoption Rate and Delay Effectmentioning

confidence: 99%

A model for commercial adoption of photovoltaic systems in California

Wang

Johnson

2017

Journal of Renewable and Sustainable Energy

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To promote the installations of solar photovoltaic (PV) systems efficiently, it is important to quantify the impact of government incentive programs and solar PV system life-cycle costs on customer adoption. In this paper, a model for commercial solar PV adoption is developed with explanatory variables such as government incentive programs and solar PV system installation costs. The adoption model is built on top of the Generalized Bass diffusion framework. The model is applied to forecast the commercial solar PV adoption in Southern California. Asymptotic standard errors of the parameter estimates are calculated to verify the significance of the explanatory variables. Empirical results show that decreasing solar PV system installation costs and government incentive programs are the main forces that drove the growth of commercial solar PV adoption. In the case of Southern California, we also discover that government incentive programs and PV system installation costs have a much higher impact on large commercial customers than on small commercial customers. Our Generalized Bass diffusion model of commercial solar PV adoption yields a lower root-mean-square error than the basic Bass Diffusion model. In addition, the commercial solar PV adoption model predicted that the eventual adoption rate of solar PV systems is higher for large commercial customers.

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Estimating Rooftop Suitability for PV: A Review of Methods, Patents, and Validation Techniques

Cited by 86 publications

References 18 publications

Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling

Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling

Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities

A model for commercial adoption of photovoltaic systems in California

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