Modeling photovoltaic diffusion: an analysis of geospatial datasets
This study combines address-level residential photovoltaic (PV) adoption trends in California with several types of geospatial information—population demographics, housing characteristics, foreclosure rates, solar irradiance, vehicle ownership preferences, and others—to identify which subsets of geo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2014-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/9/7/074009 |
| _version_ | 1797747914767859712 |
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| author | Carolyn Davidson Easan Drury Anthony Lopez Ryan Elmore Robert Margolis |
| author_facet | Carolyn Davidson Easan Drury Anthony Lopez Ryan Elmore Robert Margolis |
| author_sort | Carolyn Davidson |
| collection | DOAJ |
| description | This study combines address-level residential photovoltaic (PV) adoption trends in California with several types of geospatial information—population demographics, housing characteristics, foreclosure rates, solar irradiance, vehicle ownership preferences, and others—to identify which subsets of geospatial information are the best predictors of historical PV adoption. Number of rooms, heating source and house age were key variables that had not been previously explored in the literature, but are consistent with the expected profile of a PV adopter. The strong relationship provided by foreclosure indicators and mortgage status have less of an intuitive connection to PV adoption, but may be highly correlated with characteristics inherent in PV adopters. Next, we explore how these predictive factors and model performance varies between different Investor Owned Utility (IOU) regions in California, and at different spatial scales. Results suggest that models trained with small subsets of geospatial information (five to eight variables) may provide similar explanatory power as models using hundreds of geospatial variables. Further, the predictive performance of models generally decreases at higher resolution, i.e., below ZIP code level since several geospatial variables with coarse native resolution become less useful for representing high resolution variations in PV adoption trends. However, for California we find that model performance improves if parameters are trained at the regional IOU level rather than the state-wide level. We also find that models trained within one IOU region are generally representative for other IOU regions in CA, suggesting that a model trained with data from one state may be applicable in another state. |
| first_indexed | 2024-03-12T15:57:27Z |
| format | Article |
| id | doaj.art-32af5ac7ff25414692a6ed1ab8b5b80e |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:57:27Z |
| publishDate | 2014-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-32af5ac7ff25414692a6ed1ab8b5b80e2023-08-09T14:46:41ZengIOP PublishingEnvironmental Research Letters1748-93262014-01-019707400910.1088/1748-9326/9/7/074009Modeling photovoltaic diffusion: an analysis of geospatial datasetsCarolyn Davidson0Easan Drury1Anthony Lopez2Ryan Elmore3Robert Margolis4National Renewable Energy Laboratory, 15013 Denver West Parkway Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver West Parkway Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver West Parkway Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver West Parkway Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver West Parkway Golden, CO 80401, USAThis study combines address-level residential photovoltaic (PV) adoption trends in California with several types of geospatial information—population demographics, housing characteristics, foreclosure rates, solar irradiance, vehicle ownership preferences, and others—to identify which subsets of geospatial information are the best predictors of historical PV adoption. Number of rooms, heating source and house age were key variables that had not been previously explored in the literature, but are consistent with the expected profile of a PV adopter. The strong relationship provided by foreclosure indicators and mortgage status have less of an intuitive connection to PV adoption, but may be highly correlated with characteristics inherent in PV adopters. Next, we explore how these predictive factors and model performance varies between different Investor Owned Utility (IOU) regions in California, and at different spatial scales. Results suggest that models trained with small subsets of geospatial information (five to eight variables) may provide similar explanatory power as models using hundreds of geospatial variables. Further, the predictive performance of models generally decreases at higher resolution, i.e., below ZIP code level since several geospatial variables with coarse native resolution become less useful for representing high resolution variations in PV adoption trends. However, for California we find that model performance improves if parameters are trained at the regional IOU level rather than the state-wide level. We also find that models trained within one IOU region are generally representative for other IOU regions in CA, suggesting that a model trained with data from one state may be applicable in another state.https://doi.org/10.1088/1748-9326/9/7/074009solar photovoltaictechnology diffusiondistributed generation |
| spellingShingle | Carolyn Davidson Easan Drury Anthony Lopez Ryan Elmore Robert Margolis Modeling photovoltaic diffusion: an analysis of geospatial datasets Environmental Research Letters solar photovoltaic technology diffusion distributed generation |
| title | Modeling photovoltaic diffusion: an analysis of geospatial datasets |
| title_full | Modeling photovoltaic diffusion: an analysis of geospatial datasets |
| title_fullStr | Modeling photovoltaic diffusion: an analysis of geospatial datasets |
| title_full_unstemmed | Modeling photovoltaic diffusion: an analysis of geospatial datasets |
| title_short | Modeling photovoltaic diffusion: an analysis of geospatial datasets |
| title_sort | modeling photovoltaic diffusion an analysis of geospatial datasets |
| topic | solar photovoltaic technology diffusion distributed generation |
| url | https://doi.org/10.1088/1748-9326/9/7/074009 |
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