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Title: Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections

Abstract

A computational method to automatically detect solar panels on rooftops to aid policy and financial assessment of solar distributed generation. The code automatically classifies parcels containing solar panels in the U.S. as residential or commercial. The code allows the user to specify an input dataset containing parcels and detected solar panels, and then uses information about the parcels and solar panels to automatically classify the rooftops as residential or commercial using machine learning techniques. The zip file containing the code includes sample input and output datasets for the Boston and DC areas.

Authors:
; ;  [1]
  1. Oak Ridge Associated Universities (ORAU)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1430697
Report Number(s):
Auto Res/Comm Classification of Parcels; 005648IBMPC00
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Software
Software Revision:
00
Software Package Number:
005648
Software CPU:
IBMPC
Open Source:
Yes
Source Code Available:
Yes
Country of Publication:
United States

Citation Formats

Morton, April M, Omitaomu, Olufemi A, and Kotikot, Susan. Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections. Computer software. https://www.osti.gov//servlets/purl/1430697. Vers. 00. USDOE. 25 Mar. 2018. Web.
Morton, April M, Omitaomu, Olufemi A, & Kotikot, Susan. (2018, March 25). Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1430697.
Morton, April M, Omitaomu, Olufemi A, and Kotikot, Susan. Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections. Computer software. Version 00. March 25, 2018. https://www.osti.gov//servlets/purl/1430697.
@misc{osti_1430697,
title = {Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections, Version 00},
author = {Morton, April M and Omitaomu, Olufemi A and Kotikot, Susan},
abstractNote = {A computational method to automatically detect solar panels on rooftops to aid policy and financial assessment of solar distributed generation. The code automatically classifies parcels containing solar panels in the U.S. as residential or commercial. The code allows the user to specify an input dataset containing parcels and detected solar panels, and then uses information about the parcels and solar panels to automatically classify the rooftops as residential or commercial using machine learning techniques. The zip file containing the code includes sample input and output datasets for the Boston and DC areas.},
url = {https://www.osti.gov//servlets/purl/1430697},
doi = {},
year = {2018},
month = {3},
note =
}